Big data handling mechanisms in the healthcare applications: A comprehensive and systematic literature review

Privacy-preserving federated machine learning on FAIR health data: A real-world application

Objective

This paper introduces a privacy-preserving federated machine learning (ML) architecture built upon Findable, Accessible, Interoperable, and Reusable (FAIR) health data. It aims to devise an architecture for executing classification algorithms in a federated manner, enabling collaborative model-building among health data owners without sharing their datasets.

Materials and methods

Utilizing an agent-based architecture, a privacy-preserving federated ML algorithm was developed to create a global predictive model from various local models. This involved formally defining the algorithm in two steps: data preparation and federated model training on FAIR health data and constructing the architecture with multiple components facilitating algorithm execution. The solution was validated by five healthcare organizations using their specific health datasets.

Results

Five organizations transformed their datasets into Health Level 7 Fast Healthcare Interoperability Resources via a common FAIRification workflow and software set, thereby generating FAIR datasets. Each organization deployed a Federated ML Agent within its secure network, connected to a cloud-based Federated ML Manager. System testing was conducted on a use case aiming to predict 30-day readmission risk for chronic obstructive pulmonary disease patients and the federated model achieved an accuracy rate of 87%.

Discussion

The paper demonstrated a practical application of privacy-preserving federated ML among five distinct healthcare entities, highlighting the value of FAIR health data in machine learning when utilized in a federated manner that ensures privacy protection without sharing data.

Conclusion

This solution effectively leverages FAIR datasets from multiple healthcare organizations for federated ML while safeguarding sensitive health datasets, meeting legislative privacy and security requirements.

Toward QoS Monitoring in IoT Edge Devices Driven Healthcare-A Systematic Literature Review

Smart healthcare is altering the delivery of healthcare by combining the benefits of IoT, mobile, and cloud computing. Cloud computing has tremendously helped the health industry connect healthcare facilities, caregivers, and patients for information sharing. The main drivers for implementing effective healthcare systems are low latency and faster response times. Thus, quick responses among healthcare organizations are important in general, but in an emergency, significant latency at different stakeholders might result in disastrous situations. Thus, cutting-edge approaches like edge computing and artificial intelligence (AI) can deal with such problems. A packet cannot be sent from one location to another unless the "quality of service" (QoS) specifications are met. The term QoS refers to how well a service works for users. QoS parameters like throughput, bandwidth, transmission delay, availability, jitter, latency, and packet loss are crucial in this regard. Our focus is on the individual devices present at different levels of the smart healthcare infrastructure and the QoS requirements of the healthcare system as a whole. The contribution of this paper is five-fold: first, a novel pre-SLR method for comprehensive keyword research on subject-related themes for mining pertinent research papers for quality SLR; second, SLR on QoS improvement in smart healthcare apps; third a review of several QoS techniques used in current smart healthcare apps; fourth, the examination of the most important QoS measures in contemporary smart healthcare apps; fifth, offering solutions to the problems encountered in delivering QoS in smart healthcare IoT applications to improve healthcare services.

Urinary Metabolic Distinction of Niemann-Pick Class 1 Disease through the Use of Subgroup Discovery

In this investigation, we outline the applications of a data mining technique known as Subgroup Discovery (SD) to the analysis of a sample size-limited metabolomics-based dataset. The SD technique utilized a supervised learning strategy, which lies midway between classificational and descriptive criteria, in which given the descriptive property of a dataset (i.e., the response target variable of interest), the primary objective was to discover subgroups with behaviours that are distinguishable from those of the complete set (albeit with a differential statistical distribution). These approaches have, for the first time, been successfully employed for the analysis of aromatic metabolite patterns within an NMR-based urinary dataset collected from a small cohort of patients with the lysosomal storage disorder Niemann-Pick class 1 (NPC1) disease (n = 12) and utilized to distinguish these from a larger number of heterozygous (parental) control participants. These subgroup discovery strategies discovered two different NPC1 disease-specific metabolically sequential rules which permitted the reliable identification of NPC1 patients; the first of these involved 'normal' (intermediate) urinary concentrations of xanthurenate, 4-aminobenzoate, hippurate and quinaldate, and disease-downregulated levels of nicotinate and trigonelline, whereas the second comprised 'normal' 4-aminobenzoate, indoxyl sulphate, hippurate, 3-methylhistidine and quinaldate concentrations, and again downregulated nicotinate and trigonelline levels. Correspondingly, a series of five subgroup rules were generated for the heterozygous carrier control group, and 'biomarkers' featured in these included low histidine, 1-methylnicotinamide and 4-aminobenzoate concentrations, together with 'normal' levels of hippurate, hypoxanthine, quinolinate and hypoxanthine. These significant disease group-specific rules were consistent with imbalances in the combined tryptophan-nicotinamide, tryptophan, kynurenine and tyrosine metabolic pathways, along with dysregulations in those featuring histidine, 3-methylhistidine and 4-hydroxybenzoate. In principle, the novel subgroup discovery approach employed here should also be readily applicable to solving metabolomics-type problems of this nature which feature rare disease classification groupings with only limited patient participant and sample sizes available.

