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

Key Points

• The aim is to evaluate the accuracy, bias, and generalizability of NLP models applied to EHRs.
• Analyzed existing clinical NLP models in relation to electronic health records
• Assessed issues of accuracy, bias, and external validity
• Evaluated model performance across diverse patient groups and institutions
• Identified strategies for model fine-tuning to enhance fairness
• Many NLP models exhibit significant bias impacting accuracy
• External validity varies widely among different model applications
• Fine-tuning models can improve fairness and decision-making support in clinical settings