Prediction of Readmission in Geriatric Patients From Clinical Notes: Retrospective Text Mining Study.
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ABSTRACT: Background
Prior literature suggests that psychosocial factors adversely impact health and health care utilization outcomes. However, psychosocial factors are typically not captured by the structured data in electronic medical records (EMRs) but are rather recorded as free text in different types of clinical notes.Objective
We here propose a text-mining approach to analyze EMRs to identify older adults with key psychosocial factors that predict adverse health care utilization outcomes, measured by 30-day readmission. The psychological factors were appended to the LACE (Length of stay, Acuity of the admission, Comorbidity of the patient, and Emergency department use) Index for Readmission to improve the prediction of readmission risk.Methods
We performed a retrospective analysis using EMR notes of 43,216 hospitalization encounters in a hospital from January 1, 2017 to February 28, 2019. The mean age of the cohort was 67.51 years (SD 15.87), the mean length of stay was 5.57 days (SD 10.41), and the mean intensive care unit stay was 5% (SD 22%). We employed text-mining techniques to extract psychosocial topics that are representative of these patients and tested the utility of these topics in predicting 30-day hospital readmission beyond the predictive value of the LACE Index for Readmission.Results
The added text-mined factors improved the area under the receiver operating characteristic curve of the readmission prediction by 8.46% for geriatric patients, 6.99% for the general hospital population, and 6.64% for frequent admitters. Medical social workers and case managers captured more of the psychosocial text topics than physicians.Conclusions
The results of this study demonstrate the feasibility of extracting psychosocial factors from EMR clinical notes and the value of these notes in improving readmission risk prediction. Psychosocial profiles of patients can be curated and quantified from text mining clinical notes and these profiles can be successfully applied to artificial intelligence models to improve readmission risk prediction.
SUBMITTER: Goh KH
PROVIDER: S-EPMC8564665 | biostudies-literature |
REPOSITORIES: biostudies-literature
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| S-EPMC8429931 | biostudies-literature
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| S-EPMC6157016 | biostudies-literature
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| S-EPMC6781727 | biostudies-literature
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| S-EPMC9075435 | biostudies-literature
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| S-EPMC4136398 | biostudies-literature
Project description:Purpose: Build genesets using text mining and validate the geneset with data from public repository and a dataset generated in house using in vitro models and RNA-seq Methods: Mucus hypersecretion and mucociliary dysfucntion adverse outcome pathways (AOPs) genesets were build using the text mining method described by Rani et al., 2015. Validation was performed using RNA expression data from cigarette and e-cigarette aerosol treated cells, IL-13 treated airway cells, and COPD-lung biopsies. The cigarette and e-cigarette aerosol RNA-samples from airway cells were generated and sequenced in house. The other dataset were publically available. Results: Using unsupervised clustering, the mucus hypersecretion and mucociliary dysfunction genesets were able to discriminate the cigartte treated cells from the e-cigarettes and the air control. The e-cigarette and the air control clustered together. Clustering was also observed with IL-13 treated cells. IL-13 is an induced of mucus hypersecretion. Clustering was not observed when COPD RNA-seq samples were used. PCA analysis revealed some degree of grouping based on disease status, but this was also heavily confounded by other parameters. Conclusions: Our study described the first application of text mining to build genesets relevant to AOPs. In vitro validation confirmed the genesets could discriminates between treatment that induce mucus hypersecretion phenotypes, however this could not be confirmed with COPD biopsy samples. This could be due to a series of technical confouding factors and the heterogeneity of the COPD disease.
2020-04-30 | GSE142100 | GEO
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| S-EPMC8031429 | biostudies-literature