Abstract
SIGNIFICANCE: Analyzing narratives in patients' medical records using a framework that combines natural language processing (NLP) and machine learning may help uncover the underlying patterns of patients' visual capabilities and challenges that they are facing and could be useful in analyzing big data in optometric research. PURPOSE: The primary goal of this study was to demonstrate the feasibility of applying a framework that combines NLP and machine learning to analyze narratives in patients' medical records. To test and validate our framework, we applied it to analyze records of low vision patients and to address two questions: Was there association between patients' narratives related to activities of daily living and the quality of their vision? Was there association between patients' narratives related to activities of daily living and their sentiments toward certain "assistive items"? METHODS: Our dataset consisted of 616 records of low vision patients. From patients' complaint history, we selected multiple keywords that were related to common activities of daily living. Sentences related to each keyword were converted to numerical data using NLP techniques. Machine learning was then applied to classify the narratives related to each keyword into two categories, labeled based on different "factors of interest" (acuity, contrast sensitivity, and sentiments of patients toward certain "assistive items"). RESULTS: Using our proposed framework, when patients' narratives related to specific keywords were used as input, our model effectively predicted the categories of different factors of interest with promising performance. For example, we found strong associations between patients' narratives and their acuity or contrast sensitivity for certain activities of daily living (e.g., "drive" in association with acuity and contrast sensitivity). CONCLUSIONS: Despite our limited dataset, our results show that the proposed framework was able to extract the semantic patterns stored in medical narratives and to predict patients' sentiments and quality of vision.