(untitled) (The Science Post, 2025)
The vast majority of critical patient information is stored within unstructured clinical notes in Electronic Health Records (EHRs), making it inaccessible to traditional data analysis methods. This paper explores the application of Natural Language Processing (NLP) to automatically extract and structure this information for enhanced patient phenotyping and cohort identification. Manual chart review is time-consuming, expensive, and prone to human error, creating a significant bottleneck for clinical research and quality improvement initiatives. This research details the development of an NLP pipeline utilizing both rule-based and deep learning models to identify patients with specific conditions, such as heart failure with preserved ejection fraction (HFpEF), from radiology and cardiology reports. The results demonstrate that NLP systems can achieve high accuracy in classifying clinical concepts, significantly accelerating the process of cohort building and enabling large-scale retrospective studies that were previously infeasible. The discussion addresses challenges related to model portability, linguistic complexity, and the imperative of integrating domain expertise into the NLP development process.