A Reference Ontology for Putting Together Pregnancy and Mental Health

doi:10.21203/rs.3.rs-4541012/v1

Download PDF

Research Article

A Reference Ontology for Putting Together Pregnancy and Mental Health

https://doi.org/10.21203/rs.3.rs-4541012/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background: Pregnancy is a period characterized by mystique and societal expectations surrounding women. However, it can also be a challenging time for women. The mental well-being of a pregnant woman is influenced, in part, by her obstetric history and the psychosocial context. Detecting stress, anxiety, and depression in pregnant women is crucial for reducing health problems, particularly mental health issues.

Results: A project named Digital Pregnancy - Grávida Digital in Portuguese - is currently in development to address these aspects within the context of healthcare. To achieve this goal, a reliable methodology is necessary, and a conceptual model called the Ontology of Pregnancy and Mental Health, known in Portuguese as Ontologia da Gravidez e a Saúde Mental (OntoGSM), has been proposed. OntoGSM aims to provide semantic structuring of data generated throughout the project. It was developed based on the Unified Foundational Ontology (UFO) and followed the Systematic Approach for Building Ontologies (SABiO) as the development process. OntoGSM is the outcome of collaborative efforts from a diverse team of specialists in women's health, mental health, and computer science.

Conclusions: The OntoGSM´s objective is to create a tool that accommodates a broad spectrum of stakeholders, enabling a comprehensive understanding and support for women throughout their pregnancy journey within the healthcare domain. Once this integrated domain is accurately represented, the subsequent step involves constructing a tool that utilizes this representation.

Prenatal Care

Psychiatry

UFO

SABiO

Competency Question

Modeling

Knowledge Acquisition

Quality Control

Domain Specialist

Ontologist

Pregnancy and motherhood represent lifelong phases during which women are subject to numerous assumptions and societal expectations. Initially, there exists the myth that pregnancy signifies the zenith of a woman's existence, suggesting that together with the fetus, she attains a state of perfect unity and enduring happiness. However, this perception is far removed from reality. In fact, pregnancy is a period in a woman's life characterized by uncertainties regarding the essential support required and her own ability to navigate the challenges of motherhood [1]. There is a critical need for reliable support mechanisms and resources during this crucial period.

Indeed, pregnancy can pose significant challenges for women. According to the World Health Organization (WHO), depression emerges as one of the most prevalent disorders during this period, affecting 1 in 10 patients in developed countries and escalating to 1 in 5 patients in developing countries [2]. Despite the clinical signs and symptoms resembling those of other phases in a woman's life, the majority of depression cases during pregnancy go unreported, uninvestigated, and untreated. This lack of reporting and investigation may be attributed, in part, to cultural assumptions surrounding "Pregnancy Fullness" potentially contributing to diagnostic failures (Pan et al., 2019; Pearlstein, 2015). Addressing these cultural perspectives is crucial for enhancing the recognition and appropriate management of depression during pregnancy.

Pregnant women are at increased risk of developing mental health problems such as depression, anxiety, and self-harm [5], influenced by factors such as their obstetric history and psychosocial context. More recently during COVID-19 pandemic, women in the perinatal period showed an historical higher prevalence of mental disorders, including depression (24.9%), anxiety (32.8%), stress (29.44%), Post Traumatic Stress Disorder (PTSD) (27.93%), underscoring their vulnerability [6]. There are other situations, for example, the risk of depression is higher in multiparous pregnant women [7], while anxious symptoms are more common among primiparous women [8]. Socioeconomic hardships and relationship difficulties with partners are also linked to psychiatric manifestations during pregnancy and the postpartum period [9].

Neglecting psychiatric symptoms during pregnancy leads to reduced access to prenatal care and an increased risk of perinatal complications, including preterm delivery, fetal distress, and growth restriction [10] [11], risk of impaired interaction between mother and infant, increased risk-taking behaviors such as smoking and the use of other substances [5]. The pooled estimates from the meta-analysis reveal a statistically significant positive association between low social support and prenatal depression (AOR: 1.18 (95% CI: 1.01, 1.41)) and prenatal anxiety (AOR: 1.97 (95% CI: 1.34, 2.92)) leading to consider the development of targeted social support programs with a view to helping reduce mental health problems amongst pregnant women [5].

Another concern among health care professionals is the common sense that psychiatric medication should be entirely avoided during pregnancy, prompting half of the women already using such drugs could discontinue treatment, thus increasing the prevalence of mental disorders during pregnancy [12]. Therefore, it is crucial to consider the patient's psychiatric history as it can serve as a predictor of psychiatric symptoms during pregnancy ([12]; [13]). Raising awareness about the safety and benefits of appropriate psychiatric treatment, particularly for pregnant women experiencing moderate to severe mental disorders, is imperative.

In the realm of mental health in women, particularly concerning the reproductive cycle, psychiatric disorders are commonly more acknowledged during the puerperium. However, their incidence rate can be at least twice as high during pregnancy than after childbirth [14]. Additionally, societal ideals surrounding pregnancy, or conversely, social judgments regarding unplanned pregnancies, serve as barriers to prenatal care, hindering the investigation of the patient's mental health [15]. Thus, it appears that research in this domain has often focused disproportionately on the puerperium, neglecting the critical phase of pregnancy. This stands in contrast to studies indicating that investments in maternal and child health promotion programs can yield a substantial financial return, up to 1:20 over a 10-year period [16].

To facilitate early intervention necessitates the establishment of a robust methodology for identifying stress, anxiety, and depression in pregnant women. The primary objective is to mitigate health issues, particularly those related to mental health. The authors of this article are actively engaged in a project titled Digital Pregnancy (Grávida Digital in Portuguese), which aims to address this gap in the care provided to pregnant women. The intersection of mental health and women's health poses the challenge of integrating a substantial amount of information, requiring technological support. This entails the development of a digital platform with multifaceted functionalities, encompassing the collection of data on pregnant women, obstetric history, mental health status both before and during pregnancy, and during the postpartum period.

To enable early intervention, it is essential to establish a reliable methodology for detecting stress, anxiety, and depression in pregnant women, aiming to reduce health problems, particularly mental health issues. The authors of this article are involved in a project known as "Digital Pregnancy" ("Grávida Digital" in Portuguese), which seeks to address this gap in the care of pregnant women. Combining the fields of mental health and women's health presents the challenge of integrating a vast amount of information that requires technological support. This involves creating a digital platform with multiple functions, including the collection of data (the dataset) on pregnant women, obstetric history, mental health status before and during pregnancy, and the postpartum period.

To establish a semantically structured dataset, the construction of an ontology-driven conceptual model (or reference ontology) can be applied. This approach offers several advantages, including:

1. Enhancement of understanding within the problem domain.

2. Establishment of a common vocabulary among stakeholders interested in a specific domain or information system, be it human or artificial.

3. Well-founded definition of concepts, properties, relationships between concepts, and rules within a given domain, setting a terminological standard.

