Research map / Learning theory and algorithms / Combinatorial and search optimization
Detecting Sources of Healthcare Associated Infections
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add truncated expected load propagation to KnapsackSD (ablation) | KnapsackSD on UIHCUNIT · UIHCUNIT | speedup: 17.2× | Helped |
| Add lazy evaluation to KnapsackSD (ablation) | KnapsackSD on UIHCUNIT · UIHCUNIT | speedup: 1.09× | Helped |
| Add truncated expected load propagation to RatioSD (ablation) | RatioSD on UIHCUNIT · UIHCUNIT | speedup: 5.6× | Helped |
| Use detected sources {409, 753} instead of source {214} | Load sharing model simulations of the UIHCUNIT CDI outbreak · UIHCUNIT CDI outbreak | observed infections hit across 1000 simulations: 373 -> 807 | Helped |
| Use detected sources {409, 753} instead of source {214} | Load sharing model simulations of the UIHCUNIT CDI outbreak · UIHCUNIT CDI outbreak | unobserved infections across 1000 simulations: 2765 -> 2228 | Helped |
| Use KnapsackSD and RatioSD rather than the evaluated baselines | Source detection on synthetic outbreaks over hospital contact networks · UIHC, UIHCUNIT, UVAPRECOVID, UVAPOSTCOVID, and CARILION | F1-score and MCC: up to 5.97 times | Helped |
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