scholarly journals Towards early sepsis detection from measurements at the general ward through deep learning (Preprint)

2020 ◽  
Author(s):  
Sebastiaan Pascal Oei ◽  
Ruud Johannes Gerardus van Sloun ◽  
Myrthe van der Ven ◽  
Hendrikus Hubertus Maria Korsten ◽  
Massimo Mischi
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Reference Model ◽  
Early Stage ◽  
General Ward ◽  
Classification Model ◽  
Prediction Time ◽  
Resolution Model ◽  
Early Sepsis ◽  
Sepsis Detection

BACKGROUND Sepsis is one of the leading causes of death in the hospital. Several warning scores have been developed to categorize patients’ degrees of illness, with the purpose of recognizing sepsis development at an early stage and consequently reducing time before starting treatment. The most accurate classification method, known as the SOFA score, is developed for use in the intensive care unit (ICU). OBJECTIVE Sepsis is not exclusively developing in the ICU and may occur in any hospitalized patient. Therefore, a method for sepsis recognition outside the ICU is of major importance. METHODS Recently, the use of computational methods has been proposed for early sepsis prediction. Multiple sepsis classifiers have been devised using machine learning methods. We validated the linear classification model devised by Calvert et al. and improved upon it using a deep neural network trained on data from the MIMIC-III database. RESULTS The reference model based on Calvert et al. approach yielded an AUROC of 0.81 for a 3-hour prediction time. The deep neural network outperformed the linear model, reaching an AUROC of 0.85 for a 3-hour prediction time. CONCLUSIONS Our results are comparable to the high-resolution model derived by Nemati et al. yet using only 8 simple and commonly performed measurements, instead of the complex set of 65 measurements leveraged by Nemati et al. Therefore, sepsis prediction may also be viable in less monitored environments in the hospital, such as the general ward and the emergency room.

scholarly journals An efficient technique for CT scan images classification of COVID-19

2020 ◽  
pp. 1-14
Author(s):  
Esraa Hassan ◽  
Noha A. Hikal ◽  
Samir Elmuogy
Keyword(s):  
Neural Network ◽  
Diagnostic Tool ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Performance Metrics ◽  
Classification Model ◽  
Data Set ◽  
Training Models ◽  
The Earth ◽  
Diagnostic Time

Nowadays, Coronavirus (COVID-19) considered one of the most critical pandemics in the earth. This is due its ability to spread rapidly between humans as well as animals. COVID_19 expected to outbreak around the world, around 70 % of the earth population might infected with COVID-19 in the incoming years. Therefore, an accurate and efficient diagnostic tool is highly required, which the main objective of our study. Manual classification was mainly used to detect different diseases, but it took too much time in addition to the probability of human errors. Automatic image classification reduces doctors diagnostic time, which could save human’s life. We propose an automatic classification architecture based on deep neural network called Worried Deep Neural Network (WDNN) model with transfer learning. Comparative analysis reveals that the proposed WDNN model outperforms by using three pre-training models: InceptionV3, ResNet50, and VGG19 in terms of various performance metrics. Due to the shortage of COVID-19 data set, data augmentation was used to increase the number of images in the positive class, then normalization used to make all images have the same size. Experimentation is done on COVID-19 dataset collected from different cases with total 2623 where (1573 training,524 validation,524 test). Our proposed model achieved 99,046, 98,684, 99,119, 98,90 In terms of Accuracy, precision, Recall, F-score, respectively. The results are compared with both the traditional machine learning methods and those using Convolutional Neural Networks (CNNs). The results demonstrate the ability of our classification model to use as an alternative of the current diagnostic tool.

scholarly journals Towards early sepsis detection from measurements at the general ward through deep learning

2021 ◽  
pp. 100042
Author(s):  
Sebastiaan P. Oei ◽  
Ruud JG. van Sloun ◽  
Myrthe van der Ven ◽  
Hendrikus HM. Korsten ◽  
Massimo Mischi
Keyword(s):  
Deep Learning ◽  
General Ward ◽  
Early Sepsis ◽  
Sepsis Detection

scholarly journals Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning

10.2196/23230 ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. e23230
Author(s):  
Pei-Fu Chen ◽  
Ssu-Ming Wang ◽  
Wei-Chih Liao ◽  
Lu-Cheng Kuo ◽  
Kuan-Chih Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Deep Neural Network ◽  
University Hospital ◽  
Classification Model ◽  
Icd 10 ◽  
And Training

