BACKGROUND In 2013, New York began requiring hospitals to follow protocols for the early identification and treatment of sepsis. However, there is controversy about whether more rapid treatment of sepsis improves outcomes in patients. METHODS We studied data from patients with sepsis and septic shock that were reported to the New York State Department of Health from April 1, 2014, to June 30, 2016. Patients had a sepsis protocol initiated within 6 hours after arrival in the emergency department and had all items in a 3‐hour bundle of care for patients with sepsis (i.e., blood cultures, broad‐spectrum antibiotic agents, and lactate measurement) completed within 12 hours. Multilevel models were used to assess the associations between the time until completion of the 3‐hour bundle and risk‐adjusted mortality. We also examined the times to the administration of antibiotics and to the completion of an initial bolus of intravenous fluid. RESULTS Among 49,331 patients at 149 hospitals, 40,696 (82.5%) had the 3‐hour bundle completed within 3 hours. The median time to completion of the 3‐hour bundle was 1.30 hours (interquartile range, 0.65 to 2.35), the median time to the administration of antibiotics was 0.95 hours (interquartile range, 0.35 to 1.95), and the median time to completion of the fluid bolus was 2.56 hours (interquartile range, 1.33 to 4.20). Among patients who had the 3‐hour bundle completed within 12 hours, a longer time to the completion of the bundle was associated with higher risk‐adjusted in‐hospital mortality (odds ratio, 1.04 per hour; 95% confidence interval [CI], 1.02 to 1.05; P<0.001), as was a longer time to the administration of antibiotics (odds ratio, 1.04 per hour; 95% CI, 1.03 to 1.06; P<0.001) but not a longer time to the completion of a bolus of intravenous fluids (odds ratio, 1.01 per hour; 95% CI, 0.99 to 1.02; P=0.21). CONCLUSIONS More rapid completion of a 3‐hour bundle of sepsis care and rapid administration of antibiotics, but not rapid completion of an initial bolus of intravenous fluids, were associated with lower risk‐adjusted in‐hospital mortality. (Funded by the National Institutes of Health and others.)

Supplement to: Time to treatment and mortality during mandated emergency care for sepsis.

Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automated SZ diagnosis via electroencephalography (EEG) signals. The obtained results are compared with those of conventional intelligent methods. To implement the proposed methods, the dataset of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals were divided into 25 s time frames and then were normalized by z-score or norm L2. In the classification step, two different approaches were considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals was first carried out by conventional machine learning methods, e.g., support vector machine, k-nearest neighbors, decision tree, na\"ive Bayes, random forest, extremely randomized trees, and bagging. Various proposed DL models, namely, long short-term memories (LSTMs), one-dimensional convolutional networks (1D-CNNs), and 1D-CNN-LSTMs, were used in the following. In this step, the DL models were implemented and compared with different activation functions. Among the proposed DL models, the CNN-LSTM architecture has had the best performance. In this architecture, the ReLU activation function with the z-score and L2-combined normalization was used. The proposed CNN-LSTM model has achieved an accuracy percentage of 99.25%, better than the results of most former studies in this field. It is worth mentioning that to perform all simulations, the k-fold cross-validation method with k = 5 has been used.

Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models

Electroencephalography-based motor imagery classification using temporal convolutional network fusion

Major depressive disorder (MDD) is one of the most common modern ailments affected huge population throughout the world. The electroencephalogram (EEG) signal is widely used to screen the MDD. The manual diagnosis of MDD using EEG is time consuming, subjective and may cause human errors. Therefore, nowadays various automated systems have been developed to diagnose MDD accurately and rapidly. In this work, we have proposed a novel automated MDD detection system using EEG signals. Our proposed model has three steps: (i) Melamine pattern and discrete wavelet transform (DWT)- based multileveled feature generation, (ii) selection of most relevant features using neighborhood component analysis (NCA) and (iii) classification using support vector machine (SVM) and k nearest neighbor (kNN) classifiers. The novelty of this work is the application of melamine pattern. The molecular structure of melamine (also named chemistry spider- ChemSpider) is used to generate 1536 features. Also, various statistical features are extracted from DWT coefficients. The NCA is used to select the most relevant features and these selected features are classified using SVM and kNN classifiers. The presented model attained greater than 95% accuracies using all channels with quadratic SVM classifier. Our results obtained highest classification accuracy of 99.11% and 99.05% using Weighted kNN and Quadratic SVM respectively using A2A1 EEG channel. We have developed the automated depression model using a big dataset and yielded high classification accuracies. These results indicate that our presented model can be used in mental health clinics to confirm the manual diagnosis of psychiatrists.

https://link.springer.com/content/pdf/10.1007/s10489-021-02426-y.pdf

Automated major depressive disorder detection using melamine pattern with EEG signals

Decision support system for major depression detection using spectrogram and convolution neural network with EEG signals

DepHNN: A novel hybrid neural network for electroencephalogram (EEG)-based screening of depression

Sepsis is a major health issue causing mortality, morbidity and health care financial crisis to people around the globe. To resolve this issue, many researchers and clinical practitioners have worked hard to predict the onset of sepsis using various parameters of patients. The proposed work is an attempt of authors to analyse the various parameters (8 vital parameters, 26 laboratory or clinical parameters, and 6 demographic parameters regarding hospital stay) given in Physionet Challenge dataset so as to devise the best features for early and efficient prediction of sepsis. Authors have also addressed another important issue of missing values of some parameters of some patients by applying Gaussian Mixture Model to estimate the missing value in pre-processing steps. The pre-processed data is then fed to Extreme Gradient Boosting algorithm (XGBoost), which is a state of the art performer algorithm for prediction purposes in data analysis field. The experimental results show that by real time monitoring of data from cloud, sepsis can be predicted 6 hours prior to the onset of sepsis with an accuracy score of 0.994 and AUC score of 0.867. It is also observed that demographic parameters play a vital role in sepsis prediction. The Since the parameters used for early prediction can be easily acquired with the help of sensors, the proposed approach proves its potential for development of mobile and website applications for patient monitoring, real-time prediction of sepsis and generation of appropriate alert system.

/pdf/analysis-of-various-health-parameters-for-early-and-2vf1jo8wdj.pdf

Analysis of various health parameters for early and efficient prediction of sepsis

During this worldwide crisis, it is well known that the whole world has been hit by a plenitude of untimely deaths caused during this pandemic. The lockdown in various countries has affected the lives of human beings in many ways. Because of this, it becomes necessary to study the complex interplay of various factors, ranging from macro-scale components such as population density, mortality rate, and recovery rate to singular components such as diabetic patients, smokers, gender, and age. A major concern of higher authorities is the accurate forecasting of COVID-19 cases and the role of various factors in COVID-19 spread to assist the policymakers in understanding the economic situation of the country as well as the factors which affect the current mortality rate. The presented work aims to resolve these concerns by proposing a multivariate hybrid model by taking all the aforementioned factors into account to forecast COVID-19 cases. The proposed model consists of a convolutional neural network (CNN) layer for feature extraction and long short-term memory (LSTM) layers to forecast COVID-19 cases, thus exhibiting the inherited advantage of both. The model is trained and tested on the online available dataset acquired from various resources. Experimental results show that the proposed model can forecast the number of cases in the coming month with a mean absolute error equal to 1.78, a training accuracy of 90.63%, and validation accuracy of 95.48%.

Abhishek Parashar

Papers

DepHNN: A novel hybrid neural network for electroencephalogram (EEG)-based screening of depression

Analysis of various health parameters for early and efficient prediction of sepsis

Forecasting Covid-19 Cases in India using Multivariate Hybrid CNN-LSTM Model