ArticleApplied clinical informatics2019
Unsupervised Machine Learning of Topics Documented by Nurses about Hospitalized Patients Prior to a Rapid-Response Event.
Article in Applied clinical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Automated Identification of Nursing Diagnoses and Interventions From Nursing Records Using a Retrieval-Augmented Large Language Model Approach: Quantitative Study.Journal of medical Internet research · 2026Article
- A topic modeling analysis of stigma dimensions, social, and related behavioral circumstances in clinical notes among patients with HIV.International journal of medical informatics · 2026Article
- Implementation of Passive Deterioration Index Alerts in an Intermediate Care Unit: A Failed Early Warning System Strategy.Applied clinical informatics · 2025Article
- "Be Really Careful about That": Clinicians' Perceptions of an Intelligence Augmentation Tool for In-Hospital Deterioration Detection.Applied clinical informatics · 2025Article
- Supervised machine learning applied in nursing notes for identifying the need of childhood cancer patients for psychosocial support.Frontiers in digital health · 2025Article
- Indicators of clinical deterioration in adult general ward patients from nurses' perspectives: a mixed-methods systematic review.BMC nursing · 2024Article
- A Scalable and Extensible Logical Data Model of Electronic Health Record Audit Logs for Temporal Data Mining (RNteract): Model Conceptualization and Formulation.JMIR nursing · 2024Article
- Identifying High-Need Primary Care Patients Using Nursing Knowledge and Machine Learning Methods.Applied clinical informatics · 2023Article
- Article
- Machine learning applied to electronic health record data in home healthcare: A scoping review.International journal of medical informatics · 2023Article
- Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning Approaches.Applied clinical informatics · 2022Article
- Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring.Applied clinical informatics · 2021Article
- Using nursing notes to improve clinical outcome prediction in intensive care patients: A retrospective cohort study.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Home Healthcare Clinical Notes Predict Patient Hospitalization and Emergency Department Visits.Nursing researchArticle
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
backgroundIn the hospital setting, it is crucial to identify patients at risk for deterioration before it fully develops, so providers can respond rapidly to reverse the deterioration. Rapid response (RR) activation criteria include a subjective component ("worried about the patient") that is often documented in nurses' notes and is hard to capture and quantify, hindering active screening for deteriorating patients.
objectivesWe used unsupervised machine learning to automatically discover RR event risk/protective factors from unstructured nursing notes.
methodsIn this retrospective cohort study, we obtained nursing notes of hospitalized, nonintensive care unit patients, documented from 2015 through 2018 from Partners HealthCare databases. We applied topic modeling to those notes to reveal topics (clusters of associated words) documented by nurses. Two nursing experts named each topic with a representative Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) concept. We used the concepts along with vital signs and demographics in a time-dependent covariates extended Cox model to identify risk/protective factors for RR event risk.
resultsFrom a total of 776,849 notes of 45,299 patients, we generated 95 stable topics, of which 80 were mapped to 72 distinct SNOMED CT concepts. Compared with a model containing only demographics and vital signs, the latent topics improved the model's predictive ability from a concordance index of 0.657 to 0.720. Thirty topics were found significantly associated with RR event risk at a 0.05 level, and 11 remained significant after Bonferroni correction of the significance level to 6.94E-04, including physical examination (hazard ratio [HR] = 1.07, 95% confidence interval [CI], 1.03-1.12), informing doctor (HR = 1.05, 95% CI, 1.03-1.08), and seizure precautions (HR = 1.08, 95% CI, 1.04-1.12).
conclusionUnsupervised machine learning methods can automatically reveal interpretable and informative signals from free-text and may support early identification of patients at risk for RR events.
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