4. Improved communication among agents, whether human or artificial.

5. Reuse of knowledge from existing models.

6. Utilization of a conceptual artifact facilitating conceptual mapping for various implementational artifacts (databases), aligning with user requirements.

The term 'ontology' varies in definition, not only across different disciplines such as Philosophy, Linguistics, Computer Science, and Information Science, but even within the same discipline, different definitions can be found based on the intended application. For our project, the adopted definition for ontology is: “a reference to a formal and philosophically well-founded system of categories that can be used to articulate conceptualizations and models in specific domains of knowledge” [17].

The association between ontologies and health has been firmly established and has been referenced in foundational works in the field, as seen in traditional literature such as [18], and more contemporary contributions like [19]. Notably, conferences dedicated exclusively to this intersection exist, such as the International Conference on Biomedical Ontologies (ICBO), which hosted its 14th edition in 2023 [20]. The field of Psychiatry itself has seen the application of ontologies, including OntoPsic, an ontology within the Psychiatry domain utilized in the context of Telehealth [21], and the DSM-IV-based ontology, designed for diagnostic support in Psychiatry [22].

Hence, the primary objective of this article is to introduce OntoGSM, the Ontology of Pregnancy and Mental Health (Ontologia da Gravidez e a Saúde Mental, in Portuguese, the official registered name). This ontology-driven conceptual model is designed to achieve a coherent and shared conceptualization of the domain of mental disorders in pregnant women. OntoGSM was meticulously crafted based on a foundational ontology, namely the Unified Foundational Ontology (UFO) [23], employing Ontology Engineering techniques, particularly the Systematic Approach for Building Ontologies (SABiO) process [24]. To exemplify the practical applications of OntoGSM, beyond presenting fragments of the ontology itself, this article explores some of the competency questions adopted during its development. It elucidates how these questions can be addressed through the reasoning facilitated by the ontology. The intention is to enable both technical users and non-technical users (potential users in the healthcare field) to understand how one can reason from the concepts of OntoGSM and, from there, formulate their own competency questions, leveraging the database for various data analyses.

Related Works

OntoGSM serves as a reference ontology, and in its construction other ontological models were thoroughly analyzed and reused to enhance the quality of the ontology. The adoption of the reusability characteristic is highly recommended in the development of data models. This approach aims to expedite the process, ensure high quality, and augment the potential for integration. Such a recommendation is in line with principles advocated by both academia and industry, exemplified by the FAIR principles. Notably, the "R" in FAIR signifies the importance of reusability [25].

As outlined in the Methods section, OntoGSM extensively leverages existing ontologies, including but not limited to the G2IS ontology, OntoHI, OntoGender, OntoSMT, and OntoSMIA. These reused ontologies share a common foundation, namely the Unified Foundational Ontology (UFO). Additionally, it is pertinent to note that none of these ontologies represents the same domain; rather, they collectively cover interconnected domains. Among them, the GSIS ontology, focused on maternal and child mortality, and OntoSMT bear the closest resemblance to OntoGSM. The similarity arises from their shared application of psychological scales to generate evidence for certain mental disorders.

A concise and informal exploration of the literature revealed that certain works have already delved into questions regarding the health of pregnant women, structuring them through ontologies. However, it is noteworthy that these works may differ in focus compared to the one addressed in our project, and they may not be based on the Unified Foundational Ontology (UFO). Notable examples include:

1. OntoNeo: An ontology focusing on Obstetric and Neonatal aspects, aiming to provide a consensus representation of concepts related to mother and child to enhance Electronic Health Data Records (EHR) interoperability [26].

2. Ectopic Pregnancy Ontology: Employed as a knowledge base for an intelligent ultrasound imaging system supporting obstetric and gynecological activities, with a specific emphasis on ectopic pregnancy follow-up [27].

3. Ontology for the Analysis of Issues Related to the Health of Pregnant Women: An ontology proposed to support social media searches, aiming to identify the most commonly searched terms related to pregnancy issues [28].

Systematic Approach for Building Ontologies (SABiO)

An ontology is a product - something that can be provided to interested parties to fulfill a desire or need, or to be utilized as an artifact within a computational tool. Like any product, it necessitates a development process. Ontology Engineering is the dedicated science that studies methods, procedures, and technologies applicable to the development and utilization of ontologies [29].

For the development of the ontology-driven conceptual model (reference ontology, or simply, ontology) described herein – OntoGSM - the Systematic Approach for Building Ontologies (SABiO) method [24] was chosen. This selection considers the development process leading to the generation of the reference ontology and the essential supporting processes that complement these tasks, as depicted in Fig. 1. SABiO recommends the application of a foundational ontology to inform modeling decisions and assume ontological commitment. In the case of OntoGSM, the Unified Foundational Ontology (UFO) was adopted [23]. In future subsequent stages, the reference ontology is intended to be mapped to a computational data model, serving as input and a guide for data analysis algorithms or automated information systems, among other potential applications.

SABiO is categorized into two groups of activities – Development and Support. In the context of support, the following activities were of particular relevance to our project:

• Reuse - Throughout the development process, numerous opportunities arise for the reuse of established conceptualizations within the domain of interest. The reuse of established ontologies not only enhances productivity by eliminating the need for recreation but also contributes to the quality control of the ontology itself. OntoGSM engaged in partial reuse of the following ontologies:

(1) G2IS ontology (Intelligent Governance of Health Information Ontology – Governança Inteligente de Informação em Saúde, in Portuguese), colloquially referred to as the Stork Ontology (Ontologia Cegonha, in Portuguese). This ontology was part of a project developed in collaboration with the Federal State of Ceará (Brazil), addressing maternal and child mortality [30].

(2) OntoHI, a reference ontology for the HIV/AIDS domain [31]

(3) OntoGender, a reference ontology for the field of gender and sexuality studies. It focuses on the social perspective and comprehends interpersonal relationships based on an individual's gender [32].

(4) Under development ontologies, from ongoing innovation projects at the Institute of Psychiatry at USP / Brazil (in the process of being published in paralell), namely:

a. OntoSMT, the Ontology of Mental Health in the Work Environment – Ontologia da Saúde Mental no Trabalho, in Portuguese.

b. OntoSMIA, the Ontology of Mental Health in Childhood and Adolescence – Ontologia da Saúde Mental da Infância & Adolescência, in Portuguese.

• Knowledge Acquisition - Knowledge acquisition primarily takes place during the initial phases of the ontology development process. Conventional methods and techniques for knowledge acquisition, such as an in-depth study of available literature, analysis of relevant documents, and consultations with domain specialists, were employed to acquire the essential knowledge required for constructing OntoGSM.

• Evaluation - Certain static evaluation activities (those not requiring software running) can be conducted throughout ontology development. These activities encompass thorough technical review, simulation (instantiation) techniques, and rigorous validation involving domain specialists, contributing to quality control. Both verification (conducted by ontologists) and validation (collaborative efforts between ontologists and domain specialists) activities were undertaken to ensure the quality of OntoGSM.

