Problem
Research questions and friction points this paper is trying to address.
medication change prediction
hospital admission
ATC3 additions and removals
R-GEAN: Regimen-Guided Edit Action Network for Within-Admission Medication Change Prediction
๐ค AI Summary
็ ็ฉถ้่ฟR-GEAN็ฝ็ป้ขๆตไฝ้ขๆ้ด่ฏ็ฉๅขๅ๏ผ่งฃๅณไบ็ฐๆๆจกๅไป
ๅคๅถไธๅ่ฏ็ฉ็้ฎ้ข๏ผๆ้ซไบๅฏนๅฎ้
่ฏ็ฉๅๅ็้ขๆตๅ็กฎๆงใ
๐ Abstract
The medications prescribed to a patient often change during a hospital admission as clinicians start, stop, or continue therapies. We study whether models can predict which medication classes are added or removed between 24 hours after admission and discharge. Metrics that compare the complete discharge regimen can reward models for copying medications that remain unchanged, even when they identify no actual changes. We therefore introduce a leakage-controlled benchmark that predicts net ATC3 additions and removals using only prior completed admissions and information available within the first 24 hours of the current admission. Addition candidates are classes not active at 24 hours, whereas removal candidates are classes active at that time. We also introduce R-GEAN, an asymmetric candidate-scoring network with independent addition and removal predictors. Across 240,480 admissions from 82,286 patients, R-GEAN achieves the highest predefined summary of addition, removal, changed-regimen, and action-pattern performance, termed the edit composite (0.464), compared with 0.435 for the strongest primary comparator. Reimplemented RETAIN, GAMENet, and MICRON baselines obtain 0.428, 0.420, and 0.288, respectively. R-GEAN's advantage is concentrated in correctly identifying medication classes no longer active at discharge, while rare additions and admissions with multiple medication changes remain difficult. Rankings based on micro-F1 over the reconstructed discharge regimen and the edit composite correlate weakly across the evaluated models (Spearman r = 0.20). The continuation baseline achieves the highest complete-regimen score despite predicting no additions or removals. These results show that complete-regimen and edit-level evaluation measure different aspects of medication prediction. The benchmark evaluates observed prescribing changes, not treatment appropriateness
Innovation
Methods, ideas, or system contributions that make the work stand out.
R-GEAN
medication change prediction
leakage-controlled benchmark
addition and removal predictors
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