Air pollution is a known risk factor for the exacerbation of many diseases. Among these, is multiple sclerosis (MS), a chronic, autoimmune, neurological disease, characterised by transient episodes of neurological impairment known as relapses. Although the link between environmental factors and relapses has been a subject of investigation in the medical and biostatistical literature, its implications for predictive modelling are still unclear. Thus, in this work, we develop a deep learning model that is able to combine four weeks of environmental data, collected by pollutant-monitoring and weather stations, with patient information to predict an imminent relapse in the following week. Specifically, we cast the task as distinguishing between 4-week sequences followed by a relapse vs. 4-week sequences followed by another relapse-free week, the latter of which were extracted from MS patients who were never observed to have had a relapse. The 1556 sequences were collected in the context of the H2020 BRAINTEASER ('Bringing Artificial Intelligence Home for a Better Care of Amyotrophic Lateral Sclerosis and Multiple Sclerosis') project. The best-performing model was a recurrent neural network, which yielded an encouraging test-set area under the receiveroperating characteristic curve (AUROC) of 0.70. It also performed adequately (AUROC =0.60) on a modified version of the test set where the 4-week relapse-free sequences followed by another relapse-free week were extracted from the same subjects from whom the test sequences followed by a relapse came. Thus, our results, albeit preliminary, suggest that the inclusion of environmental data as the basis of predictive models of MS relapses is a promising direction to obtain short-term predictions, which may be helpful for therapy and life planning. It is especially encouraging that better-than-random performance was preserved on the modified test set, where environmental factors were, by construction, the most informative predictors.
Deep Learning Model Predicts Relapse Occurrence in Multiple Sclerosis Via Sequences of Environmental Data
Longato E.;Tavazzi E.;Milani A.;Marinello E.;Trescato I.;Guazzo A.;Vettoretti M.;Di Camillo B.
2025
Abstract
Air pollution is a known risk factor for the exacerbation of many diseases. Among these, is multiple sclerosis (MS), a chronic, autoimmune, neurological disease, characterised by transient episodes of neurological impairment known as relapses. Although the link between environmental factors and relapses has been a subject of investigation in the medical and biostatistical literature, its implications for predictive modelling are still unclear. Thus, in this work, we develop a deep learning model that is able to combine four weeks of environmental data, collected by pollutant-monitoring and weather stations, with patient information to predict an imminent relapse in the following week. Specifically, we cast the task as distinguishing between 4-week sequences followed by a relapse vs. 4-week sequences followed by another relapse-free week, the latter of which were extracted from MS patients who were never observed to have had a relapse. The 1556 sequences were collected in the context of the H2020 BRAINTEASER ('Bringing Artificial Intelligence Home for a Better Care of Amyotrophic Lateral Sclerosis and Multiple Sclerosis') project. The best-performing model was a recurrent neural network, which yielded an encouraging test-set area under the receiveroperating characteristic curve (AUROC) of 0.70. It also performed adequately (AUROC =0.60) on a modified version of the test set where the 4-week relapse-free sequences followed by another relapse-free week were extracted from the same subjects from whom the test sequences followed by a relapse came. Thus, our results, albeit preliminary, suggest that the inclusion of environmental data as the basis of predictive models of MS relapses is a promising direction to obtain short-term predictions, which may be helpful for therapy and life planning. It is especially encouraging that better-than-random performance was preserved on the modified test set, where environmental factors were, by construction, the most informative predictors.
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