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.
| Original language | English |
|---|---|
| Article number | 86 |
| Pages (from-to) | 86 |
| Number of pages | 1 |
| Journal | Journal of Personalized Medicine |
| Volume | 12 |
| Issue number | 1 |
| Early online date | 10 Jan 2022 |
| DOIs | |
| Publication status | Published - 10 Jan 2022 |
Keywords
- Campylobacter infections
- Electronic health records
- Feature selection
- Hospitalisation
- Machine learning
- Readmission
- Text mining
Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver