Name
Abstract | ||
|---|---|---|
One of the goals of the Precision Medicine Initiative launched in the United States in 2016 is to use innovative tools and sources in data science. We realized this goal by implementing a use case that identified patients with heart failure at Veterans Health Administration using data from the Electronic Health Records from multiple health domains between 2005 and 2013. We applied a regularized logistic regression model and predicted 30-day readmission risk for 1210 unique patients. Our validation cohort resulted in a C-statistic of 0.84. Our top predictors of readmission were prior diagnosis of heart failure, vascular and renal diseases, and malnutrition as comorbidities, compliance with outpatient follow-up, and low socioeconomic status. This validated risk prediction scheme delivered better performance than the published models so far (C-Statistics: 0.69). It can be used to strati patients for readmission and to aid clinicians in delivering precise health interventions. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3233/978-1-61499-830-3-506 | Studies in Health Technology and Informatics |
Keywords | DocType | Volume |
Patient Readmission,Models,Theoretical,Heart Failure | Conference | 245 |
ISSN | Citations | PageRank |
0926-9630 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Satish M Mahajan | 1 | 0 | 0.68 |
| Prabir Burman | 2 | 14 | 3.34 |
| Ana Newton | 3 | 0 | 0.34 |
| Paul Heidenreich | 4 | 6 | 2.22 |