Identifying vulnerable older adult populations by contextualizing geriatric syndrome information in clinical notes of electronic health records. (2nd July 2019)

• Record Type: Journal Article
• Authors: Chen, Tao; Dredze, Mark; Weiner, Jonathan P; Kharrazi, Hadi
• Is Part Of: Journal of the American Medical Informatics Association. Volume 26:Number 8/9(2019)
• Page Start: 787
• Page End: 795
• Publication Date: 2019-07-02
• Subjects: geriatric syndrome -- vulnerable geriatric population -- electronic health records -- clinical notes -- natural language processing -- deep neural network -- sentence classification; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocz093
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Geriatric syndromes such as functional disability and lack of social support are often not encoded in electronic health records (EHRs), thus obscuring the identification of vulnerable older adults in need of additional medical and social services. In this study, we automatically identify vulnerable older adult patients with geriatric syndrome based on clinical notes extracted from an EHR system, and demonstrate how contextual information can improve the process. Materials and Methods: We propose a novel end-to-end neural architecture to identify sentences that contain geriatric syndromes. Our model learns a representation of the sentence and augments it with contextual information: surrounding sentences, the entire clinical document, and the diagnosis codes associated with the document. We trained our system on annotated notes from 85 patients, tuned the model on another 50 patients, and evaluated its performance on the rest, 50 patients. Results: Contextual information improved classification, with the most effective context coming from the surrounding sentences. At sentence level, our best performing model achieved a micro-F1 of 0.605, significantly outperforming context-free baselines. At patient level, our best model achieved a micro-F1 of 0.843. Discussion: Our solution can be used to expand the identification of vulnerable older adults with geriatric syndromes. Since functional and social factors are often not captured by diagnosis codes in EHRs, the automatic identification of the geriatric syndrome can reduce disparities by ensuring consistent care across the older adult population. Conclusion: EHR free-text can be used to identify vulnerable older adults with a range of geriatric syndromes.