Schwarz Sandberg (angerteller2)

ompleters and the control group. The trial was terminated early for reasons of futility based on the results of an interim analysis, which we performed because of inclusion problems. CONCLUSIONS Our study did show a significant reduction in affective symptoms in both groups, but the differences in reduction of affective symptoms between the intervention and control groups were not significant. There were also no differences in perinatal child outcomes. Future research should examine for which women these interventions might be effective or if changes in the internet intervention might make the intervention more effective. TRIAL REGISTRATION Netherlands Trial Register NL4162; https//tinyurl.com/sdckjek. ©Hanna M Heller, Adriaan W Hoogendoorn, Adriaan Honig, Birit FP Broekman, Annemieke van Straten. Originally published in the Journal of Medical Internet Research (http//), 23.03.2020.BACKGROUND Metabolic syndrome is a cluster of disorders that significantly influence the development and deterioration of numerous diseases. FibroScan is an ultrasound device that was recently shown to predict metabolic syndrome with moderate accuracy. However, previous research regarding prediction of metabolic syndrome in subjects examined with FibroScan has been mainly based on conventional statistical models. selleck chemical Alternatively, machine learning, whereby a computer algorithm learns from prior experience, has better predictive performance over conventional statistical modeling. OBJECTIVE We aimed to evaluate the accuracy of different decision tree machine learning algorithms to predict the state of metabolic syndrome in self-paid health examination subjects who were examined with FibroScan. METHODS Multivariate logistic regression was conducted for every known risk factor of metabolic syndrome. Principal components analysis was used to visualize the distribution of metabolic syndrome patients. We further applied various statistical machine learning techniques to visualize and investigate the pattern and relationship between metabolic syndrome and several risk variables. RESULTS Obesity, serum glutamic-oxalocetic transaminase, serum glutamic pyruvic transaminase, controlled attenuation parameter score, and glycated hemoglobin emerged as significant risk factors in multivariate logistic regression. The area under the receiver operating characteristic curve values for classification and regression trees and for the random forest were 0.831 and 0.904, respectively. CONCLUSIONS Machine learning technology facilitates the identification of metabolic syndrome in self-paid health examination subjects with high accuracy. ©Cheng-Sheng Yu, Yu-Jiun Lin, Chang-Hsien Lin, Sen-Te Wang, Shiyng-Yu Lin, Sanders H Lin, Jenny L Wu, Shy-Shin Chang. Originally published in JMIR Medical Informatics (http//medinform.jmir.org), 23.03.2020.BACKGROUND Scalable and accurate health outcome prediction using electronic health record (EHR) data has gained much attention in research recently. Previous machine learning models mostly ignore relations between different types of clinical data (ie, laboratory components, International Classification of Diseases codes, and medications). OBJECTIVE This study aimed to model such relations and build predictive models using the EHR data from intensive care units. We developed innovative neural network models and compared them with the widely used logistic regression model and other state-of-the-art neural network models to predict the patient's mortality using their longitudinal EHR data. METHODS We built a set of neural network models that we collectively called as long short-term memory (LSTM) outcome prediction using comprehensive feature relations or in short, CLOUT. Our CLOUT models use a correlational neural network model to identify a latent space representation between different types of discrete clinicAlok Kapoor, Edgard Granillo, Hong Yu. Originally published in the Journal of Medical Internet Researc