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.

Opinion analysis and aspect understanding during covid-19 pandemic using BERT-Bi-LSTM ensemble method

Social media platforms significantly increase general information about disease severity and inform preventive measures among community members. To identify public opinion through tweets on the subject of Covid-19 and investigate public sentiment in the country over the period. This article proposed a novel method for sentiment analysis of coronavirus-related tweets using bidirectional encoder representations from transformers (BERT) bi-directional long short-term memory (Bi-LSTM) ensemble learning model. The proposed approach consists of two stages. In the first stage, the BERT model gains the domain knowledge with Covid-19 data and fine-tunes with sentiment word dictionary. The second stage is the Bi-LSTM model, which is used to process the data in a bi-directional way with context sequence dependency preserving to process the data and classify the sentiment. Finally, the ensemble technique combines both models to classify the sentiment into positive and negative categories. The result obtained by the proposed method is better than the state-of-the-art methods. Moreover, the proposed model efficiently understands the public opinion on the Twitter platform, which can aid in formulating, monitoring and regulating public health policies during a pandemic.

Artificial Intelligence and Human Psychology in Online Transaction Fraud

Banking operations have changed due to technological advancement. On one hand, modernization in technology has facilitated the daily operation of banks; on the other hand, this has also resulted in an increase in the number of cyber-attacks. Artificial Intelligence has introduced new models to detect and prevent cybercrimes. Some fraud has also occurred due to the involvement of employees inside particular organizations. So, this study has focused on both sides: the machine as well as the human. Firstly, the research has focused on fraud diamond theory and has analyzed factors such as rationalization, capabilities, perceived pressure, and perceived opportunities to understand the psychology of the fraudster. Secondly, Artificial Intelligence characteristics, threat exposure, big data management, explainability, cost effectiveness, and risk prediction are evaluated to explore their use in fraud reduction in banks. The research data have been collected from 15 Banks in Pakistan with the help of a questionnaire using five-item Likert scales. The data have been analyzed using IBM SPSS Software. The results gained after correlation and regression analysis proved that Fraud diamond theory and AI characteristics have positive and significant effects on cybercrimes. This study is a great contribution to the banking industry of Pakistan as it provides a complete analysis to control fraud inside organizations by understanding the mindset of fraudsters with the help of fraud diamond theory. At the same time, outside fraud will be handled with the help of Artificial Intelligence. This will result in banks growth, which ultimately boosts the economy of a country.

A sustainable advanced artificial intelligence-based framework for analysis of COVID-19 spread

The idea of sustainability aims to provide a protected operating environment that supports without risking the capacity of coming generations and to satisfy their demands in the future. With the advent of artificial intelligence, big data, and the Internet of Things, there is a tremendous paradigm transformation in how environmental data are managed and handled for sustainable applications in smart cities and societies. The ongoing COVID-19 (Coronavirus Disease) pandemic maintains a mortifying impact on the world population's health. A continuous rise in the number of positive cases produced much stress on governing organizations worldwide, and they are finding it challenging to handle the situation. Artificial Intelligence methods can be extended quite efficiently to monitor the disease, predict the pandemic's growth, and outline policies and strategies to control its transmission or spread. The combination of healthcare, along with big data, and machine learning methods, can improve the quality of life by providing better care services and creating cost-effective systems. Researchers have been using these techniques to fight against the COVID-19 pandemic. This paper emphasizes on the analysis of different factors and symptoms and presents a sustainable framework to predict and detect COVID-19. Firstly, we have collected a data set having different symptoms information of COVID-19. Then, we have explored various machine learning algorithms or methods: including Logistic Regression, Naive Bayes, Decision Tree, Random Forest Classifier, Extreme Gradient Boost, K-Nearest Neighbour, and Support Vector Machine to predict and detect COVID-19 lab results, using different symptoms information. The model might help to predict and detect the long-term spread of a pandemic and implement advanced proactive measures. The findings show that the Logistic Regression and Support Vector Machine outperformed from other machine learning algorithms in terms of accuracy; algorithms exhibit 97.66% and 98% results, respectively.