Background The International Classification of Diseases (ICD) code is widely used as the reference in medical system and billing purposes. However, classifying diseases into ICD codes still mainly relies on humans reading a large amount of written material as the basis for coding. Coding is both laborious and time-consuming. Since the conversion of ICD-9 to ICD-10, the coding task became much more complicated, and deep learning– and natural language processing–related approaches have been studied to assist disease coders. Objective This paper aims at constructing a deep learning model for ICD-10 coding, where the model is meant to automatically determine the corresponding diagnosis and procedure codes based solely on free-text medical notes to improve accuracy and reduce human effort. Methods We used diagnosis records of the National Taiwan University Hospital as resources and apply natural language processing techniques, including global vectors, word to vectors, embeddings from language models, bidirectional encoder representations from transformers, and single head attention recurrent neural network, on the deep neural network architecture to implement ICD-10 auto-coding. Besides, we introduced the attention mechanism into the classification model to extract the keywords from diagnoses and visualize the coding reference for training freshmen in ICD-10. Sixty discharge notes were randomly selected to examine the change in the F1-score and the coding time by coders before and after using our model. Results In experiments on the medical data set of National Taiwan University Hospital, our prediction results revealed F1-scores of 0.715 and 0.618 for the ICD-10 Clinical Modification code and Procedure Coding System code, respectively, with a bidirectional encoder representations from transformers embedding approach in the Gated Recurrent Unit classification model. The well-trained models were applied on the ICD-10 web service for coding and training to ICD-10 users. With this service, coders can code with the F1-score significantly increased from a median of 0.832 to 0.922 (P<.05), but not in a reduced interval. Conclusions The proposed model significantly improved the F1-score but did not decrease the time consumed in coding by disease coders.

First-Break Picking Classification Models Using Recurrent Neural Network

2021 ◽  
Author(s):  
Mohammed Ayub ◽  
SanLinn Kaka
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Contextual Information ◽  
Classification Model ◽  
Superior Performance ◽  
Learning Models ◽  
Neural Network Models ◽  
Minimum Number ◽  
Machine Learning Models

Abstract Manual first-break picking from a large volume of seismic data is extremely tedious and costly. Deployment of machine learning models makes the process fast and cost effective. However, these machine learning models require high representative and effective features for accurate automatic picking. Therefore, First- Break (FB) picking classification model that uses effective minimum number of features and promises performance efficiency is proposed. The variants of Recurrent Neural Networks (RNNs) such as Long ShortTerm Memory (LSTM) and Gated Recurrent Unit (GRU) can retain contextual information from long previous time steps. We deploy this advantage for FB picking as seismic traces are amplitude values of vibration along the time-axis. We use behavioral fluctuation of amplitude as input features for LSTM and GRU. The models are trained on noisy data and tested for generalization on original traces not seen during the training and validation process. In order to analyze the real-time suitability, the performance is benchmarked using accuracy, F1-measure and three other established metrics. We have trained two RNN models and two deep Neural Network models for FB classification using only amplitude values as features. Both LSTM and GRU have the accuracy and F1-measure with a score of 94.20%. With the same features, Convolutional Neural Network (CNN) has an accuracy of 93.58% and F1-score of 93.63%. Again, Deep Neural Network (DNN) model has scores of 92.83% and 92.59% as accuracy and F1-measure, respectively. From the pexperiment results, we see significant superior performance of LSTM and GRU to CNN and DNN when used the same features. For robustness of LSTM and GRU models, the performance is compared with DNN model that is trained using nine features derived from seismic traces and observed that the performance superiority of RNN models. Therefore, it is safe to conclude that RNN models (LSTM and GRU) are capable of classifying the FB events efficiently even by using a minimum number of features that are not computationally expensive. The novelty of our work is the capability of automatic FB classification with the RNN models that incorporate contextual behavioral information without the need for sophisticated feature extraction or engineering techniques that in turn can help in reducing the cost and fostering classification model robust and faster.

scholarly journals Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1620 ◽  
Author(s):  
Ganjar Alfian ◽  
Muhammad Syafrudin ◽  
Norma Latif Fitriyani ◽  
Muhammad Anshari ◽  
Pavel Stasa ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Risk Factors ◽  
Diabetic Retinopathy ◽  
Prediction Model ◽  
Deep Neural Network ◽  
Early Stage ◽  
Individual Risk ◽  
Proposed Model ◽  
Individual Risk Factors