• Documentation – Documentation encompasses comprehensive details of both the developed product (OntoGSM) and the applied development process. It is an activity conducted concurrently with others. Generating adequate documentation yields benefits for ontology sharing, reuse, learning, and evolution. An ontology specification document was meticulously prepared for OntoGSM, providing a detailed description of the product and the adopted process in its generation.

Concerning the development activities, the initial task necessitates identifying the purpose of the ontology and its intended applications. Once the ontology's goal and intended applications are established, requirements must be elicited. The requirements of a reference ontology pertain to the knowledge (content of the domain) that the ontology is intended to represent and can be articulated as competency questions - questions that the ontology should be capable of answering [24] to the greatest extent possible. This involves understanding the structure of knowledge and the data that will be stored through that structure. This process is predominantly carried out through the Purpose Identification and Requirements Elicitation task, which is intertwined with Knowledge Acquisition. As knowledge is acquired and elicited, the Ontology Capture and Formalization activity commences, primarily involving the modeling of entities, properties, and relationships between entities based on the identified requirements and acquired knowledge.

Typically, ontology development is a collaborative endeavor. While it is feasible as an individual effort, the optimal scenario involves a team with diverse skills and roles throughout the process. This collaborative approach facilitates in-depth discussions, aligning with one of the primary objectives of any ontology: establishing a shared conceptualization [33]. Notably, the roles of interest in this paper include the domain specialist (a probable end user of the ontology) and the ontology engineer or ontologist, responsible for developing the reference ontology [24]. It's worth noting that these roles may be fulfilled by more than one professional, as in our project.

By fostering increased participation of domain specialists in ontology representation activities, we posit that the outcome is more profound and lucid, better reflecting reality. This approach also transforms them into advocates for the utilization of ontologies [34].

The Unified Foundational Ontology (UFO)

The Unified Foundational Ontology (UFO) was formulated by incorporating theories from various disciplines, including Formal Ontology in Philosophy, Cognitive Science, Linguistics, and Philosophical Logic. Due to its expansive scope and complexity, UFO was segmented into components, each addressing different facets of reality [35]:

• UFO-A, or the ontology of endurants, concentrates on the structural aspects of conceptual modeling, encompassing objects, whole-part relationships, intrinsic and relational properties. It encompasses concepts with temporal existence, such as people, disorders, and symptoms.

• UFO-B, or the ontology of perdurants, focuses on the procedural aspects of conceptual modeling, involving events and participation in events. It deals with concepts existing in time, exemplified by pregnancy.

• UFO-C, or the ontology of social aspects, addresses concepts with social connections, such as beliefs, desires, and intentions, including considerations of a religious nature.

OntGSM utilized these three UFO components, aligning a range of constructs defined in UFO, with particular emphasis on UFO-A.

Competency Questions (CQ)

Competency questions serve the purpose of delineating what is pertinent to the ontology, aiding in evaluation. The identification of CQs can be executed through diverse strategies and at varying levels of granularity. These questions span from straightforward inquiries (e.g., identifying the interviewer) to more intricate investigations (e.g., exploring correlations between a specific disorder and sociodemographic characteristics). Apart from directing the evaluation process, sophisticated and abstract CQs can even steer the modularization of the ontology, dictating the organization of its subsets. Furthermore, CQs assist in recognizing domain complements essential for ensuring the ontology's comprehensiveness and coherence [24].

Methods for identifying CQs encompass literature searches, document analysis, observation, and consultations with domain specialists – the specialist's role is pivotal during these phases. Their involvement not only instructs the ontologist on what to investigate but also offers a more pragmatic and complementary perspective on what the ontology genuinely needs to capture. It's not just about the question itself but also about the envisaged response. Given that knowledge representation is designed for collective utilization, it is essential to bear in mind that, beyond reflecting the viewpoint of a sole individual, it must accurately depict reality, capturing "the thing as it is" rather than how a specific individual perceives it [34].

Assuming that CQs serve two primary purposes: guiding the ontology's development and aiding in its evaluation, it can be deduced that the ontology's components (concepts, relations, properties, and axioms) are derived from the CQs. Simultaneously, these elements must be both necessary and sufficient to address the CQs. In the evaluation phase, if it becomes apparent that the ontology's elements are insufficient for responding to a specific CQ, additional ontological elements must be incorporated – emphasizing the iterative nature of ontology development, encompassing requirement elicitation, modeling, and evaluation. Additionally, new CQs may emerge, perpetuating the cycle [24].

The optimal approach to obtaining the necessary and sufficient questions that the ontology must address remains an open question. Fields such as Requirements Engineering and Ontology Engineering have significant contributions to offer in this context. The Knowledge Acquisition activity, as employed by SABiO, is closely tied to the formulation of CQs. Meanwhile, the verification activity serves as a quality control measure – assessing whether the ontology is capable of addressing the formulated questions.

Another consideration related to the formulation of competency questions is that some questions are linked to the structure of knowledge represented in the ontology (for example, in OntoGSM, when inquiring about the connection between Prenatal Care and Health Problems) - these are type questions On the other hand, certain questions can only be addressed through data (or instances), constituting instance questions, such as: how many prenatal consultations did a particular pregnant woman have?

Competency questions (CQ) serve as a crucial tool for delineating the scope of the ontology and guiding its development. By discerning the questions that the model is expected to address, whether related to its structure (types) or instances, it is imperative to establish the types of questions the ontology will be equipped to answer. Ideally, a spectrum of both high-level and low-level CQs should be formulated – ranging from straightforward inquiries like the patient's age to more expansive and interpretative questions such as identifying the most prevalent health issues during pregnancy and their impact on the mental health of pregnant women.

The OntoGSM Reference Ontology

The objective of OntoGSM is to serve as a shared conceptualization, grounded in a robust foundation, encompassing the comprehensive domain of Mental Health for Pregnant Women. This aligns with the goals and protocol established for the Grávida Digital project.

Moreover, OntoGSM strives to contribute to the standardization of concepts within this integrated domain encompassing Mental Health and Women's Health. This initiative aims to facilitate a clearer comprehension of these concepts, promote domain learning, enhance communication among stakeholders in the Grávida Digital project, and potentially engage other researchers and professionals with similar interests. Ultimately, the OntoGSM conceptual model can be effectively mapped to data models (operational/implementational models), ensuring the incorporation of a high-quality semantic structure within Intelligent Information Systems in the Healthcare sector.

To introduce the ontology in this paper, the emphasis will be placed on the competency questions deemed essential for the modeling and validation of OntoGSM. Specifically, attention will be given to the questions outlined in the Questionnaire of Socio-Demographic Data (QSDD) developed for the Grávida Digital project by the domain specialists. The paper will demonstrate how answers to these questions can be acquired through navigation within the graphical representation of the ontology, adopting a reasoning that could be directly followed for data analysis in a database originated from OntoGSM.

