{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T16:49:39Z","timestamp":1792687779261,"version":"4.5.0"},"reference-count":53,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T00:00:00Z","timestamp":1641772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Health Data Research UK","award":["NIWA1"],"award-info":[{"award-number":["NIWA1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>(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\u20132015). 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\u201325), 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.<\/jats:p>","DOI":"10.3390\/jpm12010086","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T17:42:25Z","timestamp":1641836545000},"page":"86","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Predicting Hospital Readmission for Campylobacteriosis from Electronic Health Records: A Machine Learning and Text Mining Perspective"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0719-9353","authenticated-orcid":false,"given":"Shang-Ming","family":"Zhou","sequence":"first","affiliation":[{"name":"Centre for Health Technology, Faculty of Health, University of Plymouth, Plymouth PL4 8AA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronan A.","family":"Lyons","sequence":"additional","affiliation":[{"name":"Health Data Research UK, Swansea University Medical School, Swansea University, Swansea SA2 8PP, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0805-7461","authenticated-orcid":false,"given":"Muhammad A.","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Cardiff Metropolitan University, Cardiff CF5 2YB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Holborow","sequence":"additional","affiliation":[{"name":"South West Wales Cancer Centre, Singleton Hospital, Swansea SA2 8QA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sinead","family":"Brophy","sequence":"additional","affiliation":[{"name":"Health Data Research UK, Swansea University Medical School, Swansea University, Swansea SA2 8PP, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1086\/509936","article-title":"The Health Burden of Campylobacter Infection and the Impact of Antimicrobial Resistance: Playing Chicken","volume":"44","year":"2007","journal-title":"Clin. 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