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Predicting Hospital Readmission for Campylobacteriosis from Electronic Health Records: A Machine Learning and Text Mining Perspective.
- Source :
-
Journal of Personalized Medicine . Jan2022, Vol. 12 Issue 1, p86-N.PAG. 1p. - Publication Year :
- 2022
-
Abstract
- (1) Background: This study investigates influential risk factors for predicting 30-day readmission to hospital for Campylobacter infections (CI). (2) Methods: We linked general practitioner and hospital admission records of 13,006 patients with CI in Wales (1990–2015). An approach called TF-zR (term frequency-zRelevance) technique was presented to evaluates how relevant a clinical term is to a patient in a cohort characterized by coded health records. The zR is a supervised term-weighting metric to assign weight to a term based on relative frequencies of the term across different classes. Cost-sensitive classifier with swarm optimization and weighted subset learning was integrated to identify influential clinical signals as predictors and optimal model for readmission prediction. (3) Results: From a pool of up to 17,506 variables, 33 most predictive factors were identified, including age, gender, Townsend deprivation quintiles, comorbidities, medications, and procedures. The predictive model predicted readmission with 73% sensitivity and 54% specificity. Variables associated with readmission included male gender, recurrent tonsillitis, non-healing open wounds, operation for in-gown toenails. Cystitis, paracetamol/codeine use, age (21–25), and heliclear triple pack use, were associated with a lower risk of readmission. (4) Conclusions: This study gives a profile of clustered variables that are predictive of readmission associated with campylobacteriosis. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20754426
- Volume :
- 12
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Journal of Personalized Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 154855735
- Full Text :
- https://doi.org/10.3390/jpm12010086