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Unsupervised Machine Learning of Topics Documented by Nurses about Hospitalized Patients Prior to a Rapid-Response EventFunding This work was funded by the National Institute of Nursing Research (NINR) award number 1R01NR016941. Jessica Schwartz is a pre-doctoral fellow funded by the National Institute of Nursing Research Reducing Health Disparities Through Informatics (RHeaDI) T32NR007969 and was funded by a grant from CRICO titled “2017-2019: Resilience in Clinical Deterioration Survival: Learning from Different Outcomes in Critical and Acute Care.”
13 August 2019
06 November 2019
18 December 2019 (online)
Background In 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.
Objectives We used unsupervised machine learning to automatically discover RR event risk/protective factors from unstructured nursing notes.
Methods In 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.
Results From 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).
Conclusion Unsupervised 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.
Keywordshospital rapid response team - nursing assessment - electronic health records - survival analysis - nursing notes - natural language processing - machine learning - medicine
Protection of Human and Animal Subjects
The study was approved by the institutional review board of Partners HealthCare System.
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