Predicting extremely low body weight from 12-lead electrocardiograms using a deep neural network

Abstract Previous studies have successfully predicted overweight status by applying deep learning to 12-lead electrocardiogram (ECG); however, models for predicting underweight status remain unexplored. Here, we assessed the feasibility of deep learning in predicting extremely low body weight using 12-lead ECGs, thereby investigating the prediction rationale for highlighting the parts of ECGs that are associated with extremely low body weight. Using records of inpatients predominantly with anorexia nervosa, we trained a convolutional neural network (CNN) that inputs a 12-lead ECG and outputs a binary prediction of whether body mass index is ≤ 12.6 kg/m2. This threshold was identified in a previous study as the optimal cutoff point for predicting the onset of refeeding syndrome. The CNN model achieved an area under the receiver operating characteristic curve of 0.807 (95% confidence interval, 0.745–0.869) on the test dataset. The gradient-weighted class activation map showed that the model focused on QRS waves. A negative correlation with the prediction scores was observed for QRS voltage. These results suggest that deep learning is feasible for predicting extremely low body weight using 12-lead ECGs, and several ECG features, such as lower QRS voltage, may be associated with extremely low body weight in patients with anorexia nervosa

Machine learning analysis to identify the association between risk factors and onset of nosocomial diarrhea: a retrospective cohort study

Background Although several risk factors for nosocomial diarrhea have been identified, the detail of association between these factors and onset of nosocomial diarrhea, such as degree of importance or temporal pattern of influence, remains unclear. We aimed to determine the association between risk factors and onset of nosocomial diarrhea using machine learning algorithms. Methods We retrospectively collected data of patients with acute cerebral infarction. Seven variables, including age, sex, modified Rankin Scale (mRS) score, and number of days of antibiotics, tube feeding, proton pump inhibitors, and histamine 2-receptor antagonist use, were used in the analysis. We split the data into a training dataset and independant test dataset. Based on the training dataset, we developed a random forest, support vector machine (SVM), and radial basis function (RBF) network model. By calculating an area under the curve (AUC) of the receiver operating characteristic curve using 5-fold cross-validation, we performed feature selection and hyperparameter optimization in each model. According to their final performances, we selected the optimal model and also validated it in the independent test dataset. Based on the selected model, we visualized the variable importance and the association between each variable and the outcome using partial dependence plots. Results Two-hundred and eighteen patients were included. In the cross-validation within the training dataset, the random forest model achieved an AUC of 0.944, which was higher than in the SVM and RBF network models. The random forest model also achieved an AUC of 0.832 in the independent test dataset. Tube feeding use days, mRS score, antibiotic use days, age and sex were strongly associated with the onset of nosocomial diarrhea, in this order. Tube feeding use had an inverse U-shaped association with the outcome. The mRS score and age had a convex downward and increasing association, while antibiotic use had a convex upward association with the outcome. Conclusion We revealed the degree of importance and temporal pattern of the influence of several risk factors for nosocomial diarrhea, which could help clinicians manage nosocomial diarrhea

Association of psychosocial factors with physical activity among Japanese adults aged 65 and older: a 6-year repeated cross-sectional study from the Nakanojo Study

Abstract Background Physical activity (PA) provides substantial mental and physical health benefits for individuals of all ages. A limited number of long-term or longitudinal studies have investigated the association between psychosocial factors and PA in healthy older adults aged 65 and above. This study aimed to determine the long-term relationship between psychosocial factors, such as vitality, mental health, anxiety, and depression, and objectively measure PA in older adults. Methods Healthy participants from Nakanojo, Japan, aged 65–90, capable of walking, were included in this study and were followed up from 2008 to 2013. Those diagnosed with dementia and depression were excluded. Using a repeated cross-sectional dataset, a multilevel model was developed with psychosocial variables as independent variables and an average daily duration of PA volume of > 3 metabolic equivalents (METs) as the outcome. The Akaike information criterion was used to select the final model. Results This study included 1108 records from 319 participants. In the multilevel model, age (coefficient = -0.106, 95% confidence interval [CI] = -0.127 to -0.086, p  3 METs, whereas male sex (coefficient = 0.343, 95% CI = 0.115 to 0.571, p = 0.003) was positively associated with PA volume. Conclusion Depressive symptoms were related to a reduced duration of PA volume of > 3 METs among these adults aged 65 and above