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A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Non-parametric Bayesian segmentation with personalized dynamic models and Bhattacharyya-based state mapping (Algorithm 1) versus population risk scores and ML classifiers | clinical state estimation for patients · ICU admission prediction, cancer ward | PPV at TPR 71.9%: PPV 37.4% vs 34.5% (Rothman) and 31.1% (Random Forest) | Helped |
| Hyper-parameter setting kappa=1, S0=I_m (chosen by quality checks) versus kappa=0.1, S0=0.1I_m (ablation) | HDP-HMM segmentation · single patient example | segment quality (sufficient samples, statistical uniqueness) | Helped |
| Algorithm 1 timing of detection | clinical state estimation · ICU admission or discharge detection | detection lead time: approximately 24 hours prior to clinician decision | Helped |
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