Extracting information from individual risk factors provides an effective way to identify diabetes risk and associated complications, such as retinopathy, at an early stage. Deep learning and machine learning algorithms are being utilized to extract information from individual risk factors to improve early-stage diagnosis. This study proposes a deep neural network (DNN) combined with recursive feature elimination (RFE) to provide early prediction of diabetic retinopathy (DR) based on individual risk factors. The proposed model uses RFE to remove irrelevant features and DNN to classify the diseases. A publicly available dataset was utilized to predict DR during initial stages, for the proposed and several current best-practice models. The proposed model achieved 82.033% prediction accuracy, which was a significantly better performance than the current models. Thus, important risk factors for retinopathy can be successfully extracted using RFE. In addition, to evaluate the proposed prediction model robustness and generalization, we compared it with other machine learning models and datasets (nephropathy and hypertension–diabetes). The proposed prediction model will help improve early-stage retinopathy diagnosis based on individual risk factors.

scholarly journals Development of an Autism Screening Classification Model for Toddlers

2021 ◽  
Author(s):  
Afef Saihi ◽  
Hussam Alshraideh
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Early Stage ◽  
Neurodevelopmental Disorder ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Therapeutic Interventions ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Early Screening

Autism spectrum disorder ASD is a neurodevelopmental disorder associated with challenges in communication, social interaction, and repetitive behaviors. Getting a clear diagnosis for a child is necessary for starting early intervention and having access to therapy services. However, there are many barriers that hinder the screening of these kids for autism at an early stage which might delay further the access to therapeutic interventions. One promising direction for improving the efficiency and accuracy of ASD detection in toddlers is the use of machine learning techniques to build classifiers that serve the purpose. This paper contributes to this area and uses the data developed by Dr. Fadi Fayez Thabtah to train and test various machine learning classifiers for the early ASD screening. Based on various attributes, three models have been trained and compared which are Decision tree C4.5, Random Forest, and Neural Network. The three models provided very good accuracies based on testing data, however, it is the Neural Network that outperformed the other two models. This work contributes to the early screening of toddlers by helping identify those who have ASD traits and should pursue formal clinical diagnosis.

scholarly journals PROUD-MAL: static analysis-based progressive framework for deep unsupervised malware classification of windows portable executable

2021 ◽  
Author(s):  
Syed Khurram Jah Rizvi ◽  
Warda Aslam ◽  
Muhammad Shahzad ◽  
Shahzad Saleem ◽  
Muhammad Moazam Fraz
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Static Analysis ◽  
Deep Neural Network ◽  
Early Stage ◽  
Malware Detection ◽  
Machine Learning Algorithms ◽  
Organizational Network ◽  
Depth Analysis ◽  
Feature Attention

AbstractEnterprises are striving to remain protected against malware-based cyber-attacks on their infrastructure, facilities, networks and systems. Static analysis is an effective approach to detect the malware, i.e., malicious Portable Executable (PE). It performs an in-depth analysis of PE files without executing, which is highly useful to minimize the risk of malicious PE contaminating the system. Yet, instant detection using static analysis has become very difficult due to the exponential rise in volume and variety of malware. The compelling need of early stage detection of malware-based attacks significantly motivates research inclination towards automated malware detection. The recent machine learning aided malware detection approaches using static analysis are mostly supervised. Supervised malware detection using static analysis requires manual labelling and human feedback; therefore, it is less effective in rapidly evolutionary and dynamic threat space. To this end, we propose a progressive deep unsupervised framework with feature attention block for static analysis-based malware detection (PROUD-MAL). The framework is based on cascading blocks of unsupervised clustering and features attention-based deep neural network. The proposed deep neural network embedded with feature attention block is trained on the pseudo labels. To evaluate the proposed unsupervised framework, we collected a real-time malware dataset by deploying low and high interaction honeypots on an enterprise organizational network. Moreover, endpoint security solution is also deployed on an enterprise organizational network to collect malware samples. After post processing and cleaning, the novel dataset consists of 15,457 PE samples comprising 8775 malicious and 6681 benign ones. The proposed PROUD-MAL framework achieved an accuracy of more than 98.09% with better quantitative performance in standard evaluation parameters on collected dataset and outperformed other conventional machine learning algorithms. The implementation and dataset are available at https://bit.ly/35Sne3a.

Sign in / Sign up

Export Citation Format

Share Document