ArticleJournal of the American Medical Informatics Association : JAMIA2023
Predicting emergency department visits and hospitalizations for patients with heart failure in home healthcare using a time series risk model.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Developing and validating a sequence-aware deep learning model for infection risk prediction in home care.International journal of medical informatics · 2026Article
- Beyond Clinical Acuity: Cardiac Comorbidity and Complication Profiles and the Prehospital Hospitalization/Transport Decision in Ischemic Heart Disease-A Five-Year Retrospective Emergency Medical Service Study in Astana, Kazakhstan.Healthcare (Basel, Switzerland) · 2026Article
- Optimizing temporal windows for wearable-augmented post-discharge risk prediction: a methods study.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Early emergency department decision support for heart failure hospitalization using triage-level unstructured and structured data: a retrospective cohort study.BMC medical informatics and decision making · 2026Article
- A comparative analysis of machine learning models and human expertise for nursing intervention classification.JAMIA open · 2025Article
- Using Under-Represented Subgroup Fine Tuning to Improve Fairness for Disease Prediction.Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference) · 2025Article
- Does synthetic data augmentation improve the performances of machine learning classifiers for identifying health problems in patient-nurse verbal communications in home healthcare settings?Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Article
- Developing a clinical decision support framework for integrating predictive models into routine nursing practices in home health care for patients with heart failure.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Article
- Exploring home healthcare clinicians' needs for using clinical decision support systems for early risk warning.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Evaluating the validity of the nursing statements algorithmically generated based on the International Classifications of Nursing Practice for respiratory nursing care using large language models.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review.JMIR medical informatics · 2024Article
- Healthcare risk stratification model for emergency departments based on drugs, income and comorbidities: the DICER-score.BMC emergency medicine · 2024Article
- The Growing Impact of Natural Language Processing in Healthcare and Public Health.Inquiry : a journal of medical care organization, provision and financingReview
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Authors and funding
12 authors.
Funding
Abstract
objectivesLittle is known about proactive risk assessment concerning emergency department (ED) visits and hospitalizations in patients with heart failure (HF) who receive home healthcare (HHC) services. This study developed a time series risk model for predicting ED visits and hospitalizations in patients with HF using longitudinal electronic health record data. We also explored which data sources yield the best-performing models over various time windows. MATERIALS AND
methodsWe used data collected from 9362 patients from a large HHC agency. We iteratively developed risk models using both structured (eg, standard assessment tools, vital signs, visit characteristics) and unstructured data (eg, clinical notes). Seven specific sets of variables included: (1) the Outcome and Assessment Information Set, (2) vital signs, (3) visit characteristics, (4) rule-based natural language processing-derived variables, (5) term frequency-inverse document frequency variables, (6) Bio-Clinical Bidirectional Encoder Representations from Transformers variables, and (7) topic modeling. Risk models were developed for 18 time windows (1-15, 30, 45, and 60 days) before an ED visit or hospitalization. Risk prediction performances were compared using recall, precision, accuracy, F1, and area under the receiver operating curve (AUC).
resultsThe best-performing model was built using a combination of all 7 sets of variables and the time window of 4 days before an ED visit or hospitalization (AUC = 0.89 and F1 = 0.69). DISCUSSION AND
conclusionThis prediction model suggests that HHC clinicians can identify patients with HF at risk for visiting the ED or hospitalization within 4 days before the event, allowing for earlier targeted interventions.
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