JMIR Publications

ABSTRACT

Background:

The ability to predict rheumatoid arthritis (RA) flares between clinic visits based on real-time, longitudinal patient-generated data could potentially allow for timely interventions to avoid disease worsening.

Objective:

In this exploratory study, we aimed to investigate the feasibility of using machine learning methods to classify self-reported RA flares based on a small dataset of daily symptom data collected on a smartphone app.

Methods:

We used daily symptoms and weekly flares reported on the REMORA smartphone app from 20 RA patients over three months. Predictors were several summary features of the daily symptom scores collected in the week leading up to the flare question. We fitted three binary classifiers: logistic regression +/- elastic net regularization, a random forest and naïve Bayes. Performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic curve.

Results:

The data comprised an average of 60.6 daily reports and 10.5 weekly reports per participant. AUCs were broadly similar between models, but logistic regression with elastic net had the highest AUC of 0.82. At a cut-off requiring specificity to be 0.80, the corresponding sensitivity to detect flares was 0.60 for this model. The positive predictive value in this population was 53%, and negative predictive value 85%.

Conclusions:

Predicting self-reported flares based on daily symptom scorings in the preceding week using machine learning methods was feasible. The observed predictive accuracy might improve as we obtain more data, and these exploratory results should be validated in an external cohort. In the future, analysis of frequently collected patient-generated data may allow us to predict flares before they unfold, opening opportunities for just-in-time interventions.