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"Health Informatics"
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COVID-19 prevention and treatment information on the internet: a systematic analysis and quality assessment
ObjectiveTo evaluate the quality of information regarding the prevention and treatment of COVID-19 available to the general public from all countries.DesignSystematic analysis using the ‘Ensuring Quality Information for Patients’ (EQIP) Tool (score 0–36), Journal of American Medical Association (JAMA) benchmark (score 0–4) and the DISCERN Tool (score 16–80) to analyse websites containing information targeted at the general public.Data sourcesTwelve popular search terms, including ‘Coronavirus’, ‘COVID-19 19’, ‘Wuhan virus’, ‘How to treat coronavirus’ and ‘COVID-19 19 Prevention’ were identified by ‘Google AdWords’ and ‘Google Trends’. Unique links from the first 10 pages for each search term were identified and evaluated on its quality of information.Eligibility criteria for selecting studiesAll websites written in the English language, and provides information on prevention or treatment of COVID-19 intended for the general public were considered eligible. Any websites intended for professionals, or specific isolated populations, such as students from one particular school, were excluded, as well as websites with only video content, marketing content, daily caseload update or news dashboard pages with no health information.ResultsOf the 1275 identified websites, 321 (25%) were eligible for analysis. The overall EQIP, JAMA and DISCERN scores were 17.8, 2.7 and 38.0, respectively. Websites originated from 34 countries, with the majority from the USA (55%). News Services (50%) and Government/Health Departments (27%) were the most common sources of information and their information quality varied significantly. Majority of websites discuss prevention alone despite popular search trends of COVID-19 treatment. Websites discussing both prevention and treatment (n=73, 23%) score significantly higher across all tools (p<0.001).ConclusionThis comprehensive assessment of online COVID-19 information using EQIP, JAMA and DISCERN Tools indicate that most websites were inadequate. This necessitates improvements in online resources to facilitate public health measures during the pandemic.
Journal Article
Mistreated : why we think we're getting good health care--and why we're usually wrong
\"Despite all the debate about health care, Americans tend to assume they are in the best of hands when they enter the hospital. This is inaccurate : American health care is in the bottom half of all industrialized countries. This is only the largest in a broad set of misperceptions. We appropriately worry about the security of technology, but fail to see how its absence kills hundreds of people every day from medical errors. We over-value the impact of intervention on saving lives and ignore the 200,000 people who die each year unnecessarily from diseases they did not have to get. We worry that end of life discussions and palliative care will lead to \"death squads,\" when research proves that people actually live not only better, but also longer. We demand modern information technology from our banks, airlines, retailers and hotels, but we passively accept last century's technology in our health care. It's not just patients who get things wrong. Physicians perceive that the dollars they take from drug companies don't alter their prescribing habits, but the data demonstrates that for every dollar the pharmaceutical world spends on doctors, they get $10 in return. Academic researchers deny that their results are influenced by which company funds the work, but in 95% of the cases, the outcome supports the funding source. Dr. Robert Pearl has seen these mistakes from all sides: as a concerned citizen, a patient, a health industry leader, and most importantly, a victim of bureaucracy, whose own father died due in part to medical error. In this book, Pearl explains why misperception is so common in medicine, both for patients and physicians. Solving the challenges of health care today including excessive costs, poor quality and the lack of convenience will require an understanding of this phenomenon, and an approach that aligns health care delivery with up to date information and data. It emphasizes the power of context, and how through integration, prepayment, information technology and physician leadership, superior outcomes can be achieve. It draws on other industries and companies like Amazon and Uber that were able to overcome customer fear, and shift perception, and provides a roadmap for the future\"-- Provided by publisher.
Book
Automated Detection of Alzheimer’s Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques
The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer’s disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure, widely adopted in hospitals to examine cognitive abnormalities. Images are acquired using the T2 imaging sequence. The paradigm consists of a series of quantitative techniques: filtering, feature extraction, Student’s t-test based feature selection, and k-Nearest Neighbor (KNN) based classification. Additionally, a comparative analysis is done by implementing other feature extraction procedures that are described in the literature. Our findings suggest that the Shearlet Transform (ST) feature extraction technique offers improved results for Alzheimer’s diagnosis as compared to alternative methods. The proposed CABD tool with the ST + KNN technique provided accuracy of 94.54%, precision of 88.33%, sensitivity of 96.30% and specificity of 93.64%. Furthermore, this tool also offered an accuracy, precision, sensitivity and specificity of 98.48%, 100%, 96.97% and 100%, respectively, with the benchmark MRI database.
