https://www.selleckchem.com/pr....oducts/palazestrant.
RESULTS The proposed model outperformed the baseline model, with an area under the receiver operating characteristic curve of 0.659 versus 0.512 (P = .019). The feature importance analysis showed that the learned model relied most on temperature and systolic blood pressure, and temporal trends (eg, increasing or decreasing) were more important than the average values. CONCLUSION Leveraging readily available clinical data from EHRs, we developed a machine-learning model for aGVHD prediction in patients undergoing HCT. Continuous mon
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