Research map / Probabilistic and scientific modeling / Molecules and proteins
Learning Phenotypes and Dynamic Patient Representations via RNN Regularized Collective Non-Negative Tensor Factorization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| build per-patient temporal tensors (CNTF) instead of one tensor accumulated over the stay (Rubik) | computational phenotyping · MIMIC-III phenotypes | sparsity: 0.79 -> 0.96 | Helped |
| CNTF instead of Rubik | computational phenotyping · MIMIC-III phenotypes | similarity (lower is more distinct): 0.90 -> 0.43 | Helped |
| add RNN-based temporal regularization to CNTF (ablation) | CNTF · MIMIC-III early mortality prediction | AUROC | Helped |
| use CNTF with RNN regularization instead of Rubik/CP-APR features | mortality predictor · MIMIC-III mortality, day 3 | AUROC: 0.70 on day 3, reached by baselines only after five days | Helped |
| use CNTF instead of baselines at discharge | mortality predictor · MIMIC-III mortality, discharge | AUROC: 0.85 for all models | No clear effect |
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