Real-World Pharmacotherapy-Driven Cardiovascular Risk Prediction Using Interpretable Machine Learning and Jordanian EHR Data
Said Moshawih, Lobna Gharaibeh, Islam Alfreahat, Abeer Jabra Shnoudeh · 2026
Read Real-World Pharmacotherapy-Driven Cardiovascular Risk Prediction Using Interpretable Machine Learning and Jordanian EHR Data by Said Moshawih, Lobna Gharaibeh, Islam Alfreahat, Abeer Jabra Shnoudeh (2026) 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/PMC13413450.txt.
Description
Said Moshawih and co-authors build in 2026 a heart risk tool tuned to Jordan and to the drugs patients take. Stock risk sums tend to miss the mark in poorer lands, where most heart deaths now fall. The study used the national Hakeem health record system. It drew on about 600,000 people seen from 2018 to 2022. Diagnoses, blood pressure, lab values, and drug lists fed three data sets of mixed depth.
The group carried a heavy load: near half had high blood pressure or diabetes, and over half had poor blood fats. The final model, named JoRisk, scored 0.844 on the usual curve test. Lipid drugs, swings in blood pressure, age, and sex drove it most. The tool stays open to read, which the authors say helps clinic staff trust it.
Catalog record
| CREATOR | Said Moshawih, Lobna Gharaibeh, Islam Alfreahat, Abeer Jabra Shnoudeh |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 67417 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |