Natural Language Processing for Automated Annotation of Clinical Notes: Enhancing Phenotyping and Cohort Identification (The Science Post, 2025)
The vast majority of critical patient information is stored within unstructured clinical notes in Electronic HealthRecords (EHRs), making it inaccessible to traditional data analysis methods. This paper explores the application ofNatural Language Processing (NLP) to automatically extract and structure this information for enhanced patientphenotyping 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 thedevelopment of an NLP pipeline utilizing both rule-based and deep learning models to identify patients with specificconditions, such as heart failure with preserved ejection fraction (HFpEF), from radiology and cardiology reports. Theresults demonstrate that NLP systems can achieve high accuracy in classifying clinical concepts, significantlyaccelerating the process of cohort building and enabling large-scale retrospective studies that were previouslyinfeasible. The discussion addresses challenges related to model portability, linguistic complexity, and the imperativeof integrating domain expertise into the NLP development process.