JMIR Publications

ABSTRACT

Background:

The Expanded Disability Status Scale (EDSS) score is a widely used measure to monitor disability progression in people with multiple sclerosis (MS). However, extracting and deriving the EDSS from unstructured electronic health records can be time-consuming.

Objective:

To compare rule-based and deep learning natural language processing algorithms for detecting and predicting the total EDSS and EDSS functional system subscores from the electronic health records of patients with MS.

Methods:

We studied 17,452 electronic health records of 4,906 MS patients followed at one of Canada’s largest MS clinics between June 2015 and July 2019. We randomly divided the records into training (80%) and test (20%) data sets and compared the performance characteristics of three natural language processing models. First, we applied a rule-based approach, extracting the EDSS score from sentences containing the keyword ‘EDSS’. Next, we trained a Convolutional Neural Network (CNN) model to predict the nineteen half-step increments of the EDSS score. Finally, we used a combined rule-based-CNN model. For each approach, we determined the accuracy, precision, recall, and F-score compared to the reference standard, which were the manually labelled EDSS scores in the clinic database.

Results:

Overall, the combined keyword-CNN model demonstrated the best performance, with accuracy, precision, recall and F-score of 0.90, 0.83, 0.83, and 0.83 respectively. Respective figures for the rule-based and CNN models individually were 0.57, 0.91, 0.65, 0.70 and 0.86, 0.70, 0.70, 0.70. Because of missing data, model performance for EDSS sub-scores was lower than that for the total EDSS score. Performance improved when considering notes with known values of the EDSS subscores.

Conclusions:

A combined keyword/CNN natural language processing model can extract and accurately predict EDSS scores from patient records. This approach can be automated for efficient information extraction in clinical and research settings.