Machine learning models with time-series clinical features to predict radiographic progression in patients with ankylosing spondylitis
- Author:
Bon San KOO
1
;
Miso JANG
;
Ji Seon OH
;
Keewon SHIN
;
Seunghun LEE
;
Kyung Bin JOO
;
Namkug KIM
;
Tae-Hwan KIM
Author Information
- Publication Type:Original Article
- From:Journal of Rheumatic Diseases 2024;31(2):97-107
- CountryRepublic of Korea
- Language:EN
-
Abstract:
Objective:Ankylosing spondylitis (AS) is chronic inflammatory arthritis causing structural damage and radiographic progression to the spine due to repeated and continuous inflammation over a long period. This study establishes the application of machine learning models to predict radiographic progression in AS patients using time-series data from electronic medical records (EMRs).
Methods:EMR data, including baseline characteristics, laboratory findings, drug administration, and modified Stoke AS Spine Score (mSASSS), were collected from 1,123 AS patients between January 2001 and December 2018 at a single 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 . We used three machine learning methods (logistic regression with the least absolute shrinkage and selection operation, random forest, and extreme gradient boosting algorithms) with three-fold cross-validation.
Results:The random forest model using the T1 EMR dataset best predicted the radiographic progression P2 among the machine learning models tested with a mean accuracy and area under the curves of 73.73% and 0.79, respectively. Among the T1 variables, 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 when predicting radiographic progression.
