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.

Multiple Sclerosis (MS) is a chronic autoimmune and inflammatory neurological disorder characterised by episodes of symptom exacerbation, known as relapses. Relapses have been linked to environmental factors such as the weather and pollutant concentrations in the air, but the exact relationship between these phenomena is still unclear. In this study, we investigated the role of environmental factors in predicting imminent relapse occurrence in MS patients, leveraging clinical and environmental data collected over a period of one week preceeding the possible event, using data collected in the context of the H2020 BRAINTEASER project. To do this, we developed and tested a range of combinations of predictive models (logistic regression, LR; and random forest, RF) and feature selection schemes, both manual and data-driven. The RF model trained after a data-driven feature selection process based on the Variable Importance in Projection (VIP) metric yielded the best results, i.e., an AUC-ROC of 0.713 and an AUC-PR of 0.639. We identified several key predictors, including clinical variables such as time since MS onset, age at onset, diagnostic delay, and the Expanded Disability Status Scale (EDSS) score, and environmental variables such as wind speed, precipitation, NO2, PM10, average and maximum temperatures, and humidity. These findings suggest that environmental factors may be viable predictors of imminent relapse occurrence in MS.

DYNAMITE, an acronym for DYNamic Archetypal analysis for MIning disease TrajEctories, is a new methodology developed specifically to model disease progression by exploiting information available in longitudinal clinical datasets. First, archetypal analysis is applied to data organised in matrix form, with the aim of finding extreme and representative disease states (archetypes) linked to the original data through convex coefficients. Then, each original observation is associated with a single archetype based on their similarity; finally, an event log is created encoding the progression of disease states for each patient in terms of archetype states. In the last stage of the procedure, archetypal analysis is coupled with process mining, which allows the event log archetypes to be visualised graphically as sequences of disease states, allowing the clinical trajectories of patients to be extracted and examined. As a proof of concept, we applied the proposed method to data from a cohort of amyotrophic lateral sclerosis patients whose progression was monitored using the 12-item ALSFRS-R questionnaire. Without any a priori knowledge, DYNAMITE identified six archetypes clearly describing different types and severity of impairment and provided reliable clinical trajectories consistent with the prognosis of amyotrophic lateral sclerosis patients. DYNAMITE offers high interpretability at every stage of the analysis, which makes it particularly suitable for use in healthcare where explainability is paramount, and enables analysis of clinical trajectories at both individual and population levels.

BACKGROUND: Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive loss of motor neurons in the brain and spinal cord. The fact that ALS's disease course is highly heterogeneous, and its determinants not fully known, combined with ALS's relatively low prevalence, renders the successful application of artificial intelligence (AI) techniques particularly arduous. OBJECTIVE: This systematic review aims at identifying areas of agreement and unanswered questions regarding two notable applications of AI in ALS, namely the automatic, data-driven stratification of patients according to their phenotype, and the prediction of ALS progression. Differently from previous works, this review is focused on the methodological landscape of AI in ALS. METHODS: We conducted a systematic search of the Scopus and PubMed databases, looking for studies on data-driven stratification methods based on unsupervised techniques resulting in (A) automatic group discovery or (B) a transformation of the feature space allowing patient subgroups to be identified; and for studies on internally or externally validated methods for the prediction of ALS progression. We described the selected studies according to the following characteristics, when applicable: variables used, methodology, splitting criteria and number of groups, prediction outcomes, validation schemes, and metrics. RESULTS: Of the starting 1604 unique reports (2837 combined hits between Scopus and PubMed), 239 were selected for thorough screening, leading to the inclusion of 15 studies on patient stratification, 28 on prediction of ALS progression, and 6 on both stratification and prediction. In terms of variables used, most stratification and prediction studies included demographics and features derived from the ALSFRS or ALSFRS-R scores, which were also the main prediction targets. The most represented stratification methods were K-means, and hierarchical and expectation-maximisation clustering; while random forests, logistic regression, the Cox proportional hazard model, and various flavours of deep learning were the most widely used prediction methods. Predictive model validation was, albeit unexpectedly, quite rarely performed in absolute terms (leading to the exclusion of 78 eligible studies), with the overwhelming majority of included studies resorting to internal validation only. CONCLUSION: This systematic review highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease progressively affecting upper and lower motor neurons in the brain and spinal cord. Mean life expectancy is three to five years, with paralysis of muscles, respiratory failure and loss of vital functions being the common causes of death. Clinical manifestations of ALS are heterogeneous due to the mix of anatomic regions involvement and the variability in disease course; consequently, diagnosis and prognosis at the level of individual patient is really challenging. Prediction of ALS progression and stratification of patients into meaningful subgroups have been long-standing interests to clinical practice, research and drug development. METHODS: We developed a Dynamic Bayesian Network (DBN) model on more than 4500 ALS patients included in the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT), in order to detect probabilistic relationships among clinical variables and identify risk factors related to survival and loss of vital functions. Furthermore, the DBN was used to simulate the temporal evolution of an ALS cohort predicting survival and the time to impairment of vital functions (communication, swallowing, gait and respiration). A first attempt to stratify patients by risk factors and simulate the progression of ALS subgroups was also implemented. RESULTS: The DBN model provided the prediction of ALS most probable trajectories over time in terms of important clinical outcomes, including survival and loss of autonomy in functional domains. Furthermore, it allowed the identification of biomarkers related to patients' clinical status as well as vital functions, and unrevealed their probabilistic relationships. For instance, DBN found that bicarbonate and calcium levels influence survival time; moreover, the model evidenced dependencies over time among phosphorus level, movement impairment and creatinine. Finally, our model provided a tool to stratify patients into subgroups of different prognosis studying the effect of specific variables, or combinations of them, on either survival time or time to loss of autonomy in specific functional domains. CONCLUSIONS: The analysis of the risk factors and the simulation allowed by our DBN model might enable better support for ALS prognosis as well as a deeper insight into disease manifestations, in a context of a personalized medicine approach.