Some of the CQs from the QSDD, addressed in the paper, are identified in Table 1, along with the justification for their formulation.

Table 1

Some Competency Questions of QSDD Questionnaire from *Grávida Digital* Project
Competency Question	Justification for the CQ
CQ1: Were you born in the same city where you live today?	Recent migration has been associated with both risk for some psychiatric disorders and resilience for others [36]
CQ2: Do you work outside your home?	This question tries to identify issues related to financial income, women's double shift and professional stress
CQ3: Up to which grade level have you completed your education?	Education grade level reflects socioeconomic status and the degree of social insertion
CQ4: How many times have you been pregnant before the ongoing pregnancy?	Multiparity is a risk for mental disorder during pregnancy and it can be correlated with access to reproductive planning socioeconomic vulnerability [37]
CQ5: Have you ever had an abortion?	Abortion is surrounded by moral, ethical and legal controversies that are relevant for the woman’s relationship with pregnancy and mental health [38]
CQ6: If so, was it because you wanted to? (related to CQ5)
CQ7: If so, was it because someone put pressure on you to interrupt your pregnancy? (related to CQ5)
CQ8: What is the total number of pre-natal care appointments you have participated so far?	These interconnected questions aim to assess adherence to prenatal care
CQ9: How many appointments did you miss so far (related to CQ8)?
CQ10: How is your relationship with the baby´s father?	This question aims to assess the presence and relationship with the child's father [39]

From the elaboration of the CQs, together with the knowledge acquired through the knowledge acquisition activity, the modeling of the ontology was initiated. Among the complementary artifacts of the ontology, there are the graphical representation, the glossary of terms, the ontology restrictions, and the descriptive text complementary to the graphical representation. In this paper, fragments of the diagrams (graphical representation) necessary for reasoning to represent and answer the Competency Questions in Table 1 are identified. The following subsection presents the overview of OntoGSM, and in the subsection of Reasoning on the Competency Questions, both the answers to the questions and the fragments of diagrams needed to answer them are provided.

Graphical Representation of OntoGSM

Considering the size and complexity of the knowledge representation in the integrated domain of Pregnant Women's Mental Health, it was decided to modularize OntoGSM. An overview of the ontology division can be found in Fig. 2, which illustrates the developed modules. The three major modules are: Questionnaire (containing information about the scales to be applied); Person (containing information about the profile of the individuals within the domain – the pregnant woman and her family, and the healthcare professional who assists the pregnant woman – developed primarily based on the QSDD); Reuse (identifying the ontologies partially reused in OntoGSM). The other modules complement the mentioned modules. Additionally, each module is distinguished by color – this color designates each internal element of the module, allowing identification of the origin of a concept from any diagram in which it is applied.

Reasoning on the Competency Questions

This section presents the answers (obtained through reasoning facilitated by navigation within OntoGSM) to the competency questions identified in Table 1.

CQ1

Were you born in the same city where you live today?

Answer options

Yes; No - but in the same state of the federation; No - born in another state; No - born in another country.

Goal: this question is useful for identifying whether the pregnant woman has migrated between different geographic locations throughout her life, also identifying the level of migration that may have occurred. Initially focuses on comparing two locations: the place of birth and the current domicile.

Reasoning to identify the concepts/data of the answer (identified through navigation in OntoGSM, Fig. 3 and Fig. 4): Consider a certain Person (an instance of Pregnant Woman). Every Person is characterized by a nationality, which refers to a Birthplace, classified as a Place of Interest. This Place of Interest has an Address, associated with a City – thus, capturing the City which serves as the Birthplace of a Person. Complementarily, a Person is a resident who resides in a Domicile, classified as a Place of Occurrence of Housing, also a Place of Interest, and therefore associated with a City – this way, capturing the City of residence of a person. After identifying the two necessary cities, it is checked whether they are the same. The Migration event occurs when a Person undergoes a change of Domicile concerning his/her Birthplace. When a Change of Location occurs (the places of birth and residence are different), it is due to the occurrence of a Migration. If the Migration involved only a change of City (without changing the State and Country), then it is categorized as City Migration level. If there has been a change of State (but not of Country), then a State Migration level has occurred. Finally, if there was a change of Country, then there was a National Migration level.

CQ2

Do you work outside your home?

Answer options

Yes (With a formal contract, without a formal contract (self-employed)); No (She is unemployed, housewife, works home office style, or by work demand, she is away from work due to health reasons, she is a student, she is retired).

Goal

This question facilitates inferences regarding whether the occupational responsibilities of the pregnant woman may have any impact on her pregnancy. What is done through the identification of the type of worker and some basic information regarding the nature of the pregnant woman’s employment.

Reasoning to identify the concepts/data of the answer (identified through navigation in OntoGSM, Fig. 5): Each Pregnant Woman is a Person. A Person may be categorized as a Worker, with the classification further divided into an Employee or a Self-Employed Person (where the latter holds a Work Permit in the Unsigned phase). An Employee establishes an Employment Relationship with her Employer, holding a Work Permit in the Signed phase. A Worker possesses a Workplace, which is defined as a Place of Interest. This Workplace can be characterized as either Away from Home (located in an Establishment other than the Worker's Domicile) or At Home (occurring within the Worker's Domicile). A Worker undergoes phases such as Active (indicating she is currently working), Unemployed (denoting a period of not working without retirement) and Retired (indicating completion of regular working time). A Non-worker can be referred to as a Housewife, responsible for domestic chores within her residence. The work format classifies the Worker as either a Remote Worker (engaged in work with a workplace at home) or a Worker on Demand (providing occasional services). An Active Worker may temporarily be on Leave due to Health Issues when facing a Health Problem. Consequently, for a given pregnant woman, it becomes possible to discern her current work situation.

CQ3

Up to which grade level have you completed your education?

Answer options

(Considering the structure of the Brazilian educational system) 01. Illiterate / Incomplete Elementary School; 02. Complete Elementary I / Incomplete Elementary II; 03. Complete Elementary II / Incomplete High School; 04. Complete high school / incomplete higher education; 05. Complete higher education; 99. Don't know how to inform.

Goal

Socioeconomic concepts pertaining to people are frequently integral factors in studies examining the manifestation of anxiety and depression. Among these, particular attention is often directed towards an individual's educational grade level.

