# Cohort Retrieval using Dense Passage Retrieval

arXiv: https://arxiv.org/abs/2507.01049 · HTML: https://mlsanity.com/paper/2507.01049/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 26 Jun 2025.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Efficient Natural Language Response Suggestion for Smart Reply](https://mlsanity.com/paper/1705.00652/) (2017)
- [Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents](https://mlsanity.com/paper/2310.19923/) (2023)
- [Improving Biomedical Information Retrieval with Neural Retrievers](https://mlsanity.com/paper/2201.07745/) (2022)
- [Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations](https://mlsanity.com/paper/2010.03550/) (2020)
- [Effective Matching of Patients to Clinical Trials using Entity Extraction and Neural Re-ranking](https://mlsanity.com/paper/2307.00381/) (2023)
- [Patient Cohort Retrieval using Transformer Language Models](https://mlsanity.com/paper/2009.05121/) (2020)
- [Generalist embedding models are better at short-context clinical semantic search than specialized embedding models](https://mlsanity.com/paper/2401.01943/) (2024)
- [Attention-Based LSTM Network for COVID-19 Clinical Trial Parsing](https://mlsanity.com/paper/2012.10063/) (2020)
- [CREATE: Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records using OMOP Common Data Model](https://mlsanity.com/paper/1901.07601/) (2019)
- [Top K Relevant Passage Retrieval for Biomedical Question Answering](https://mlsanity.com/paper/2308.04028/) (2023)
- [Numbers Matter! Bringing Quantity-awareness to Retrieval Systems](https://mlsanity.com/paper/2407.10283/) (2024)

## Abstract (arXiv)

Patient cohort retrieval is a pivotal task in medical research and clinical practice, enabling the identification of specific patient groups from extensive electronic health records (EHRs). In this work, we address the challenge of cohort retrieval in the echocardiography domain by applying Dense Passage Retrieval (DPR), a prominent methodology in semantic search. We propose a systematic approach to transform an echocardiographic EHR dataset of unstructured nature into a Query-Passage dataset, framing the problem as a Cohort Retrieval task. Additionally, we design and implement evaluation metrics inspired by real-world clinical scenarios to rigorously test the models across diverse retrieval tasks. Furthermore, we present a custom-trained DPR embedding model that demonstrates superior performance compared to traditional and off-the-shelf SOTA methods.To our knowledge, this is the first work to apply DPR for patient cohort retrieval in the echocardiography domain, establishing a framework that can be adapted to other medical domains.