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 (National University of Singapore)
- 2. Kaiping Zheng (National University of Singapore)
- 3. Beng Chin Ooi (National University of Singapore)
- 4. H. V. Jagadish (University of Michigan)
- 5. James Yip (National University Health System)
BibTeX Citation
@article{cai_vldb24,
title = {{CohortNet: Empowering Cohort Discovery for Interpretable Healthcare Analytics}},
author = {Cai, Qingpeng and Zheng, Kaiping and Ooi, Beng Chin and Jagadish, H. V. and Yip, James},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {10},
pages = {2487--2500},
doi = {10.14778/3675034.3675041},
url = {https://doi.org/10.14778/3675034.3675041},
year = {2024}
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Outgoing Citations (Sorted by Pagerank)
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 |
|---|---|---|---|---|
| 3,190 | Looking for Trouble: Analyzing Classifier Behavior via Pattern Divergence | 2021 | SIGMOD | 7.6532441e-05 |
| 6,966 | COVIZ: A System for Visual Formation and Exploration of Patient Cohorts | 2019 | VLDB | 5.7303405e-05 |
| 8,176 | Cohort Query Processing | 2017 | VLDB | 5.4736785e-05 |
| 8,352 | PACE: Learning Effective Task Decomposition for Human-in-the-loop Healthcare Delivery | 2021 | SIGMOD | 5.4453394e-05 |
| 11,789 | TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications | 2020 | SIGMOD | 5.093636e-05 |
| 13,421 | DyHealth: Making Neural Networks Dynamic for Effective Healthcare Analytics | 2022 | VLDB | - |
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