Reasoning to identify the concepts/data of the answer (identified through navigation in OntoGSM, Fig. 6): To determine the years of formal education a Person (a Pregnant Woman in this case) has within the OntoGSM, it has been established that Student is a role assumed by a Person due to an Enrollment relationship with an Educational Institution. An Academic Certificate serves as an artifact indicating the Grade Level of Education a Student has attained, allowing for the identification of whether an individual has completed education up to elementary school or graduated through to the postgraduate level. Additionally, an Academic Certificate specifies whether the education is Complete or Partial. In cases where a Person have initiated but not completed a certain grade level of education, a Partial Academic Certificate is assigned, detailing the number of years a person has studied at that level without completion. Therefore, when presented with a Person, it becomes feasible to determine her status as a Student (when enrolled in an Educational Institution) and subsequently verify her Academic Certificate with the highest Grade Level of Education. Furthermore, it is possible to identify the total number of years a Person has studied by summing all the years from Academic Certificates (both Complete and Partial) associated with that Person.

CQ4

How many times have you been pregnant before the ongoing pregnancy?

Answer options

a numerical value to be informed by the pregnant woman.

Goal

An information that can assist healthcare professionals in monitoring the physical and mental health of a pregnant woman is the number of prior pregnancies she has experienced. In this case, we consider the ongoing pregnancy and pregnancies preceding the current one.

Reasoning to identify the concepts/data of the answer (identified through navigation in OntoGSM, Fig. 7): Consider the concept of Person. A Woman is a subclassification of Person. Subsequently, a Woman may or may not be engaged in a Pregnancy event, during which she is referred to as a Pregnant Woman. The cardinality of the relationship between Pregnancy and Pregnant Woman is evident, signifying the number of instances in which a Woman has experienced or is experiencing Pregnancy events. This encompasses both prior pregnancies and the ongoing one. Consequently, for each woman of interest in the project, it becomes feasible to ascertain the total number of pregnancies she has undergone through the cardinality of the relationship between the concepts of Pregnancy and Pregnant Woman, thereby categorizing her as either primiparous (experiencing her first pregnancy) or multiparous (having experienced two or more pregnancies).

The next three questions are related to abortion situation, considering this as one of the possible outcomes of a pregnancy.

CQ5: Was any of the abortions induced? Answer options: Yes; No.

CQ6: If so, was it because you wanted to? Answer options: Yes; No.

CQ7: If so, was it because someone pressured you? Answer options: Yes; No.

Goal

There are certain data that are particularly sensitive to inquire about and address during pregnancy. However, situations involving trauma or having a negative impact on the pregnant woman, though challenging, may be necessary for a more thorough analysis and to provide her with more appropriate follow-up. Examples of such questions include those related to a previous abortion, such as CQ5, CQ6, and CQ7. Through OntoGSM, it is possible to determine, for instance, whether an abortion has occurred and whether it was induced or spontaneous.

Reasoning to identify the concepts/data of the answer (identified through navigation in OntoGSM, Fig. 8): Every Pregnancy is associated with an outcome referred to as Pregnancy Conclusion. The conclusion of a pregnancy can manifests as either Abortion or Childbirth. In cases where the Pregnancy concludes with an Abortion, it can be further characterized as either Induced Abortion or Spontaneous Abortion. In instances of Induced Abortion, there is typically a Decision to Abort, a choice made by the Pregnant Woman. A Third Party, a Person connected somehow to the Pregnant Woman, may or may not play a role in influencing this decision. Therefore, for a specific Pregnant Woman, it is possible to assess, for each of her Pregnancies, whether the Pregnancy Conclusion was an Abortion and, if so, whether it was Induced (addressing CQ5). In the event of an Induced Abortion, it can be determined whether this is associated with a Decision to Abort made by the Pregnant Woman (addressing CQ6). Finally, if such a decision has taken place, it can be further investigated whether the Decision to Abort was influenced by a Third Party (addressing CQ7). And this investigation can be carried out for each of the pregnancies whose outcome was an abortion.

The next two questions are related to prenatal care.

CQ8: What is the total number of medical appointments you have participated so far? Answer option: a numerical value to be informed by the pregnant woman.

CQ9: How many appointments did you miss so far? Answer option: a numerical value to be informed by the pregnant woman.

Goal

An essential set of data for assessing a woman's health during pregnancy pertains to the monitoring of the pregnancy, commonly referred to as Prenatal care. Consequently, certain questions from the Socio-Demographic Data Questionnaire (QSDD) are associated with this concept, including CQ8 and CQ9.

Reasoning to identify the concepts/data of the answer (identified through navigation in OntoGSM, Fig. 9): A Pregnancy involves a regimen of Pregnancy Monitoring, comprising several events like Medical Appointments, Examinations, and Vaccinations, expected to occur throughout the gestation period. Each specific Procedure within the monitoring has an Attendance property, with potential statuses being Present, Absent, or Late. Taking a particular Pregnant Woman and her corresponding Pregnancy, the Pregnancy Monitoring encompasses numerous associated Procedures. Consequently, one can pinpoint all Medical Appointments tied to that particular Pregnancy Monitoring. Subsequently, for each of these Procedures, the Attendance status can be determined, addressing cases of compliance with CQ8 (Present) or CQ9 (Absent), counting occurrences of each case.

Some apparently simple questions, when mapping their reasoning to an ontology, have a higher level of demand regarding this mapping, as occurred with CQ10 and OntoGSM. In this case, the question has two aspects to be worked on: identifying who the baby's biological father is and what the pregnant woman's relationship with this man is like. Thus, navigation to answer such a question needs to consider this.

CQ10

How is your relationship with this baby's father?

Answer option

married; single (we never had a stable relationship); stable civil union for at least 5 years; stable civil union for less than 5 years; divorced or separated; widow.

Goal

Knowing how the pregnant woman relates to the biological father of the baby can provide the healthcare professional with useful information on how to conduct the woman's care.

Reasoning to identify the concepts/data of the answer (identified through navigation in OntoGSM, Fig. 10, Fig. 11 and Fig. 12): Part 1 of CQ10 - For a Pregnancy to occur, a previous Fertilization event is necessary, which is characterized by the participation of the Biological Father and the Biological Mother. The role of the Pregnant Woman arises in the period of Pregnancy, when the Woman who is the Biological Mother in the Fertilization effectively becomes the Pregnant Woman of the Fetus (the baby being generated). Thus, considering the current Pregnancy (the one for which a Pregnancy Conclusion has not yet occurred) of the Woman of interest and after identifying the current Pregnancy, it is possible to locate the Fertilization event that resulted in such Pregnancy and, from there, to identify the Man who played the role of Biological Father in such event. This is the biological aspect of CQ10. Part 2 of CQ10 - Considering the affective and social aspect now and the same Woman of interest that was considered in the first part of the question. A Pregnant Woman is a Person. A Person may never have had a Partner or have had a Partner at some point in time (including more than one), and that Partner can be identified as Casual or Habitual. In the case of a Habitual partner, it will configure: (1) a Conjugal Society that may be classified as Marriage or Stable Union, and the union has an establishment date that identifies its duration. Also, a Conjugal Society can be Active or Dissolved (the dissolution options are: Divorce, Widowhood, Separation); (2) an Informal relation as Dating. Thus, it is possible to locate all Persons characterized as Partners of such Woman. Then, to check if any of these Partners is the Man identified as the Biological Father of the current Pregnancy (first part of the question). If so, check whether this Partner is characterized as a Companion (involved in a Stable Union) or Spouse (involved in a Marriage) of the Pregnant Woman, also identifying whether it is a case of Active or Dissolved Conjugal Society, and in the latter case, what was the type of dissolution. If it is a Dating case or even if it was just a Casual Partner (and so the Person is categorized as Single).

