Clinical risk factors and machine learning for venous thromboembolism in lung cancer: an exploratory single-center study
Hongqin Jia, Yu Chen, Kang Qian, Hongbin Zhu · 2026
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Description
Jia and colleagues studied in 2026 which lung cancer patients develop venous thromboembolism, a dangerous clot in the veins. They also asked whether machine learning predicts it better than standard statistics. Clots are a common, severe side problem in lung cancer, so early warning signs matter. The team reviewed records from one Chinese hospital.
Of 180 patients, those with higher D-dimer, a blood marker of clot breakdown, faced greater clot risk. Early chemotherapy rounds also tracked with more clots, though the authors suspect that link reflects who gets chosen for treatment, not a true cause. Among four learning models, random forest ranked risk best but was poorly calibrated. A penalized regression gave the most trustworthy probabilities. The authors conclude that learning tools added little over standard methods in this small dataset.
Catalog record
| CREATOR | Hongqin Jia, Yu Chen, Kang Qian, Hongbin Zhu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 42 KB |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |