Research map / Learning theory and algorithms / Prediction and decision losses
ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Bayesian belief-based forecaster with Gaussian density estimation, temporal alignment, and threshold/smoothing alarm (ForecastICU) vs. LR, SVM, Lasso, random forest | ICU admission prediction · UCLA ICU admission dataset | PPV at TPR 50%: 54.7% vs 49.6% best benchmark (+5.1%) | Helped |
| ForecastICU vs. best benchmark on TPR at PPV 40% | ICU admission prediction · UCLA ICU admission dataset | TPR at PPV 40%: 68.1% TPR, +7.8% vs best benchmark | Helped |
| ForecastICU vs. Rothman index (deployed technology) | ICU admission risk assessment · UCLA ICU admission dataset | AUC: +22.3% AUC | Helped |
| ForecastICU vs. Rothman index on false alarms per true alarm at TPR 0.5 | ICU admission alarms · UCLA ICU admission dataset | false alarms per true alarm: 0.84 vs 2.34 | Helped |
| ForecastICU vs. best benchmark on timeliness at PPV 50%/TPR 50% | ICU admission prediction · UCLA ICU admission dataset | alarm lead time (hours): about 2-3 hours earlier than best benchmark; 9 hours before physician decision at PPV 40%, TPR 50% | Helped |
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