Natural Language Processing (NLP) in the Extraction of Clinical Information from Electronic Health Records (EHRs) for Cancer Prognosis

Abstract

NLP has become an important tool in healthcare, particularly in extracting clinical information from EHRs in order to help enhance cancer prognosis. EHRs store vast amounts of structured and unstructured data, offering tremendous potential for improvement in patient outcomes through the delivery of critical insights into the conditions of patients, their responses to treatment, and possible prognostic outcomes. Nevertheless, meaningful information extracted from these huge amounts of unstructured data, like clinical notes, is still hard to gain. The current review thus shows the developments in NLP techniques that aim to extract and analyze clinical data from EHRs, focusing on cancer prognosis, and also showcases some progress in NLP over the last decade, including various methods like named entity recognition, sentiment analysis, and text classification. Some of the limitations and challenges of current models elaborated on in the paper concern variability in clinical language and high-quality annotated data. Finally, it proposes further improvements and future directions for NLP-based approaches toward more accurate, more individualized cancer prognosis and therefore highlights further research and development as needed in this area of rapid growth.