CohortNet: Empowering Cohort Discovery for Interpretable Healthcare Analytics
Summary: CohortNet auto-discovers interpretable patient cohorts by learning per-feature temporal embeddings, adaptively discretizing feature states via K-Means and exploring cohort patterns heuristically. Produces evidence-backed cohort representations for retrieval-driven, interpretable predictions with 2.8–4.1% AUC-PR gains. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Qingpeng Cai
- 2. Kaiping Zheng
- 3. Beng Chin Ooi
- 4. H. V. Jagadish
- 5. James Yip
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Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,170 | Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence | 2021 | SIGMOD | 7.4517805e-05 |
| 6,014 | COVIZ: A System for Visual Formation and Exploration of Patient Cohorts | 2019 | VLDB | 5.2365238e-05 |
| 8,189 | Cohort Query Processing | 2017 | VLDB | 4.5598377e-05 |
| 8,386 | PACE: Learning Effective Task Decomposition for Human-in-the-loop Healthcare Delivery | 2021 | SIGMOD | 4.525798e-05 |
| 11,598 | TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications | 2020 | SIGMOD | 4.1905499e-05 |
| 13,232 | DyHealth: Making Neural Networks Dynamic for Effective Healthcare Analytics | 2022 | VLDB | - |
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