# A survey of using EHR as real-world evidence for discovering and validating new drug indications

arXiv: https://arxiv.org/abs/2505.24767 · HTML: https://mlsanity.com/paper/2505.24767/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 147 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 16 of 16)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022)
- [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](https://mlsanity.com/paper/2402.07927/) (2024)
- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [Large Language Models are Few-Shot Clinical Information Extractors](https://mlsanity.com/paper/2205.12689/) (2022)
- [Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis](https://mlsanity.com/paper/1706.03446/) (2017)
- [Learning to Diagnose with LSTM Recurrent Neural Networks](https://mlsanity.com/paper/1511.03677/) (2015)
- [Do We Still Need Clinical Language Models?](https://mlsanity.com/paper/2302.08091/) (2023)
- [Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit](https://mlsanity.com/paper/2010.01165/) (2020)
- [Large Language Models to Identify Social Determinants of Health in Electronic Health Records](https://mlsanity.com/paper/2308.06354/) (2023)
- [Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning](https://mlsanity.com/paper/2304.02711/) (2023)
- [Bias and Fairness in Chatbots: An Overview](https://mlsanity.com/paper/2309.08836/) (2023)
- [Natural Language Processing for EHR-Based Computational Phenotyping](https://mlsanity.com/paper/1806.04820/) (2018)
- [Enhancing Phenotype Recognition in Clinical Notes Using Large Language Models: PhenoBCBERT and PhenoGPT](https://mlsanity.com/paper/2308.06294/) (2023)
- [Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence](https://mlsanity.com/paper/2009.10931/) (2020)

## Abstract (arXiv)

Electronic Health Records (EHRs) have been increasingly used as real-world evidence (RWE) to support the discovery and validation of new drug indications. This paper surveys current approaches to EHR-based drug repurposing, covering data sources, processing methodologies, and representation techniques. It discusses study designs and statistical frameworks for evaluating drug efficacy. Key challenges in validation are discussed, with emphasis on the role of large language models (LLMs) and target trial emulation. By synthesizing recent developments and methodological advances, this work provides a foundational resource for researchers aiming to translate real-world data into actionable drug-repurposing evidence.