Predicting Hospital Readmission for Campylobacteriosis from Electronic Health Records: A Machine Learning and Text Mining Perspective.
J Pers Med
; 12(1)2022 Jan 10.
Article
em En
| MEDLINE
| ID: mdl-35055401
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.
Texto completo:
1
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article