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Research map / Probabilistic and scientific modeling / Molecules and proteins

Learning to Select Best Forecast Tasks for Clinical Outcome Prediction

NeurIPS 2020 · Probabilistic and scientific modeling › Molecules and proteins · approach: Spatiotemporal forecasting and imputation · 5 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
learn auxiliary task weights with AutoSelect instead of equal-weight pretrainingPretrain (All) · ICU Mortality, 1% dataAUC-ROC: 0.809 -> 0.833Helped
learn auxiliary task weights with AutoSelectPretrain (All) · ICU BP, 1% dataAUC-ROC: 0.778 -> 0.838Helped
learn auxiliary task weights with AutoSelectPretrain (All) · ICU KD, 1% dataAUC-ROC: 0.771 -> 0.823Helped
learn auxiliary task weights with AutoSelectPretrain (All) · ICU Mortality, 100% dataAUC-ROC: 0.899 -> 0.909No clear effect
pretrain with 96 forecast tasksSupervised · ICU Mortality, 10% dataAUC-ROC: 0.853 -> 0.853No clear effect
pretrain with 96 forecast tasksSupervised · ICU Mortality, 1% dataAUC-ROC: 0.738 -> 0.809Helped
co-train target task with all auxiliary tasksSupervised · ICU Mortality, 1% dataAUC-ROC: 0.738 -> 0.725Hurt
pretrain only on top selected tasks (ablation)Pretrain (All) · ICU BP, 1% dataAUC-ROC: 0.778 -> 0.812Helped
pretrain on non-top tasks only (ablation)Pretrain (All) · ICU Mortality, 1% dataAUC-ROC: 0.809 -> 0.772Hurt
pretrain only on top selected tasks (ablation)AutoSelect · ICU Mortality, 10% dataAUC-ROC: 0.882 -> 0.850Hurt
use mortality-learned task weights to transfer to BPAutoSelect · ICU BP, 1% dataAUC-ROC: 0.778 -> 0.842Helped
use mortality-learned task weights to transfer to KDAutoSelect · ICU KD, 1% dataAUC-ROC: 0.771 -> 0.833Helped

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