We have 2 goals to consider: the goal of OntoGSM itself and the goal of the paper derived from it, as identified in the Background section. To achieve these goals, we present excerpts from 11 diagrams of the model (out of a total of 74 structural diagrams in the current version, including diagrams for reusing the mentioned ontologies). To enable a concise understanding of the purpose of OntoGSM within a limited text space, we focused on an evaluative scale of the project – the QSDD. The 10 competency questions outlined in Table 1 further narrow down the content covered in the paper (the QSDD has more than 100 questions).

In its current version, OntoGSM is a conceptual ontology-driven model, and we believe it has the potential to achieve the expected benefits of such an artifact (such as the six benefits mentioned in the Background). The demonstration that these benefits have been realized can be objectively assessed in the future – when the Grávida Digital project is completed, with the collected and analyzed data. This does not imply that evaluation activities have not taken place so far, providing assurance that we are on the right track.

We argue that the quality of domain representation was achieved through a series of procedures we adopted, including: the use of a development process solidified in the community (SABiO), the reuse of ontological models interconnected with the domain, in addition to considering a well-established and widely disseminated foundational ontology (UFO), but mainly, due to the joint efforts of project members, both technical and domain specialists. This allowed the project team to achieve shared, realistic, and consistent knowledge – reflected in OntoGSM.

To enable a better understanding of what a conceptual model is by domain experts, some measures were adopted. They were introduced to Ontology Engineering concepts and actively participated in the artifact construction process. Regarding the artifact's use, it became clear to these professionals that the model can be employed for joint team understanding, as a teaching tool in a specific area, understanding what competency questions are, how the model functions when processed automatically using CQ, and from there, they became more aware of how to look at the model and try to extract knowledge from it – be it knowledge for human or artificial agent processing.

During the Knowledge Acquisition activity, discussions with domain experts were conducted, along with grounding in literature from the field and the project's evaluative scales. Additionally, in the Evaluation activity, discussions with experts were again held, aiming for their approval of the modeling developed based on the previous activity. At this point, two outcomes stand out: the refinement of the model and the establishment of more shared knowledge (in moments where there were disagreements among experts, and consensus needed to be reached). As an example of the first point, we have the concepts related to employment (see Fig. 5), which was not the expertise of any team member and needed to be researched, while only what was of interest to the project should be characterized. As an example of the second point, we have the definition of what would be classified as migration (see Fig. 4) – a change between the current place of residence and the place of birth (without considering possible intermediate changes between birth and the current moment).

To demonstrate the richness of competency questions and how they are reflected in the model, different types were considered. CQs like 2, 3, and 4 (see Table 1) are simpler, and the answer to them is obtained by navigating a single diagram each. CQs like 5, 6, and 7 are strongly interconnected, and the answer to them is obtained from similar navigation within the same diagram. Questions like CQs 1 and 10 are more complex, involve reasoning division, and demand navigation across different diagrams to arrive at an answer.

Another noteworthy aspect of the model was the identified reuse situations, namely: (1) OntoSMT and OntoSMIA, which are ontologies with a similar domain to OntoGSM, focusing on mental health assessment in at-risk groups, each addressing different groups; (2) G2IS – they have similar domains (pregnancy), but G2IS focuses on physical health without delving into mental health; (3) OntoHI and OntoGender – have a tangential connection with OntoGSM but were still considered with the idea of reusing modeling decisions and preparing for possible future integration.

As a limitation related to reuse, it should be noted that only models with the same foundation as OntoGSM (in this case, UFO) were reused, excluding models in similar domains but with different foundations, such as OntoNeo. This is a point to be investigated in the future.

There was also awareness, through the CQs, of model limitations and the attention needed during data collection to ensure thorough analyses. For example, consider CQ10. It is straightforward to identify who the biological mother of the fetus is (after all, it is the pregnant woman the model refers to), and if there is no data about her, the record will not proceed. However, what about identifying the biological father? If this data is not identified, a series of conclusions cannot be made. In the future, it will be inevitable to consider cases where the pregnant woman does not want (or does not know) to reveal data about the baby's father, and the data will be incomplete.

Among the lessons learned by the team, in addition to OntoGSM itself and the knowledge it contains, we believe that the most valuable was the human interaction of professionals from different areas to achieve a common goal. Also, the importance of domain experts within the ontology development process, creating an awareness of how they can use the model in different ways (in the focus of the article, using CQs to navigate the ontology and extract knowledge from it, as well as visualizing new CQs for concept or data analysis).

OntoGSM is the outcome of collaborative efforts involving a team of diverse specialists (from women's health, mental health, and computer science), aiming to create an artifact capable of accommodating a wide spectrum of stakeholders. This artifact seeks to enhance comprehension of the intricate landscape surrounding pregnancy and mental health. The subsequent phase involves utilizing OntoGSM as the foundation for a semantic database, paving the way for the future development of tools designed to aid women during this notably sensitive phase of their lives. The ontology facilitates the creation and utilization of technologies for screening and early detection of prevalent mental health conditions among pregnant women. Additionally, OntoGSM enables the integration of two closely related fields of medical knowledge—women's health/reproductive health and mental health—resulting in a semantically structured and integrated database. Investing in scalable mental health prevention solutions for the most vulnerable segments of the population is crucial for social development. OntoGSM establishes a vital conceptual framework to accomplish this objective.

Regarding the development of the ontology, a significant aspect of the process employed in OntoGSM is the collaborative effort between ontologists (technical staff) and domain specialists (potential model users). This collaboration had a direct impact on the quality of the developed model, enhancing its representativeness of reality. Team members actively engaged in various tasks, especially during requirements elicitation and validation, leading to intense and productive discussions. Notable outcomes of this collaboration include domain specialists gaining a clearer understanding of what an ontology is and its potential applications. Additionally, it contributed to a more precise conceptualization of the domain, aligning it closely with the realities it represents.

Certain aspects require attention for the future development of OntoGSM: (1) addressing incomplete data poses a challenge, particularly when individuals are unable to provide specific details such as street addresses. Strategies must be devised to facilitate concept navigation even with partial information. (2) Addressing non-normative parenthood situations, including those involving same-sex couples or polygamous arrangements, presents additional challenges. The initial version of the ontology is deemed stable, but ongoing efforts are essential to ensure its relevance to diverse real-life situations.

