Global Epileptic Seizure Identification With Affinity Propagation Clustering Partition Mutual Information Using Cross-Layer Fully Connected Neural Network

Dynamic nomogram for predicting acute kidney injury in patients with community-acquired pneumonia

INTRODUCTION

Acute kidney injury (AKI) is a common complication in patients with community-acquired pneumonia (CAP) and negatively affects both short-term and long-term prognosis in patients with CAP. However, no study has been conducted on developing a clinical tool for predicting AKI in CAP patients. Therefore, this study aimed to develop a predictive tool based on a dynamic nomogram for AKI in CAP patients.

METHODS

This retrospective study was conducted from January 2014 to May 2017, and data from adult inpatients with CAP at Nanjing First Hospital were analysed. Demographic data and clinical data were obtained. The least absolute shrinkage and selection operator (LASSO) regression model was used to select important variables, which were entered into logistic regression to construct the predictive model for AKI. A dynamic nomogram was based on the results of the logistic regression model. Calibration and discrimination were used to assess the performance of the dynamic nomogram. A decision curve analysis was used to assess clinical efficacy.

RESULTS

A total of 2883 CAP patients were enrolled in this study. The median age was 76 years (IQR 63-84), and 61.3% were male. AKI developed in 827 (28.7%) patients. The LASSO regression analysis selected five important factors for AKI (albumin, acute respiratory failure, CURB-65 score, Cystatin C and white cell count), which were then entered into the logistic regression to construct the predictive model for AKI in CAP patients. The dynamic nomogram model showed good discrimination with an area under the receiver operating characteristics curve of 0.870 and good calibration with a Brier score of 0.129 and a calibration plot. The decision curve analysis showed that the dynamic nomogram prediction model had good clinical decision-making.

CONCLUSION

This easy-to-use dynamic nomogram may help physicians predict AKI in patients with CAP.

Interpretation of deep non-linear factorization for autism

Autism, a neurodevelopmental disorder, presents significant challenges for diagnosis and classification. Despite the widespread use of neural networks in autism classification, the interpretability of their models remains a crucial issue. This study aims to address this concern by investigating the interpretability of neural networks in autism classification using the deep symbolic regression and brain network interpretative methods. Specifically, we analyze publicly available autism fMRI data using our previously developed Deep Factor Learning model on a Hibert Basis tensor (HB-DFL) method and extend the interpretative Deep Symbolic Regression method to identify dynamic features from factor matrices, construct brain networks from generated reference tensors, and facilitate the accurate diagnosis of abnormal brain network activity in autism patients by clinicians. Our experimental results show that our interpretative method effectively enhances the interpretability of neural networks and identifies crucial features for autism classification.

Deep learning for predicting the occurrence of cardiopulmonary diseases in Nanjing, China

The efficiency of disease prevention and medical care service necessitated the prediction of incidence. However, predictive accuracy and power were largely impeded in a complex system including multiple environmental stressors and health outcome of which the occurrence might be episodic and irregular in time. In this study, we established four different deep learning (DL) models to capture inherent long-term dependencies in sequences and potential complex relationships among constituents by initiating with the original input into a representation at a higher abstract level. We collected 504,555 and 786,324 hospital outpatient visits of grouped categories of respiratory (RESD) and circulatory system disease (CCD), respectively, in Nanjing from 2013 through 2018. The matched observations in time-series that might pose risk to cardiopulmonary health involved conventional air pollutants concentrations and metrological conditions. The results showed that a well-trained network architecture built upon long short-term memory block and a working day enhancer achieved optimal performance by three quantitative statistics, i.e., 0.879 and 0.902 of Nash-Sutcliffe efficiency, 0.921% and 0.667% of percent bias, and 0.347 and 0.312 of root mean square error-standard deviation ratio for RESD and CCD hospital visits, respectively. We observed the non-linear association of nitrogen dioxide and ambient air temperature with CCD hospital visits. Furthermore, these two environmental stressors were identified as the most sensitive predictive variables, and exerted synergetic effect for two health outcomes, particular in winter season. Our study indicated that high-quality surveillance data of atmospheric environments could provide novel opportunity for anticipating temporal trend of cardiopulmonary health outcomes based on DL model.