ArticleJournal of the American Medical Informatics Association : JAMIA2017
Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- OrderRex clinical user testing: a randomized trial of recommender system decision support on simulated cases.Journal of the American Medical Informatics Association : JAMIA · 2020Trial
- Development of a machine learning model using structured and unstructured features for predicting surgery among patients with carpal tunnel syndrome: development and validation.Scientific reports · 2026Article
- A Systematic Review of Topic Modeling Techniques for Electronic Health Records.Healthcare (Basel, Switzerland) · 2026Review
- Use of advanced topic modeling to generate domains for a preference-based index in osteoarthritis.Health and quality of life outcomes · 2024Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Patient Information Summarization in Clinical Settings: Scoping Review.JMIR medical informatics · 2023Article
- Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method.International journal of environmental research and public health · 2023Article
- Combining text mining with clinical decision support in clinical practice: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Consumer perceptions of telehealth for mental health or substance abuse: a Twitter-based topic modeling analysis.JAMIA open · 2022Article
- Predicting the Mortality of ICU Patients by Topic Model with Machine-Learning Techniques.Healthcare (Basel, Switzerland) · 2022Article
- A Data-Driven Algorithm to Recommend Initial Clinical Workup for Outpatient Specialty Referral: Algorithm Development and Validation Using Electronic Health Record Data and Expert Surveys.JMIR medical informatics · 2022Article
- Identifying patterns in administrative tasks through structural topic modeling: A study of task definitions, prevalence, and shifts in a mental health practice's operations during the COVID-19 pandemic.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Evaluation of clustering and topic modeling methods over health-related tweets and emails.Artificial intelligence in medicine · 2021Article
- Clinician involvement in research on machine learning-based predictive clinical decision support for the hospital setting: A scoping review.Journal of the American Medical Informatics Association : JAMIA · 2021Article
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- Web Exclusive. Annals for Hospitalists Inpatient Notes - Realizing the Promises of Hospital Electronic Order Sets.Annals of internal medicine · 2020Article
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- Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Predicting Inpatient Medication Orders From Electronic Health Record Data.Clinical pharmacology and therapeutics · 2020Article
- Physician Usage and Acceptance of a Machine Learning Recommender System for Simulated Clinical Order Entry.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveBuild 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
methodsThe 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.
resultsExisting 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 < 10 -20 ) by using probabilistic topic models to summarize clinical data into up to 32 topics. Many of these latent topics yield natural clinical interpretations (e.g., "critical care," "pneumonia," "neurologic evaluation"). 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.
conclusionProbabilistic 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.
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