Match score dataset for team ball sports.

In this data article, we present a dataset containing match scores from major international competitions for 12 popular team ball sports: basketball, cricket, field hockey, futsal, handball, ice hockey, lacrosse, roller hockey, rugby, soccer, volleyball, and water polo. The dataset was obtained by web scraping data available on Wikipedia pages and includes the following information related to individual matches: the year of the competition edition when a match occurred, the names of the two opposing teams, their respective scores, and the name of the winning team. Our match score dataset provides researchers in the field of sports analytics with valuable data that can be used to compute team statistics, develop team ranking and rating systems, infer patterns and trends in a team's performance across the edition years, build predictive models to forecast the outcome of future matches, and evaluate the performance of machine learning algorithms.

Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models.

OBJECTIVE: The increased survival in Systemic Lupus Erythematosus (SLE) patients implies the development of chronic damage, occurring in up to 50% of cases. Its prevention is a major goal in the SLE management. We aimed at predicting chronic damage in a large monocentric SLE cohort by using neural networks. METHODS: We enrolled 413 SLE patients (M/F 30/383; mean age ± SD 46.3±11.9 years; mean disease duration ± SD 174.6 ± 112.1 months). Chronic damage was assessed by the SLICC/ACR Damage Index (SDI). We applied Recurrent Neural Networks (RNNs) as a machine-learning model to predict the risk of chronic damage. The clinical data sequences registered for each patient during the follow-up were used for building and testing the RNNs. RESULTS: At the first visit in the Lupus Clinic, 35.8% of patients had an SDI≥1. For the RNN model, two groups of patients were analyzed: patients with SDI = 0 at the baseline, developing damage during the follow-up (N = 38), and patients without damage (SDI = 0). We created a mathematical model with an AUC value of 0.77, able to predict damage development. A threshold value of 0.35 (sensitivity 0.74, specificity 0.76) seemed able to identify patients at risk to develop damage. CONCLUSION: We applied RNNs to identify a prediction model for SLE chronic damage. The use of the longitudinal data from the Sapienza Lupus Cohort, including laboratory and clinical items, resulted able to construct a mathematical model, potentially identifying patients at risk to develop damage.