Machine learning models with time-series clinical features to predict radiographic progression in patients with ankylosing spondylitis

Abstract

Background Ankylosing spondylitis is chronic inflammatory arthritis that causes structural damage to the spine due to repeated and continuous inflammation over a long period of time. The purpose of this study was to establish the application of machine learning models for predicting radiographic progression in patients with AS using time-series data from electronic medical records (EMRs). Methods EMR data, including baseline characteristics, laboratory finding, drug administration, and modified Stoke Ankylosing Spondylitis Spine Score (mSASSS), were collected from 1,123 AS patients who were followed up for 18 years at a common center at the time of first (T1), second (T2), and third (T3) visits. The radiographic progression of the (n + 1)th visit (Pn+1 = (mSASSSn+1 – mSASSSn) / (Tn+1 – Tn) ≥ 1 unit per year) was predicted using follow-up visit datasets from T1 to Tn. Three machine learning methods (logistic regression with least absolute shrinkage and selection operation, random forest, and extreme gradient boosting algorithms) with three-fold cross-validation were used. Results The random forest model using the T1 EMR dataset showed the highest performance in predicting the radioactive progression P2 among all the machine learning models tested. The mean accuracy and the area under the curves were 73.73% and 0.79, respectively. Among the variables of T1, the most important variables for predicting radiographic progression were in the order of total mSASSS, age, and alkaline phosphatase. Conclusion Prognosis predictive models using time-series data showed reasonable performance with clinical features of the first visit dataset for predicting radiographic progression. Additional feature data such as spine radiographs or life-log data may improve the performance of these models.