AOR Adjusted Odds Ratio

BR Brazil

CQ Competency Questions

DSM-IV Diagnostic and Statistical Manual of Mental Disorders, 4th revision

EHR Electronic Health Data Records

FAIR Findable, Accessible, Interoperable and Reusable

G2IS Intelligent Governance of Health Information know in Portuguese as Governança Inteligente de Informação em Saúde

HIV/AIDS Human Immunodeficiency Virus infection / Acquired Immune Deficiency Syndrome

ICBO International Conference on Biomedical Ontologies

OntoGender Reference Ontology for Gender and Sexuality Studies, known in Portuguese as Ontologia de Referência de Estudos de Gênero e Sexualidade

OntoGSM Ontology of Pregnancy and Mental Health, known in Portuguese as Ontologia da Gravidez e a Saúde Mental

OntoHI Reference Ontology for HIV Disease, known in Portuguese asOntologia de Referência da Doença HIV

OntoNeo Ontology focusing on Obstetric and Neonatal aspects

OntoPsic Ontology for Psychiatry, known in Portuguese as Ontologia para Psiquiatria

OntoSMIA Reference Ontology for Child & Adolescent Mental Health, known in Portuguese as Ontologia de Referência da Saúde Mental na Infância & Adolescência

OntoSMT Reference Ontology for Worker Mental Health, known in Portuguese as Ontologia de Referência da Saúde Mental do Trabalhador

QSDD Questionnaire of Socio-Demographic Data

SABiO Systematic Approach for Building Ontologies

UFO Unified Foundation Ontology

USP University of São Paulo, know in Portuguese as Universidade de São Paulo

WHO World Health Organization

Authors´s contributions

Maria das Graças da Silva Teixeira: Specialist in the Computer Science domain. Responsible for coordinating the development of OntoGSM. Acts as a researcher for the Grávida Digital project.

Luiz Guilherme Rodrigues Meireles: Student in the Computer Science domain. Was involved in the development of OntoGSM.

Cássio Pereira Cândido: Student in the Computer Science domain. Was involved in the development of OntoGSM.

Kelly dos Santos Prado: Specialist in the Health domain. Acted as a researcher for the Grávida Digital project, focusing on the project´s protocol.

Wilian Hiroshi Hisatugu: Specialist in the Computer Science domain. Acts as a researcher for the Grávida Digital project.

Anilton Salles Garcia: Specialist in the Computer Science domain. Acts as coordinator of the Grávida Digital project, focusing on the area of Computer Science.

Rodrigo da Silva Dias: Specialist in the Health domain. Acted as a researcher for the Grávida Digital project.

Rafael Teixeira Sousa: Specialist in the Computer Science domain. Acts as a researcher for the Grávida Digital project.

Amanda Sodré Mota: Specialist in the Health domain. Acts as a researcher for the Grávida Digital project, focusing on the project´s protocol, contributing to the methodology.

Marco Aurélio Knippel Galletta: Specialist in the Health domain. Acts as coordinator of the Grávida Digital project, focusing on the area of Obstetrics.

Hermano Tavares: Specialist in the Health domain. Acts as coordinator of the Grávida Digital project, focusing on the area of Psychiatry.

All authors drafted the manuscript and read and approved the final version.

Funding

We are grateful for the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) for their financial support through the project Instituto Nacional de Psiquiatria do Desenvolvimento (INCT), grant number 14/50917-0.

Availability of data and materials

OntoGSM is registered with the Brazilian National Institute of Industrial Property (INPI) under number BR512022003057-8. Related data, including OntoGSM documentation, can be requested directly from the authors, who will address the demand according to what is established in the Data Management Plan of the Grávida Digital project and the INPI register.

