HUMIC ACID-LIKE MATTER ISOLATED FROM GREEN URBAN WASTES. PART II: PERFORMANCE IN CHEMICAL AND ENVIRONMENTAL TECHNOLOGIES

Novel uses of the organic fraction of municipal solid wastes for diversified technological applications are reported. A humic acid-like substance (cHAL2) isolated from green urban wastes was tested as a chemical auxiliary for fabric cleaning and dyeing, and as a catalyst for the photodegradation of dyes. The results illustrate the fact that biomass wastes can be an interesting source of products for the chemical market. Process and product development in this direction are likely to offer high economic and environmental benefits in a modern, more sustainable waste treatment strategy

Machine learning in clinical and epidemiological research: isn't it time for biostatisticians to work on it?

In recent years, there has been a widespread cross-fertilization between Medical Statistics and Machine Learning (ML) techniques

Comparison of Machine Learning Techniques for Prediction of Hospitalization in Heart Failure Patients

The present study aims to compare the performance of eight Machine Learning Techniques (MLTs) in the prediction of hospitalization among patients with heart failure, using data from the Gestione Integrata dello Scompenso Cardiaco (GISC) study. The GISC project is an ongoing study that takes place in the region of Puglia, Southern Italy. Patients with a diagnosis of heart failure are enrolled in a long-term assistance program that includes the adoption of an online platform for data sharing between general practitioners and cardiologists working in hospitals and community health districts. Logistic regression, generalized linear model net (GLMN), classification and regression tree, random forest, adaboost, logitboost, support vector machine, and neural networks were applied to evaluate the feasibility of such techniques in predicting hospitalization of 380 patients enrolled in the GISC study, using data about demographic characteristics, medical history, and clinical characteristics of each patient. The MLTs were compared both without and with missing data imputation. Overall, models trained without missing data imputation showed higher predictive performances. The GLMN showed better performance in predicting hospitalization than the other MLTs, with an average accuracy, positive predictive value and negative predictive value of 81.2%, 87.5%, and 75%, respectively. Present findings suggest that MLTs may represent a promising opportunity to predict hospital admission of heart failure patients by exploiting health care information generated by the contact of such patients with the health care system