Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generation

• URL: http://arxiv.org/abs/2502.21107v1
• Date: Fri, 28 Feb 2025 14:46:02 GMT
• Title: Generating patient cohorts from electronic health records using two-step retrieval-augmented text-to-SQL generation
• Authors: Angelo Ziletti, Leonardo D'Ambrosi,
• Abstract summary: The system achieves 0.75 F1-score in cohort identification on EHR data, effectively capturing complex temporal and logical relationships.<n>These results demonstrate the feasibility of automated cohort generation for epidemiological research.
• Score: 0.6138671548064356
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Clinical cohort definition is crucial for patient recruitment and observational studies, yet translating inclusion/exclusion criteria into SQL queries remains challenging and manual. We present an automated system utilizing large language models that combines criteria parsing, two-level retrieval augmented generation with specialized knowledge bases, medical concept standardization, and SQL generation to retrieve patient cohorts with patient funnels. The system achieves 0.75 F1-score in cohort identification on EHR data, effectively capturing complex temporal and logical relationships. These results demonstrate the feasibility of automated cohort generation for epidemiological research.

Related papers

• ACR: A Benchmark for Automatic Cohort Retrieval [1.3547712404175771]
  Current cohort retrieval methods rely on automated queries of structured data combined with manual curation. Recent advancements in large language models (LLMs) and information retrieval (IR) offer promising avenues to revolutionize these systems. This paper introduces a new task, Automatic Cohort Retrieval (ACR), and evaluates the performance of LLMs and commercial, domain-specific neuro-symbolic approaches.
  arXiv  Detail & Related papers  (2024-06-20T23:04:06Z)
• Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health records [0.6138671548064356]
  We introduce an end-to-end methodology that combines text-to-generation with retrieval augmented generation (RAG) to answer epidemiological questions. RAG offers a promising direction for improving their capabilities, as shown in a realistic industry setting.
  arXiv  Detail & Related papers  (2024-03-14T09:45:05Z)
• TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic Tree-Based Memory Network [54.332862955411656]
  Clinical trials are critical for drug development but often suffer from expensive and inefficient patient recruitment. In recent years, machine learning models have been proposed for speeding up patient recruitment via automatically matching patients with clinical trials. We introduce a dynamic tree-based memory network model named TREEMENT to provide accurate and interpretable patient trial matching.
  arXiv  Detail & Related papers  (2023-07-19T12:35:09Z)
• Toward Cohort Intelligence: A Universal Cohort Representation Learning Framework for Electronic Health Record Analysis [15.137213823470544]
  We propose a universal COhort Representation lEarning (CORE) framework to augment EHR utilization by leveraging the fine-grained cohort information among patients. CORE is readily applicable to diverse backbone models, serving as a universal plug-in framework to infuse cohort information into healthcare methods for boosted performance.
  arXiv  Detail & Related papers  (2023-04-10T09:12:37Z)
• Development and validation of a natural language processing algorithm to pseudonymize documents in the context of a clinical data warehouse [53.797797404164946]
  The study highlights the difficulties faced in sharing tools and resources in this domain. We annotated a corpus of clinical documents according to 12 types of identifying entities. We build a hybrid system, merging the results of a deep learning model as well as manual rules.
  arXiv  Detail & Related papers  (2023-03-23T17:17:46Z)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)
• COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching [70.08786840301435]
  We propose CrOss-Modal PseudO-SiamEse network (COMPOSE) to address these challenges for patient-trial matching. Experiment results show COMPOSE can reach 98.0% AUC on patient-criteria matching and 83.7% accuracy on patient-trial matching.
  arXiv  Detail & Related papers  (2020-06-15T21:01:33Z)
• Temporal Phenotyping using Deep Predictive Clustering of Disease Progression [97.88605060346455]
  We develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest. Experiments on two real-world datasets show that our model achieves superior clustering performance over state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2020-06-15T20:48:43Z)
• Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale [0.5498849973527224]
  We present an unsupervised framework based on deep learning to process heterogeneous EHRs. We derive patient representations that can efficiently and effectively enable patient stratification at scale.
  arXiv  Detail & Related papers  (2020-03-14T00:04:20Z)
• DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction [67.91606509226132]
  Clinical trials are essential for drug development but often suffer from expensive, inaccurate and insufficient patient recruitment. DeepEnroll is a cross-modal inference learning model to jointly encode enrollment criteria (tabular data) into a shared latent space for matching inference.
  arXiv  Detail & Related papers  (2020-01-22T17:51:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.