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.

This study investigates the potential of using generic, hybrid, and personalized neural network models for glucose prediction in individuals with Type 1 Diabetes (T1D). Data from 194 participants in the Wireless Innovations for Seniors with Diabetes Mellitus (WISDM) study, totaling over 46 million minutes of Continuous Glucose Monitoring (CGM), were used to develop and evaluate the models. A baseline reference model, Last Observation Carried Forward (LOCF), was also included for comparison. Models were trained using data from 70% of the participants and tested on the remaining 30%, with prediction horizons (PH) set at 30 and 60 minutes. At the 30-minute PH, the generic model achieved a Root Mean Square Error (RMSE) of 19.6 mg/dL and a Mean Absolute Relative Difference (MARD) of 9.2%. These results were slightly worse than those of the hybrid and personalized models, which yielded RMSEs of 19.4 mg/dL and 19.5 mg/dL, respectively, and MARDs of 9.6% for both. However, the differences were not statistically significant. At the 60-minute PH, the generic model showed the best performance, with an RMSE of 34.9 mg/dL and MARD of 16.2%. The hybrid and personalized models exhibited slightly higher RMSEs (35.3 mg/dL and 35.5 mg/dL, respectively) and MARDs (17.8% and 18.1%, respectively). These findings suggest that both generic and individualized models can provide satisfactory glucose forecasting results based solely on CGM data. Nonetheless, the potential benefits of individualized approaches deserve further investigation, particularly when substantial training data are available.

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.

Past research has demonstrated that continuous exposure to pollutants, such as PM2.5 and PM10, is associated with an increased risk of developing and worsening respiratory and neurodegenerative diseases. Calculating and reducing exposure to these pollutants is crucial to assess these risks and perform proper prevention. In this study, we estimate personal exposure to PM2.5 based on the integration of sensors measurements, meteorological data and land use parameters, which could impact on actual pollution levels, especially in areas located far from the sensors. Pollution data have been collected from a dense network of sensors located in Pavia, Italy, meteorological and geographical data have been collected from public sources. We used geographical data to create graphs that model the city road structure, and applied Land Use Regression methods to estimate air pollution on its nodes, adjusting the measurements interpolated from the sensors with the effects of weather data, land use parameters such as the distance from the closest high-traffic road, and additional temporal information such as weekends/holidays and working days. We tested several regression methods: linear regression, both simple and with regularization (Ridge, LASSO and ElasticNet), Random Forest regression, Gradient Boosting and Support Vector Regression (SVR). Results show that meteorological variables, namely temperature and humidity, and temporal factors do contribute significantly in obtaining pollution values in the graph nodes that differ from values obtained exclusively through sensors interpolation.

Inflammatory Bowel Disease (IBD) is a term that includes two chronic conditions characterized by inflammation of the gastrointestinal tract, i.e., Ulcerative Colitis and Crohn’s Disease. IBD can be associated to symptoms that significantly reduce quality of life and can lead to serious complications, therefore proper disease monitoring and treatment is necessary. To this end, Telemedicine has been identified as a useful tool, since it can ease the patients’ burden on the management of nutrition and pharmacological treatment, and can improve the connection with the physicians. In this paper, we present NutriA, a new responsive web application for the management of IBD patients in Italy, created to assist patients with diet, medications and symptoms monitoring, optimizing the follow-ups frequency and providing direct assistance by the doctors. Besides being useful to improve the patients’ well-being, NutriA is also designed to be used in a new clinical study on the effects of malnutrition on IBD treatment.

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.

Disease subtyping can assist the development of precision medicine but remains a challenge in data analysis by reason of the many different methods to group individuals depending on their data. However, identification of subclasses of disease will help to produce better models which are more specific to patients and will improve prediction and interpretation of underlying characteristics of disease. This paper presents a novel algorithm that integrates latent class models with supervised learning. The new algorithm uses latent class models to cluster patients within groups that results in improved classification as well as aiding the understanding of the dissimilarities of the discovered groups. The methods are tested on data from patients with Systemic Sclerosis (SSc), a rare potentially fatal condition. Results show that the "Latent Class Multi-Label Classification Model" improves accuracy when compared with competitive similar methods.

Diseases are often umbrella terms for many subcategories of disease. The identification of these subcategories is vital if we are to develop personalised treatments that are better focussed on individual patients. In this short paper, we explore the use of a combination of unsupervised learning to identify potential subclasses, and supervised learning to build models for better predicting a number of different health outcomes for patients that suffer from systemic sclerosis, a rare chronic connective tissue disorder - but one that shares many characteristics with other diseases. We explore a number of different algorithms for constructing models that simultaneously predict health outcomes and identify subcategories.