Advancements in NLP for Clinical Data Extraction from Electronic Health Records

Electronic Health Records (EHRs) store vast amounts of clinical data in both structured and unstructured formats. Extracting meaningful clinical insights from EHRs is essential for improving patient care, enabling precision medicine, and supporting clinical research. Natural Language Processing (NLP) models have shown significant promise in automating the extraction of clinical information from unstructured text in EHRs. This paper reviews recent advancements in NLP techniques, including rule-based methods, machine learning, and deep learning approaches, to enhance clinical data extraction. We discuss challenges such as data heterogeneity, privacy concerns, and model interpretability and explore potential solutions. The paper also highlights emerging trends, such as self-supervised learning and multimodal integration, that are shaping the future of clinical NLP.