A survey of using EHR as real-world evidence for discovering and validating new drug indications
- URL: http://arxiv.org/abs/2505.24767v1
- Date: Fri, 30 May 2025 16:30:54 GMT
- Title: A survey of using EHR as real-world evidence for discovering and validating new drug indications
- Authors: Nabasmita Talukdar, Xiaodan Zhang, Shreya Paithankar, Hui Wang, Bin Chen,
- Abstract summary: Electronic Health Records have been increasingly used as real-world evidence to support the discovery and validation of new drug indications.<n>This paper surveys current approaches to EHR-based drug repurposing, covering data sources, processing methodologies, and representation techniques.
- Score: 9.095974173273028
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials [49.19897427783105]
The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift.
We investigate how these advanced computational models can uncover target-disease linkage, interpret complex biomedical data, enhance drug molecule design, predict drug efficacy and safety profiles, and facilitate clinical trial processes.
arXiv Detail & Related papers (2024-09-06T02:03:38Z) - DrugAgent: Multi-Agent Large Language Model-Based Reasoning for Drug-Target Interaction Prediction [8.98329812378801]
DrugAgent is a multi-agent system for drug-target interaction prediction.<n>It combines multiple specialized perspectives with transparent reasoning.<n>Our approach provides detailed, human-interpretable reasoning for each prediction.
arXiv Detail & Related papers (2024-08-23T21:24:59Z) - Explainable Biomedical Hypothesis Generation via Retrieval Augmented Generation enabled Large Language Models [46.05020842978823]
Large Language Models (LLMs) have emerged as powerful tools to navigate this complex data landscape.
RAGGED is a comprehensive workflow designed to support investigators with knowledge integration and hypothesis generation.
arXiv Detail & Related papers (2024-07-17T07:44:18Z) - Recent Advances in Predictive Modeling with Electronic Health Records [71.19967863320647]
utilizing EHR data for predictive modeling presents several challenges due to its unique characteristics.
Deep learning has demonstrated its superiority in various applications, including healthcare.
arXiv Detail & Related papers (2024-02-02T00:31:01Z) - Zero-shot Learning of Drug Response Prediction for Preclinical Drug
Screening [38.94493676651818]
We propose a zero-shot learning solution for the.
task in preclinical drug screening.
Specifically, we propose a Multi-branch Multi-Source Domain Adaptation Test Enhancement Plug-in, called MSDA.
arXiv Detail & Related papers (2023-10-05T05:55:41Z) - EHRDiff: Exploring Realistic EHR Synthesis with Diffusion Models [8.799590232822752]
Privacy concerns have resulted in limited access to high-quality and large-scale EHR data for researchers.
Recent research has delved into synthesizing realistic EHR data through generative modeling techniques.
In this study, we investigate the potential of diffusion models for EHR data synthesis and introduce a novel method, EHRDiff.
arXiv Detail & Related papers (2023-03-10T02:15:58Z) - SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity
Prediction [127.43571146741984]
Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery.
wet experiments remain the most reliable method, but they are time-consuming and resource-intensive.
Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue.
We present the SSM-DTA framework, which incorporates three simple yet highly effective strategies.
arXiv Detail & Related papers (2022-06-20T14:53:25Z) - Deep learning for drug repurposing: methods, databases, and applications [54.08583498324774]
Repurposing existing drugs for new therapies is an attractive solution that accelerates drug development at reduced experimental costs.
In this review, we introduce guidelines on how to utilize deep learning methodologies and tools for drug repurposing.
arXiv Detail & Related papers (2022-02-08T09:42:08Z) - DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for
AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise
Annotations [90.27736364704108]
We present DrugOOD, a systematic OOD dataset curator and benchmark for AI-aided drug discovery.
DrugOOD comes with an open-source Python package that fully automates benchmarking processes.
We focus on one of the most crucial problems in AIDD: drug target binding affinity prediction.
arXiv Detail & Related papers (2022-01-24T12:32:48Z) - Heterogeneous Treatment Effect Estimation using machine learning for
Healthcare application: tutorial and benchmark [8.869515663374248]
Many studies have shown that drugs effects are heterogeneous among the population.
Lots of advanced machine learning models about estimating heterogeneous treatment effects (HTE) have emerged in recent years.
We aim to introduce the HTE methodology to the healthcare area and provide feasibility consideration.
arXiv Detail & Related papers (2021-09-27T02:34:44Z) - Applications of artificial intelligence in drug development using
real-world data [3.692950272002333]
The FDA has been actively promoting the use of real-world data (RWD) in drug development.
RWD can generate important real-world evidence reflecting the real-world clinical environment where the treatments are used.
Machine- and deep-learning (ML/DL) methods have been increasingly used across many stages of the drug development process.
arXiv Detail & Related papers (2021-01-22T01:13:54Z) - When deep learning meets causal inference: a computational framework for
drug repurposing from real-world data [12.68717103979673]
Existing methods for drug repurposing may exist translational issues when applied to human beings.
We present an efficient and easily-customized framework for generating and testing multiple candidates for drug repurposing.
We demonstrate our framework in a case study of coronary artery disease (CAD) by evaluating the effect of 55 repurposing drug candidates on various disease outcomes.
arXiv Detail & Related papers (2020-07-16T21:30:56Z)
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.