Ethics approval and consent to participate

The protocol of the Grávida Digital project was submitted to the Research Ethics Committee of the University of São Paulo Medical School and was approved by this organization. The research is registered in the Plataforma Brasil (link: https://plataformabrasil.saude.gov.br/), register CAAE 64158717.9.0000.0065.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Morrissey M V. Suffer no more in silence: challenging the myths of women’s mental health in childbearing. Int J Psychiatr Nurs Res. 2007;12 (2):1429–38.
WHO WHO. Thinking Healthy: A Manual for Psychosocial Management of Perinatal Depression (WHO generic field-trial version 1.0) [Internet]. Geneva; 2015. Available from: https://apps.who.int/iris/handle/10665/152936
Pan WL, Chang CW, Chen SM, Gau ML. Assessing the effectiveness of mindfulness-based programs on mental health during pregnancy and early motherhood - a randomized control trial. BMC Pregnancy Childbirth. 2019;19.
Pearlstein T. Depression during pregnancy. Best Pract Res Clin Obstet Gynaecol. 2015;29:754–64.
Bedaso A, Adams J, Peng W, Sibbritt D. The relationship between social support and mental health problems during pregnancy: a systematic review and meta-analysis. Reprod Health. 2021;18(162).
Delanerolle G, McCauley M, Hirsch M, Yutain Z, Cong X, Cavalini H, et al. The prevalence of mental ill-health in women during pregnancy and after childbirth during the Covid-19 pandemic: a systematic review and Meta-analysis. BMC Pregnancy Childbirth. 2023;23.
Magalhães A, Souza D, Barros W, Eufrásio L, Júnior L, Viana E. Influência da paridade no desenvolvimento da depressão em mulheres brasileiras. Res Soc Dev. 2021;10:e59910414516.
Sushila Shrestha, Pun KD. Anxiety on Primigravid Women Attending Antenatal Care: A Hospital Based Cross-sectional Study. Kathmandu Univ Med J. 2018;16 (61):23–7.
Cormick G, Puppo S, Vazquez P, Gibbons L, Rodriguez R, Bosio M, et al. Factors associated with depression during pregnancy in women from a low socioeconomic level: A hierarchical model approach. Psychiatry Res. 2021;298:113798.
Bonari L, Pinto N, Ahn E, Einarson A, Steiner M, Koren G. Perinatal risks of untreated depression during pregnancy. Can J Psychiatry. 2004;49(11):726–35.
Rahman A, Iqbal Z, Bunn J, Lovel H, Harrington R. Impact of maternal depression on infant nutritional status and illness: A cohort study. Arch Gen Psychiatry. 2004;61(9):946–52.
Gentile S. Untreated Depression During Pregnancy: Short- and Long-term Effecs in Offspring. A Systematic Review. Neuroscience. 2017;342:154–66.
Hartmann JM, Mendoza-Sassi RA, Cesar JA. Depressão entre puérperas: Prevalência e fatores associados. Cad Saude Publica. 2017;33(9):1–10.
Radoš SN, Tadinac M, Herman R. Anxiety During Pregnancy and Postpartum: Course, Predictors and Comorbidity with Postpartum Depression. Acta Clin Croat [Internet]. 2018;57(1):39–51. Available from: https://pubmed.ncbi.nlm.nih.gov/30256010/
Mcleish J, Ayers S, Mccourt C. Community-based perinatal mental health peer support: a realist review. BMC Pregnancy Childbirth. 2023;23.
Bonin EM, Stevens M, Beecham J, Byford S, Parsonage M. Costs and longer-term savings of parenting programmes for the prevention of persistent conduct disorder: A modelling study. BMC Public Health. 2011;11:803.
Guizzardi G, João Paulo Almeida, Guizzardi RS, Barcellos MP, Falbo R. Ontologias de Fundamentação, Modelagem Conceitual e Interoperabilidade Semântica. In: GUIZZARDI, G.; BAIÃO, F. A.; OLIVEIRA JPM, editor. Iberoamerican Meeting of Ontological Research [Internet]. CEUR-WS.org; 2011. Available from: http://ceur-ws.org/Vol-728/paper6.pdf
Noy NF, McGuinness DL. Ontology Development 101: A Guide to Creating Your First Ontology [Internet]. 2001. Available from: http://www-ksl.stanford.edu/people/dlm/papers/ontology-tutorial-noy-mcguinness-abstract.html
Farinelli; F, Almeida MB. Ontologias Biomédicas: Teoria e Prática. In: 18o Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS 2019). Sociedade Brasileira de Computação; 2019. p. 93–140.
th International Conference on Biomedical Ontology (ICBO 2023) [Internet]. 2023 [cited 2024 Jan 15]. Available from: https://icbo-conference.github.io/
da Costa Dias F. OntoPsic: uma ontologia para psiquiatria aplicada ao contexto da telessaúde [Internet]. Universidade Federal de Pernambuco; 2010. Available from: https://repositorio.ufpe.br/handle/123456789/2408
Silva C, Marreiros G, Silva N. Development of an Ontology for Supporting Diagnosis in Psychiatry. In: Omatu S, Bersini H, Corchado JM, Rodríguez S, Pawlewski Pawełand Bucciarelli E, editors. Distributed Computing and Artificial Intelligence, 11th International Conference. Cham: Springer International Publishing; 2014. p. 343–50.
Guizzardi G. Ontological Foundations for Structural Conceptual Models [Internet]. 1st ed. PhD thesis. The Netherlends: Centre for Telematics and Information Technology, University of Twente; 2005. 441 p. (Telematica Instituut Fundamental Research Series; vol. 015). Available from: http://doc.utwente.nl/50826
Falbo R. SABiO: Systematic approach for building ontologies. CEUR Workshop Proc [Internet]. 2014;1301. Available from: http://ceur-ws.org/Vol-1301/ontocomodise2014_2.pdf
Go FAIR Initiative [Internet]. 2021 [cited 2021 Apr 4]. Available from: https://www.go-fair.org/
Farinelli F, Almeida M, Elkin P, Smith B. OntONeo: The Obstetric and Neonatal Ontology. In 2016.
Maurice P, Dhombres F, Blondiaux E, Friszer S, Guilbaud L, Lelong N, et al. Towards ontology-based decision support systems for complex ultrasound diagnosis in obstetrics and gynecology. J Gynecol Obs Hum Reprod [Internet]. 2017;46 (5):423–9. Available from: https://pubmed.ncbi.nlm.nih.gov/28934086/
Joo Yun Lee. Ontology-Based Natural Language Processing of Social Media Data in the Assessment of Health Information Sought During Pregnancy. Stud Heal Technol Inf [Internet]. 2021;284:68–70. Available from: https://pubmed.ncbi.nlm.nih.gov/34920474/
Gómez-Pérez A, Fernández-López M, Corcho-Garcia O. Ontological Engineering with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web. Secaucus, NJ, USA: Springer-Verlag New York, Inc.; 2003.
Oliveira M, Silveira V, Vidal V, Garcia AS, Ramos R, Sales TP, et al. Relatório Técnico: Projeto GISSA - Meta Física 3 - Atividade 3.8 - Definir Modelo do Sistema Integrado Inteligente de Saúde [Internet]. 2015. Available from: https://amauroboliveira.files.wordpress.com/2015/11/2015-dez02-meta-3-ativ-8-moldelodosistemaintegradointeligc3aancia-draf-2-0.pdf
dos Reis Costa D. Aplicação da Metodologia SABiO na Construção de OntoHI. Universidade Federal do Espírito Santo; 2020.
Rodrigues LG, Oliveira AFI, Silva Teixeira M das G da. OntoGender – An Ontology in the Domain of Gender and Sexuality. In: SoLEE 2021: 2nd International Workshop on Ontology of Social, Legal and Economic Entities, held at JOWO 2021: Episode VII The Bolzano Summer of Knowledge [Internet]. Bolzano, Itália; 2021. Available from: http://ceur-ws.org/Vol-2969/paper70-SoLEE.pdf
Borst WN. Construction of Engineering Ontologies for Knowledge Sharing and Reuse [Internet]. University of Twente; 1997. Available from: https://research.utwente.nl/en/publications/construction-of-engineering-ontologies-for-knowledge-sharing-and-
Norris E, Hastings J, Marques MM, Mutlu ANF, Zink S, Michie S. Why and How to Engage Expert Stakeholders in Ontology Development: Insights From Social and Behavioural Sciences. J Biomed Semantics [Internet]. 2021;12(4). Available from: https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-021-00240-6#citeas
Guizzardi G, Wagner G, Almeida J, Guizzardi R. Towards Ontological Foundations for Conceptual Modeling: The Unified Foundational Ontology (UFO) Story. Appl Ontol. 2015;10.
Lebano A, Hamed S, Bradby H, Gil-Salmerón A, Dura Ferrandis E, Garcés-Ferrer J, et al. Migrants’ and refugees’ health status and healthcare in Europe: A scoping literature review. BMC Public Health. 2020;20.
Silva BP, Neves P, Mazzaia M, Gabrielloni M. Common mental disorders and perinatal depressive symptoms: an integrative review. Rev Bras Enferm. 2020;73 (Suppl.
Cuenca D. Pregnancy loss: Consequences for mental health. Front Glob Women’s Heal. 2023;3:1032212.
Chauhan A, Potdar J. Maternal Mental Health During Pregnancy: A Critical Review. Cureus. 2022;14.

No competing interests reported.

Download PDF

Reviewers agreed at journal
01 Aug, 2024
Reviews received at journal
16 Jul, 2024
Reviewers agreed at journal
20 Jun, 2024
Reviewers invited by journal
18 Jun, 2024
Editor assigned by journal
10 Jun, 2024
Submission checks completed at journal
10 Jun, 2024
First submitted to journal
06 Jun, 2024

You are reading this latest preprint version

A Reference Ontology for Putting Together Pregnancy and Mental Health

Status:

Version 1

Abstract

Figures

Background

Related Works

Methods

Systematic Approach for Building Ontologies (SABiO)

The Unified Foundational Ontology (UFO)

Competency Questions (CQ)

Results

The OntoGSM Reference Ontology

Graphical Representation of OntoGSM

Reasoning on the Competency Questions

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1