Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran.

Background: The high number of COVID-19 deaths is a serious threat to the world. Demographic and clinical biomarkers are significantly associated with the mortality risk of this disease. This study aimed to implement Generalized Neural Additive Model (GNAM) as an interpretable machine learning method to predict the COVID-19 mortality of patients.

Application of machine learning models based on decision trees in classifying the factors affecting mortality of COVID-19 patients in Hamadan, Iran.

Background: Due to the high mortality of COVID-19 patients, the use of a high-precision classification model of patient's mortality that is also interpretable, could help reduce mortality and take appropriate action urgently. In this study, the random forest method was used to select the effective features in COVID-19 mortality and the classification was performed using logistic model tree (LMT), classification and regression tree (CART), C4.5, and C5.

Effect of Massage Therapy for the Treatment of Neonatal Jaundice: A Systematic Review and Dose-Response Meta-analysis.

Background: The effectiveness of massage therapy in the treatment of neonatal jaundice has been established in previous literature, but how much the level of massage can reduce the mean of bilirubin in neonates with jaundice is a question that has been addressed in this review.

Methods: Four electronic databases, including Cochrane, PubMed, Scopus, and Web of Science, were searched for relevant literature. For the dose-response association between massage therapy and treatment of neonatal icterus, we conducted a meta-analysis using the random-effects model.