Assessing Clinical Natural Language Processing (NLP) Models for Interpreting Electronic Health Records (EHR): Focus on Accuracy, Bias, and Generalizability

The paper explores the application of Natural Language Processing (NLP) in Electronic Health Records (EHRs) analysis. It assesses the accuracy, bias, and external validity of clinical NLP models, where the external validity is synonymous with the clinical decision-making support ability. The study outlines the major issues in enhancing the fairness and performance of models in the most varied patient groups and institutions. The solution to these issues lies in fitting models with fine-tuning and avoiding bias to achieve fairer healthcare