Journal article 559 views 118 downloads
Predicting Hospital Readmission for Campylobacteriosis from Electronic Health Records: A Machine Learning and Text Mining Perspective
,
Ronan Lyons ,
Muhammad A. Rahman,
Alexander Holborow,
Sinead Brophy
Journal of Personalized Medicine, Volume: 12, Issue: 1, Start page: 86
Swansea University Authors:
Shang-ming Zhou , Ronan Lyons , Sinead Brophy
-
PDF | Version of Record
Copyright: © 2022 by the authors. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
Download (1.72MB)
DOI (Published version): 10.3390/jpm12010086
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...
| Published in: | Journal of Personalized Medicine |
|---|---|
| ISSN: | 2075-4426 |
| Published: |
MDPI AG
2022
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa59184 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| 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. |
|---|---|
| Keywords: |
hospitalisation, readmission, Campylobacter infections, machine learning, text mining, feature selection, electronic health records |
| College: |
Faculty of Medicine, Health and Life Sciences |
| Funders: |
This work was supported by the Health Data Research UK (NIWA1). This analysis was also
undertaken with the support of the National Centre for Population Health and Wellbeing Research
(NCPHWR) via Health and Care Research Wales (grant ref. CA02). |
| Issue: |
1 |
| Start Page: |
86 |