Research map / Reinforcement learning / Policy gradient and actor-critic
Deep Reinforcement Learning for Cost-Effective Medical Diagnosis
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SM-DDPO instead of the strongest listed baseline | Clinical diagnostic policy · AKI | AM: 0.714 -> 0.741 | Helped |
| Use SM-DDPO instead of the strongest listed baseline | Clinical diagnostic policy · Ferritin | AM: 0.842 -> 0.845 | Helped |
| Use SM-DDPO instead of the strongest listed baseline | Clinical diagnostic policy · Sepsis | AM: 0.752 -> 0.754 | Helped |
| Optimize the dynamic policy for F1 | SM-DDPO · Ferritin | F1: 0.624 | Helped |
| Optimize the dynamic policy for F1 | SM-DDPO · AKI | F1: 0.495 | Helped |
| Optimize the dynamic policy for F1 | SM-DDPO · Sepsis | F1: 0.562 | Helped |
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