Journal Article
Healthcare big data analytics : computational optimization and cohesive approaches
Highlighting how optimized big data applications can be used for patient monitoring and clinical diagnosis, this book also explores the challenges, opportunities and future research directions, discussing the stages of data collection and pre-processing, as well as the associated challenges and issues in data handling and setup.
Book
Investigating for bias in healthcare algorithms: a sex-stratified analysis of supervised machine learning models in liver disease prediction
ObjectivesThe Indian Liver Patient Dataset (ILPD) is used extensively to create algorithms that predict liver disease. Given the existing research describing demographic inequities in liver disease diagnosis and management, these algorithms require scrutiny for potential biases. We address this overlooked issue by investigating ILPD models for sex bias.MethodsFollowing our literature review of ILPD papers, the models reported in existing studies are recreated and then interrogated for bias. We define four experiments, training on sex-unbalanced/balanced data, with and without feature selection. We build random forests (RFs), support vector machines (SVMs), Gaussian Naïve Bayes and logistic regression (LR) classifiers, running experiments 100 times, reporting average results with SD.ResultsWe reproduce published models achieving accuracies of >70% (LR 71.31% (2.37 SD) – SVM 79.40% (2.50 SD)) and demonstrate a previously unobserved performance disparity. Across all classifiers females suffer from a higher false negative rate (FNR). Presently, RF and LR classifiers are reported as the most effective models, yet in our experiments they demonstrate the greatest FNR disparity (RF; −21.02%; LR; −24.07%).DiscussionWe demonstrate a sex disparity that exists in published ILPD classifiers. In practice, the higher FNR for females would manifest as increased rates of missed diagnosis for female patients and a consequent lack of appropriate care. Our study demonstrates that evaluating biases in the initial stages of machine learning can provide insights into inequalities in current clinical practice, reveal pathophysiological differences between the male and females, and can mitigate the digitisation of inequalities into algorithmic systems.ConclusionOur findings are important to medical data scientists, clinicians and policy-makers involved in the implementation medical artificial intelligence systems. An awareness of the potential biases of these systems is essential in preventing the digital exacerbation of healthcare inequalities.
Journal Article
Designing intelligent healthcare systems, products, and services using disruptive technologies and health informatics
\"This book offers both theoretical and practical application-based chapters on Computational Intelligence, IoT, Blockchain, Cloud, and Big Data Analytics and presents novel technical studies on designing intelligent healthcare systems, product, and services. It offers conceptual and visionary works comprising hypothetical and speculative scenarios and will also include recently developed disruptive holistic techniques in healthcare and monitoring of physiological data. The book will also provide metaheuristic computational intelligent based algorithms for analysis, diagnosis, and prevention of disease through disruptive technologies\"-- Provided by publisher.
Book
Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals
Depression affects large number of people across the world today and it is considered as the global problem. It is a mood disorder which can be detected using electroencephalogram (EEG) signals. The manual detection of depression by analyzing the EEG signals requires lot of experience, tedious and time consuming. Hence, a fully automated depression diagnosis system developed using EEG signals will help the clinicians. Therefore, we propose a deep hybrid model developed using convolutional neural network (CNN) and long-short term memory (LSTM) architectures to detect depression using EEG signals. In the deep model, temporal properties of the signals are learned with CNN layers and the sequence learning process is provided through the LSTM layers. In this work, we have used EEG signals obtained from left and right hemispheres of the brain. Our work has provided 99.12% and 97.66% classification accuracies for the right and left hemisphere EEG signals respectively. Hence, we can conclude that the developed CNN-LSTM model is accurate and fast in detecting the depression using EEG signals. It can be employed in psychiatry wards of the hospitals to detect the depression using EEG signals accurately and thus aid the psychiatrists.
Journal Article