Predicting control of cardiovascular disease risk factors in South Asia using machine learning
Anna Reuter, Mohammed K Ali, Viswanathan Mohan, Lydia Chwastiak, Kavita Singh, K M Venkat Narayan, Dorairaj Prabhakaran, Nikhil Tandon, Nikkil Sudharsanan · 2024
Read Predicting control of cardiovascular disease risk factors in South Asia using machine learning by Anna Reuter, Mohammed K Ali, Viswanathan Mohan, Lydia Chwastiak, Kavita Singh, K M Venkat Narayan, Dorairaj Prabhakaran, Nikhil Tandon, Nikkil Sudharsanan (2024) 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-11631980.txt.
Description
Anna Reuter and co-authors use machine learning to flag diabetes patients in South Asia who may miss heart risk goals, in work from twenty twenty-four. Many at-risk patients fail to control sugar, pressure, and cholesterol together. Clinicians lack a structured way to spot those patients early. Data come from two finished trials in urban India and Pakistan. Over fifteen hundred adults with diabetes feed the models.
Common clinic data predict failure to hit goals a year later. Patients in the top risk quarter are flagged for extra support. Flagging proves sharpest for sugar control and for meaningful gains. Precision for pressure and cholesterol goal failure runs lower. Gains-based flags score high across all three risk factors, though. Such flags could target scarce staff time where it helps most. Routine care in low-resource clinics is the intended home.
Catalog record
| CREATOR | Anna Reuter, Mohammed K Ali, Viswanathan Mohan, Lydia Chwastiak, Kavita Singh, K M Venkat Narayan, Dorairaj Prabhakaran, Nikhil Tandon, Nikkil Sudharsanan |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 44 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |