Explainable time-to-progression predictions in multiple sclerosis.

Background: Prognostic machine learning research in multiple sclerosis has been mainly focusing on black-box models predicting whether a patients' disability will progress in a fixed number of years. However, as this is a binary yes/no question, it cannot take individual disease severity into account. Therefore, in this work we propose to model the time to disease progression instead.

Factor Retention in Exploratory Multidimensional Item Response Theory.

Multidimensional Item Response Theory (MIRT) is applied routinely in developing educational and psychological assessment tools, for instance, for exploring multidimensional structures of items using exploratory MIRT. A critical decision in exploratory MIRT analyses is the number of factors to retain. Unfortunately, the comparative properties of statistical methods and innovative Machine Learning (ML) methods for factor retention in exploratory MIRT analyses are still not clear.

CHIT1 at diagnosis predicts faster disability progression and reflects early microglial activation in multiple sclerosis.

Multiple sclerosis (MS) is characterized by heterogeneity in disease course and prediction of long-term outcome remains a major challenge. Here, we investigate five myeloid markers - CHIT1, CHI3L1, sTREM2, GPNMB and CCL18 - in the cerebrospinal fluid (CSF) at diagnostic lumbar puncture in a longitudinal cohort of 192 MS patients. Through mixed-effects and machine learning models, we show that CHIT1 is a robust predictor for faster disability progression.