Heart transplant allocation on Real heart transplant trajectories 1.0 (April-December 2019)
3,328.7PLYG (Apr)Omniscient
Evaluation Results
| Method | Links | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| OmniscientPolicy Category=Upper bound2026.02 | 3,328.7 | 3,620.68 | 3,383.43 | 3,621.28 | 3,937.68 | 3,590.33 | 3,377.67 | 3,011.73 | 3,404.93 | 3,475.16 | |
| NN (4)Policy Category=Non-linear pot., Model Type=Neural Network, Number of Features=42026.02 | 3,161.46 | 3,424.41 | 3,195.55 | 3,428.72 | 3,747.19 | 3,389.69 | 3,208.22 | 2,882.68 | 3,245.6 | 3,298.17 | |
| NN (34)Policy Category=Non-linear pot., Model Type=Neural Network, Number of Features=342026.02 | 3,159.21 | 3,426.39 | 3,204.26 | 3,441.19 | 3,744.17 | 3,397.23 | 3,204.37 | 2,886.6 | 3,255.66 | 3,302.12 | |
| SMACPolicy Category=Linear pot., Model Type=Sequential Model-based Algorithm Configuration2026.02 | 3,156.49 | 3,413.65 | 3,195.48 | 3,412.5 | 3,742.73 | 3,382.23 | 3,203.56 | 2,883.59 | 3,237.31 | 3,291.95 | |
| SVMPolicy Category=Linear pot., Model Type=Support Vector Machine2026.02 | 3,152.88 | 3,423.36 | 3,202.31 | 3,441.79 | 3,724.13 | 3,388.08 | 3,209.87 | 2,881.16 | 3,247.1 | 3,296.74 | |
| LRPolicy Category=Linear pot., Model Type=Logistic Regression2026.02 | 3,123.28 | 3,406.34 | 3,186.39 | 3,442.54 | 3,708.29 | 3,390.62 | 3,186.07 | 2,864.91 | 3,249.54 | 3,284.22 | |
| MyopicPolicy Category=Baselines2026.02 | 3,119.34 | 3,401.63 | 3,176.31 | 3,431.31 | 3,707.53 | 3,380.64 | 3,184.58 | 2,867 | 3,253.35 | 3,280.19 | |
| CASPolicy Category=Baselines, Optimization=Imitation learning with logistic regression2026.02 | 2,236.19 | 2,503.84 | 2,293.97 | 2,470.58 | 2,607.67 | 2,428.52 | 2,341.8 | 2,103.34 | 2,198.27 | 2,353.8 | |
| Status quoPolicy Category=Baselines2026.02 | 1,332.56 | 1,494.35 | 1,335.29 | 1,590.27 | 954.02 | 1,632.99 | 1,772.68 | 1,705.2 | 983.93 | 1,422.37 |