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Mixed pyramid attention network for nuclear cataract classification based on anterior segment OCT images
AbstractNuclear cataract (NC) is a leading ocular disease globally for blindness and vision impairment. NC patients can improve their vision through cataract surgery or slow the opacity development with early intervention. Anterior segment optical coherence tomography (AS-OCT) image is an emerging ophthalmic image type, which can clearly observe the whole lens structure. Recently, clinicians have been increasingly studying the correlation between NC severity levels and clinical features from the nucleus region on AS-OCT images, and the results suggested the correlation is strong. However, automatic NC classification resear...
Source: Health Information Science and Systems - March 25, 2022 Category: Information Technology Source Type: research

Machine learning models for classification and identification of significant attributes to detect type 2 diabetes
AbstractType 2 Diabetes (T2D) is a chronic disease characterized by abnormally high blood glucose levels due to insulin resistance and reduced pancreatic insulin production. The challenge of this work is to identify T2D-associated features that can distinguish T2D sub-types for prognosis and treatment purposes. We thus employed machine learning (ML) techniques to categorize T2D patients using data from the Pima Indian Diabetes Dataset from the Kaggle ML repository. After data preprocessing, several feature selection techniques were used to extract feature subsets, and a range of classification techniques were used to analy...
Source: Health Information Science and Systems - February 9, 2022 Category: Information Technology Source Type: research

COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network
AbstractThe reliable and rapid identification of the COVID-19 has become crucial to prevent the rapid spread of the disease, ease lockdown restrictions and reduce pressure on public health infrastructures. Recently, several methods and techniques have been proposed to detect the SARS-CoV-2 virus using different images and data. However, this is the first study that will explore the possibility of using deep convolutional neural network (CNN) models to detect COVID-19 from electrocardiogram (ECG) trace images. In this work, COVID-19 and other cardiovascular diseases (CVDs) were detected using deep-learning techniques. A pub...
Source: Health Information Science and Systems - January 19, 2022 Category: Information Technology Source Type: research

External validation of Machine Learning models for COVID-19 detection based on Complete Blood Count
ConclusionWe report an average AUC of 95% and average Brier score of 0.11, out-performing existing ML methods, and showing good cross-site transportability. The best performing model (SVM) reported an average AUC of 97.5% (Sensitivity: 87.5%, Specificity: 94%), comparable with the performance of RT-PCR, and was also the best calibrated. The validated models can be useful in the early identification of potential COVID-19 patients, due to the rapid availability of CBC exams, and in multiple test settings. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - October 23, 2021 Category: Information Technology Source Type: research

COVID-19 diagnosis from chest x-rays: developing a simple, fast, and accurate neural network
ConclusionsOur parsimonious CNN shows that it is possible to differentiate COVID-19 from other viral pneumonia and normal lungs on x-ray images with high accuracy. Our method may assist clinicians with making more accurate diagnostic decisions and support chest X-rays as a valuable screening tool for the early, rapid diagnosis of COVID-19. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - October 12, 2021 Category: Information Technology Source Type: research

Na ïve Bayesian network-based contribution analysis of tumor biology and healthcare factors to racial disparity in breast cancer stage-at-diagnosis
ConclusionThe modifiable mechanisms of mammography utilization and biopsy delay drive about 49.9% of racial difference in stage-at-diagnosis, potentially guiding more targeted interventions to eliminate cancer outcome disparities. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - September 24, 2021 Category: Information Technology Source Type: research

Predicting special care during the COVID-19 pandemic: a machine learning approach
In this study, we propose a method based on statistics and machine learning that uses blood lab exam data from patients to predict whether patients will require special care (hospitalization in regular or special-care units). We also predict the number of days the patients will stay under such care. The two-step procedure developed uses Bayesian Optimisation to select the best model among several candidates. This leads us to final models that achieve 0.94 area under ROC curve performance for the first target and 1.87 root mean squared error for the second target (which is a 77% improvement over the mean baseline) —making...
Source: Health Information Science and Systems - August 14, 2021 Category: Information Technology Source Type: research

