# Patient Cohort Retrieval using Transformer Language Models

arXiv: https://arxiv.org/abs/2009.05121 · HTML: https://mlsanity.com/paper/2009.05121/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [LeafAI: query generator for clinical cohort discovery rivaling a human programmer](https://mlsanity.com/paper/2304.06203/) (2023)
- [CliniQ: A Multi-faceted Benchmark for Electronic Health Record Retrieval with Semantic Match Assessment](https://mlsanity.com/paper/2502.06252/) (2025)
- [ACR: A Benchmark for Automatic Cohort Retrieval](https://mlsanity.com/paper/2406.14780/) (2024)
- [Automating Early Disease Prediction Via Structured and Unstructured Clinical Data](https://mlsanity.com/paper/2603.28167/) (2026)
- [Application of Transformers based methods in Electronic Medical Records: A Systematic Literature Review](https://mlsanity.com/paper/2304.02768/) (2023)
- [Cohort Retrieval using Dense Passage Retrieval](https://mlsanity.com/paper/2507.01049/) (2025)
- [DR.EHR: Dense Retrieval for Electronic Health Record with Knowledge Injection and Synthetic Data](https://mlsanity.com/paper/2507.18583/) (2025)

## Abstract (arXiv)

We apply deep learning-based language models to the task of patient cohort retrieval (CR) with the aim to assess their efficacy. The task of CR requires the extraction of relevant documents from the electronic health records (EHRs) on the basis of a given query. Given the recent advancements in the field of document retrieval, we map the task of CR to a document retrieval task and apply various deep neural models implemented for the general domain tasks. In this paper, we propose a framework for retrieving patient cohorts using neural language models without the need of explicit feature engineering and domain expertise. We find that a majority of our models outperform the BM25 baseline method on various evaluation metrics.