# CohortNet: Empowering Cohort Discovery for Interpretable Healthcare Analytics

arXiv: https://arxiv.org/abs/2406.14015 · HTML: https://mlsanity.com/paper/2406.14015/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 55 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism](https://mlsanity.com/paper/1608.05745/) (2016)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare](https://mlsanity.com/paper/1810.09593/) (2018)
- [ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context](https://mlsanity.com/paper/1911.12216/) (2019)
- [StageNet: Stage-Aware Neural Networks for Health Risk Prediction](https://mlsanity.com/paper/2001.10054/) (2020)
- [Benchmarking machine learning models on multi-centre eICU critical care dataset](https://mlsanity.com/paper/1910.00964/) (2019)
- [Database Meets Deep Learning: Challenges and Opportunities](https://mlsanity.com/paper/1906.08986/) (2019)
- [Retrieval & Interaction Machine for Tabular Data Prediction](https://mlsanity.com/paper/2108.05252/) (2021)
- [TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications](https://mlsanity.com/paper/2003.12012/) (2020)
- [Predict and Interpret Health Risk using EHR through Typical Patients](https://mlsanity.com/paper/2312.10977/) (2023)
- [Cohort Query Processing](https://mlsanity.com/paper/1601.00182/) (2016)
- [RuDi: Explaining Behavior Sequence Models by Automatic Statistics Generation and Rule Distillation](https://mlsanity.com/paper/2208.07211/) (2022)

## Built on by (most cited first, 3 of 3)

- [C2TC: A Training-Free Framework for Efficient Tabular Data Condensation](https://mlsanity.com/paper/2602.21717/) (2026)
- [Health+: Empowering Individuals via Unifying Health Data](https://mlsanity.com/paper/2602.19319/) (2026)
- [Cohort-Anchored Foundation Models for Electronic Health Records: From Risk Scores to Auditable Peer Cohorts](https://mlsanity.com/paper/2606.21885/) (2026)

## Abstract (arXiv)

Cohort studies are of significant importance in the field of healthcare analysis. However, existing methods typically involve manual, labor-intensive, and expert-driven pattern definitions or rely on simplistic clustering techniques that lack medical relevance. Automating cohort studies with interpretable patterns has great potential to facilitate healthcare analysis but remains an unmet need in prior research efforts. In this paper, we propose a cohort auto-discovery model, CohortNet, for interpretable healthcare analysis, focusing on the effective identification, representation, and exploitation of cohorts characterized by medically meaningful patterns. CohortNet initially learns fine-grained patient representations by separately processing each feature, considering both individual feature trends and feature interactions at each time step. Subsequently, it classifies each feature into distinct states and employs a heuristic cohort exploration strategy to effectively discover substantial cohorts with concrete patterns. For each identified cohort, it learns comprehensive cohort representations with credible evidence through associated patient retrieval. Ultimately, given a new patient, CohortNet can leverage relevant cohorts with distinguished importance, which can provide a more holistic understanding of the patient's conditions. Extensive experiments on three real-world datasets demonstrate that it consistently outperforms state-of-the-art approaches and offers interpretable insights from diverse perspectives in a top-down fashion.