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Predicting hospital readmission for Campylobacteriosis from electronic health records: A machine learning and text mining perspectivel

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posted on 17.03.2022, 16:41 by Shang-Ming Zhou, Ronan A. Lyons, Muhammad A. Rahman, Alexander Holborow, Sinead Brophy
(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

History

Published in

Journal of Personalized Medicine

Publisher

MDPI

Version

VoR (Version of Record)

Citation

Zhou, Shang-Ming, Ronan A. Lyons, Muhammad A. Rahman, Alexander Holborow, and Sinead Brophy (2022) "Predicting Hospital Readmission for Campylobacteriosis from Electronic Health Records: A Machine Learning and Text Mining Perspective" Journal of Personalized Medicine 12, no. 1: 86. https://doi.org/10.3390/jpm12010086

Electronic ISSN

2075-4426

Cardiff Met Affiliation

  • Cardiff School of Technologies

Cardiff Met Authors

Muhammad A. Rahman

Copyright Holder

© The Authors

Language

en