An NLP-based cognitive system for disease status identification in electronic health records

This paper presents a natural language processing (NLP) based cognitive decision support system that automatically identifies the status of a disease from the clinical notes of a patient record. The system relies on IBM Watson Patient Record NLP analytics and supervised or semi-supervised learning techniques. It uses unstructured text in clinical notes, data from the structured part of a patient record, and disease control targets from the clinical guidelines. We evaluated the system using de-identified patient records of 414 hypertensive patients from a multi-specialty hospital system in the U.S. The experimental results show that, using supervised learning methods, our system can achieve an average 0.86 F1-score in identifying disease status passages and average accuracy of 0.77 in classifying the status as controlled or not. To the best of our knowledge, this is the first system to automatically identify disease control status from clinical notes.