Medical ontology learning framework to investigate daytime impairment in insomnia disorder and treatment effects
Abstract Background Specificity challenges frequently arise in medical ontology used for the representation of real-world data, particularly in defining mental health disorders within widely used classification systems such as the International Classification of Diseases (ICD). This study aims to ad...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-024-00698-2 |
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| author | Alexander J. Büsser Renato Durrer Moritz Freidank Matteo Togninalli Antonio Olivieri Michael A. Grandner William V. McCall |
| author_facet | Alexander J. Büsser Renato Durrer Moritz Freidank Matteo Togninalli Antonio Olivieri Michael A. Grandner William V. McCall |
| author_sort | Alexander J. Büsser |
| collection | DOAJ |
| description | Abstract Background Specificity challenges frequently arise in medical ontology used for the representation of real-world data, particularly in defining mental health disorders within widely used classification systems such as the International Classification of Diseases (ICD). This study aims to address these challenges by introducing the Disease-Specific Medical Ontology Learning (DiSMOL) framework, designed to generate precise disease representations from clinical physician notes, with a focus on daytime impairment in insomnia disorder. Methods The study applied the Disease-Specific Medical Ontology Learning framework to clinical notes to better represent daytime impairment. The framework’s performance was compared to insomnia expert-selected codes from ICD. Key statistical methods included sensitivity and F1-score comparisons, as well as analysis of symptom changes after the use of various medications, including benzodiazepines, non-benzodiazepine receptor agonists, and trazodone. Results The DiSMOL framework significantly enhances the identification of daytime impairment in people with insomnia. Sensitivity increases from 17% to 98%, and the F1-score improves from 28% to 86%, compared with expert-selected ICD codes. Additionally, the framework reveals significant increases in daytime impairment symptoms following benzodiazepine use (18.9%), while traditional ICD codes do not detect any significant change. Conclusions The study demonstrates that DiSMOL offers a more accurate method for identifying specific disease aspects, such as daytime impairment in insomnia, than traditional coding systems. These findings highlight the potential of specialized ontologies to enhance the representation and analysis of real-world clinical data, with important implications for healthcare policy and personalized medicine. |
| format | Article |
| id | doaj-art-48efd30f7600445fb4f52d10f277318b |
| institution | DOAJ |
| issn | 2730-664X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-48efd30f7600445fb4f52d10f277318b2025-08-20T02:54:37ZengNature PortfolioCommunications Medicine2730-664X2025-02-01511910.1038/s43856-024-00698-2Medical ontology learning framework to investigate daytime impairment in insomnia disorder and treatment effectsAlexander J. Büsser0Renato Durrer1Moritz Freidank2Matteo Togninalli3Antonio Olivieri4Michael A. Grandner5William V. McCall6Idorsia PharmaceuticalsVisiumVisiumVisiumIdorsia PharmaceuticalsDepartment of Psychiatry, University of Arizona College of MedicineDepartment of Psychiatry and Health Behavior, Medical College of Georgia at Augusta UniversityAbstract Background Specificity challenges frequently arise in medical ontology used for the representation of real-world data, particularly in defining mental health disorders within widely used classification systems such as the International Classification of Diseases (ICD). This study aims to address these challenges by introducing the Disease-Specific Medical Ontology Learning (DiSMOL) framework, designed to generate precise disease representations from clinical physician notes, with a focus on daytime impairment in insomnia disorder. Methods The study applied the Disease-Specific Medical Ontology Learning framework to clinical notes to better represent daytime impairment. The framework’s performance was compared to insomnia expert-selected codes from ICD. Key statistical methods included sensitivity and F1-score comparisons, as well as analysis of symptom changes after the use of various medications, including benzodiazepines, non-benzodiazepine receptor agonists, and trazodone. Results The DiSMOL framework significantly enhances the identification of daytime impairment in people with insomnia. Sensitivity increases from 17% to 98%, and the F1-score improves from 28% to 86%, compared with expert-selected ICD codes. Additionally, the framework reveals significant increases in daytime impairment symptoms following benzodiazepine use (18.9%), while traditional ICD codes do not detect any significant change. Conclusions The study demonstrates that DiSMOL offers a more accurate method for identifying specific disease aspects, such as daytime impairment in insomnia, than traditional coding systems. These findings highlight the potential of specialized ontologies to enhance the representation and analysis of real-world clinical data, with important implications for healthcare policy and personalized medicine.https://doi.org/10.1038/s43856-024-00698-2 |
| spellingShingle | Alexander J. Büsser Renato Durrer Moritz Freidank Matteo Togninalli Antonio Olivieri Michael A. Grandner William V. McCall Medical ontology learning framework to investigate daytime impairment in insomnia disorder and treatment effects Communications Medicine |
| title | Medical ontology learning framework to investigate daytime impairment in insomnia disorder and treatment effects |
| title_full | Medical ontology learning framework to investigate daytime impairment in insomnia disorder and treatment effects |
| title_fullStr | Medical ontology learning framework to investigate daytime impairment in insomnia disorder and treatment effects |
| title_full_unstemmed | Medical ontology learning framework to investigate daytime impairment in insomnia disorder and treatment effects |
| title_short | Medical ontology learning framework to investigate daytime impairment in insomnia disorder and treatment effects |
| title_sort | medical ontology learning framework to investigate daytime impairment in insomnia disorder and treatment effects |
| url | https://doi.org/10.1038/s43856-024-00698-2 |
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