Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets
| Autor: | Steven M. Asch, Lester Mackey, Mary K. Goldstein, Jonathan H. Chen, Russ B. Altman |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Topic model
Computer science Health Informatics Context (language use) computer.software_genre Machine learning Research and Applications 01 natural sciences Clinical decision support system Latent Dirichlet allocation Medical Order Entry Systems 03 medical and health sciences symbols.namesake 0302 clinical medicine Humans 030212 general & internal medicine 0101 mathematics probabilistic topic modeling clinical summarization Interpretability clinical decision support systems Models Statistical business.industry Diagnostic Tests Routine 010102 general mathematics Probabilistic logic Usability data mining Decision Support Systems Clinical Automatic summarization Hospitalization Editor's Choice electronic health records ROC Curve order sets symbols Artificial intelligence Patient Care business computer Natural language processing Algorithms |
| Zdroj: | Journal of the American Medical Informatics Association : JAMIA |
| ISSN: | 1527-974X 1067-5027 |
| Popis: | Objective: Build probabilistic topic model representations of hospital admissions processes and compare the ability of such models to predict clinical order patterns as compared to preconstructed order sets.Materials and Methods: The authors evaluated the first 24 hours of structured electronic health record data for > 10 K inpatients. Drawing an analogy between structured items (e.g., clinical orders) to words in a text document, the authors performed latent Dirichlet allocation probabilistic topic modeling. These topic models use initial clinical information to predict clinical orders for a separate validation set of > 4 K patients. The authors evaluated these topic model-based predictions vs existing human-authored order sets by area under the receiver operating characteristic curve, precision, and recall for subsequent clinical orders.Results: Existing order sets predict clinical orders used within 24 hours with area under the receiver operating characteristic curve 0.81, precision 16%, and recall 35%. This can be improved to 0.90, 24%, and 47% (P Discussion: Existing order sets tend to provide nonspecific, process-oriented aid, with usability limitations impairing more precise, patient-focused support. Algorithmic summarization has the potential to breach this usability barrier by automatically inferring patient context, but with potential tradeoffs in interpretability.Conclusion: Probabilistic topic modeling provides an automated approach to detect thematic trends in patient care and generate decision support content. A potential use case finds related clinical orders for decision support. |
| Databáze: | OpenAIRE |
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