Deciphering Medical Reports with Natural Language Processing for Cancer Detection

Abstract

The increasing penetration of electronic medical records (EMR) and digital clinical documentation irrespective of the healthcare setting preserves a persistent challenge of mining useful data from unstructured healthcare data. Disease detection, an indispensable part of the medical data analysis needs, mandates an advanced implementation of natural lan- guage processing (NLP) techniques to automate the understand- ing of textual information disseminated in clinical reports. This paper presents a thorough review of current NLP methods for disease detection, focusing on text classification models, including rule-based, machine-learning, and deep-learning approaches like BERT and CNNs. By exploring these techniques in the context of disease identification, we emphasize advancements that enhance diagnostic accuracy, speed, and support for healthcare decisions. Additionally, we discuss significant challenges such as managing complex medical terminology, addressing data sparsity, and ensuring interpretability in predictive models that affect the implementation of NLP in real-world healthcare settings. This will give the reader more information regarding the strengths and weaknesses of state-of-the-art models in assisting the production of practical, scalable solutions to clinical NLP tasks