Improving convolutional neural networks performance for image classification using test time augmentation: a case study using MURA dataset
AbstractBone fractures are one of the main causes to visit the emergency room (ER); the primary method to detect bone fractures is using X-Ray images. X-Ray images require an experienced radiologist to classify them; however, an experienced radiologist is not always available in the ER. An accurate automatic X-Ray image classifier in the ER can help reduce error rates by providing an instant second opinion to the emergency doctor. Deep learning is an emerging trend in artificial intelligence, where an automatic classifier can be trained to classify musculoskeletal images. Image augmentations techniques have proven their us...
Source: Health Information Science and Systems - July 31, 2021 Category: Information Technology Source Type: research

An algorithm for Parkinson ’s disease speech classification based on isolated words analysis
ConclusionThe promising performance yielded by our model confirms the feasibility of automatic assessment of PD using voice recordings. Moreover, a post-hoc analysis of the most relevant features discloses the option of voice processing using a simple smartphone application. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - July 30, 2021 Category: Information Technology Source Type: research

The development of a machine learning algorithm to identify occupational injuries in agriculture using pre-hospital care reports
ConclusionsThe application of the trained algorithm on newer data reduced the volume of records requiring visual inspection by two thirds over the previous keyword search strategy, making it a sustainable and cost-effective way to understand injury trends in agriculture. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - July 29, 2021 Category: Information Technology Source Type: research

Hand tremor detection in videos with cluttered background using neural network based approaches
We examined configurations with different sets of features and neural network based classification models. We compared the performance of different combinations of features and classification models and then selected the combination which provided the highest accuracy of hand tremor detection. We used cross validation to test the accuracy of the trained model predictions. The highest classification accuracy for automatically detecting tremor (vs non tremor ) was 80.6% and this was obtained using Convolutional Neural Network-Long Short-Term Memory and features based on measures of frequency and amplitude change. (Source: He...
Source: Health Information Science and Systems - July 12, 2021 Category: Information Technology Source Type: research

ITEXT-BIO: Intelligent Term EXTraction for BIOmedical analysis
AbstractHere, we introduce ITEXT-BIO, an intelligent process for biomedical domain terminology extraction from textual documents and subsequent analysis. The proposed methodology consists of two complementary approaches, including free and driven term extraction. The first is based on term extraction with statistical measures, while the second considers morphosyntactic variation rules to extract term variants from the corpus. The combination of two term extraction and analysis strategies is the keystone of ITEXT-BIO. These include combined intra-corpus strategies that enable term extraction and analysis either from a singl...
Source: Health Information Science and Systems - July 10, 2021 Category: Information Technology Source Type: research

SpecMEn-DL: spectral mask enhancement with deep learning models to predict COVID-19 from lung ultrasound videos
AbstractLung Ultrasound (LUS) images are considered to be effective for detecting Coronavirus Disease (COVID-19) as an alternative to the existing reverse transcription-polymerase chain reaction (RT-PCR)-based detection scheme. However, the recent literature exhibits a shortage of works dealing with LUS image-based COVID-19 detection. In this paper, a spectral mask enhancement (SpecMEn) scheme is introduced along with a histogram equalization pre-processing stage to reduce the noise effect in LUS images prior to utilizing them for feature extraction. In order to detect the COVID-19 cases, we propose to utilize the SpecMEn ...
Source: Health Information Science and Systems - July 9, 2021 Category: Information Technology Source Type: research

A novel information sharing framework for people living with type-2 diabetes in the context of a group education program
ConclusionsThe proposed information-sharing framework promotes patients ’ self-management of diabetes, revealing that patient participation in diabetes OHC leads to empowering self-management of their diabetes and in turns shedding some light on how healthcare organizations can improve patients’ information behaviour through OHC provisions. This qualitative study su ggested that the proposed novel framework is perceived as a useful platform to empower diabetic patients in their self-management, extending value of physical group activities. (Source: Health Information Science and Systems)
Source: Health Information Science and Systems - July 7, 2021 Category: Information Technology Source Type: research

Detection of the quality of vital signals by the Monte Carlo Markov Chain (MCMC) method and noise deleting
AbstractVital signal renovation plays an important role in a wide range of applications, including signal analysis and diagnosing diseases through it. Therefore, it is salient to get the main content of the vital signal. In this research, a new approach to the problem of noise removal from vital signals is presented based on random optimization through Monte Carlo Markov Chain (MCMC) sampling. For this purpose, the problem of noise omission from the vital signal is described as a Bayesian squared minimization problem, and considering a non-parametric random approach to solve this problem, the Monte Carlo Markov Chain noise...
Source: Health Information Science and Systems - July 1, 2021 Category: Information Technology Source Type: research