Development of machine learning models to predict risk of hospitalisation and 90-day readmission among patients with cardiovascular risk factors using community health survey data
Arinze Nkemdirim Okere, Tianfeng Li, Md Mohaimenul Islam, Askal A Ali, Sarah G Buxbaum, Vakaramoko Diaby · 2025
Read Development of machine learning models to predict risk of hospitalisation and 90-day readmission among patients with cardiovascular risk factors using community health survey data by Arinze Nkemdirim Okere, Tianfeng Li, Md Mohaimenul Islam, Askal A Ali, Sarah G Buxbaum, Vakaramoko Diaby (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12766757.txt.
Description
Arinze Okere and co-authors built machine learning models in 2025 that predict hospital stays from what patients report about their own lives, rather than from clinic records. Such survey data is cheap to gather and might flag people headed for the hospital.
The survey reached 1,318 United States adults with at least one heart risk factor. Thirty-five percent had been hospitalized, and about ten percent were readmitted within ninety days. Eight models were trained and tested; a forest of extra randomized trees won both tasks, scoring near perfect on readmission. Heart disease, pill burden, race, work, and insurance status carried the most weight. The authors see a low cost triage aid for clinics and insurers.
Catalog record
| CREATOR | Arinze Nkemdirim Okere, Tianfeng Li, Md Mohaimenul Islam, Askal A Ali, Sarah G Buxbaum, Vakaramoko Diaby |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 27 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |