Conformal prediction enables disease course prediction and allows individualized diagnostic uncertainty in multiple sclerosis

Abstract

Accurate assessment of progression and disease course in multiple sclerosis (MS) is vital for timely and appropriate clinical intervention. The gradual transition from relapsing-remitting MS (RRMS) to secondary progressive MS (SPMS) is often diagnosed retrospectively with a typical delay of three years. To address this diagnostic delay, we developed a predictive model that uses electronic health records to distinguish between RRMS and SPMS at each individual visit. To enable reliable predictions, conformal prediction was implemented at the individual patient level with a confidence of 93%. Our model accurately predicted the change in diagnosis from RRMS to SPMS for patients who transitioned during the study period. Additionally, we identified new patients who, with high probability, are in the transition phase but have not yet received a clinical diagnosis. Our methodology aids in monitoring MS progression and proactively identifying transitioning patients. An anonymized model is available at https://msp-tracker.serve.scilifelab.se/.