Design and Evaluation of a Postpartum Depression Ontology

Abstract

Objective Postpartum depression (PPD) remains an understudied research area despite its high prevalence. The goal of this study is to develop an ontology to aid in the identification of patients with PPD and to enable future analyses with electronic health record (EHR) data. Methods We used Protégé-OWL to construct a postpartum depression ontology (PDO) of relevant comorbidities, symptoms, treatments, and other items pertinent to the study and treatment of PPD. Results The PDO identifies and visualizes the risk factor status of variables for PPD, including comorbidities, confounders, symptoms, and treatments. The PDO includes 734 classes, 13 object properties, and 4,844 individuals. We also linked known and potential risk factors to their respective codes in the International Classification of Diseases versions 9 and 10 that would be useful in structured EHR data analyses. The representation and usefulness of the PDO was assessed using a task-based patient case study approach, involving 10 PPD case studies. Final evaluation of the ontology yielded 86.4% coverage of PPD symptoms, treatments, and risk factors. This demonstrates strong coverage of the PDO for the PPD domain. Conclusion The PDO will enable future researchers to study PPD using EHR data as it contains important information with regard to structured (e.g., billing codes) and unstructured data (e.g., synonyms of symptoms not coded in EHRs). The PDO is publicly available through the National Center for Biomedical Ontology (NCBO) BioPortal ( https://bioportal.bioontology.org/ontologies/PARTUMDO ) which will enable other informaticists to utilize the PDO to study PPD in other populations.

Knowledge Representation and Management 2022: Findings in Ontology Development and Applications

“…Morse et al [16] created a Postpartum Depression Ontology (PDO), which encompassed relevant comorbidities, symptoms, treatments, and risk factors associated with postpartum depression (PPD). The PDO incorporated both structured (e.g., International Classification of Diseases versions 9 and 10 codes) and unstructured information (e.g., synonyms of symptoms without standardized codes), aiming to assist in identifying postpartum depression (PPD) patients and supporting PPD research based on electronic health record data.…”

“…We obtained 387 papers after the section editors' initial screening. The section editors further reviewed these papers jointly and reached a consensus list of 15 papers, which were nominated as the candidate best papers [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. External reviewers, IMIA Yearbook editors and section editors further evaluated these 15 papers and finally selected two best papers (see Table 1).…”