Natural Language Processing of Electronic Health Records for Predicting Alzheimer's Disease

Abstract

This chapter explores the use of Natural Language Processing (NLP) to analyze Electronic Health Records (EHRs) for early prediction of Alzheimer's disease. It delves into NLP techniques that enhance diagnostic accuracy by extracting insights from unstructured data within EHRs. The potential of NLP to revolutionize early detection and improve patient outcomes through precise, real-time data analysis is highlighted, emphasizing advancements in healthcare technology. The integration of NLP with EHR systems promises to advance personalized medicine, allowing for earlier interventions that can significantly alter the course of Alzheimer's disease. By enabling a deeper understanding of nuanced patient data, NLP fosters a proactive approach to healthcare that prioritizes prevention and precise treatment strategies.