Enabling Artificial Intelligence for Genome Sequence Analysis of COVID-19 and Alike Viruses

Recent pandemic of COVID-19 (Coronavirus) caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) has been growing lethally with unusual speed. It has infected millions of people and continues a mortifying influence on the global population's health and well-being. In this situation, genome sequence analysis and advanced artificial intelligence techniques may help researchers and medical experts to understand the genetic variants of COVID-19 or SARS-CoV-2. Genome sequence analysis of COVID-19 is crucial to understand the virus's origin, behavior, and structure, which might help produce/develop vaccines, antiviral drugs, and efficient preventive strategies. This paper introduces an artificial intelligence based system to perform genome sequence analysis of COVID-19 and alike viruses, e.g., SARS, middle east respiratory syndrome, and Ebola. The system helps to get important information from the genome sequences of different viruses. We perform comparative data analysis by extracting basic information of COVID-19 and other genome sequences, including information of nucleotides composition and their frequency, tri-nucleotide compositions, count of amino acids, alignment between genome sequences, and their DNA similarity information. We use different visualization methods to analyze these viruses' genome sequences and, finally, apply machine learning based classifier support vector machine to classify different genome sequences. The data set of different virus genome sequences are obtained from an online publicly accessible data center repository. The system achieves good classification results with an accuracy of 97% for COVID-19, 96%, SARS, and 95% for MERS and Ebola genome sequences, respectively.

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people's lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.

Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review

The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by implementing strategies maximizing their performance within an uncertain healthcare environment. The efforts, however, have remained insufficient in view of the growing number of admissions and increased severity of the coronavirus disease. Therefore, the primary aim of this paper is to review the literature on process improvement interventions focused on increasing the ED response to the current COVID-19 outbreak to delineate future research lines based on the gaps detected in the practical scenario. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform a review containing the research papers published between December 2019 and April 2021 using ISI Web of Science, Scopus, PubMed, IEEE, Google Scholar, and Science Direct databases. The articles were further classified taking into account the research domain, primary aim, journal, and publication year. A total of 65 papers disseminated in 51 journals were concluded to satisfy the inclusion criteria. Our review found that most applications have been directed towards predicting the health outcomes in COVID-19 patients through machine learning and data analytics techniques. In the overarching pandemic, healthcare decision makers are strongly recommended to integrate artificial intelligence techniques with approaches from the operations research (OR) and quality management domains to upgrade the ED performance under social-economic restrictions.

A Framework for Pandemic Prediction Using Big Data Analytics

IoT (Internet of Things) devices and smart sensors are used in different life sectors, including industry, business, surveillance, healthcare, transportation, communication, and many others. These IoT devices and sensors produce tons of data that might be valued and beneficial for healthcare organizations if it becomes subject to analysis, which brings big data analytics into the picture. Recently, the novel coronavirus pandemic (COVID-19) outbreak is seriously threatening human health, life, production, social interactions, and international relations. In this situation, the IoT and big data technologies have played an essential role in fighting against the pandemic. The applications might include the rapid collection of big data, visualization of pandemic information, breakdown of the epidemic risk, tracking of confirmed cases, tracking of prevention levels, and adequate assessment of COVID-19 prevention and control. In this paper, we demonstrate a health monitoring framework for the analysis and prediction of COVID-19. The framework takes advantage of Big data analytics and IoT. We perform descriptive, diagnostic, predictive, and prescriptive analysis applying big data analytics using a novel disease real data set, focusing on different pandemic symptoms. This work's key contribution is integrating Big Data Analytics and IoT to analyze and predict a novel disease. The neural network-based model is designed to diagnose and predict the pandemic, which can facilitate medical staff. We predict pandemic using neural networks and also compare the results with other machine learning algorithms. The results reveal that the neural network performs comparatively better with an accuracy rate of 99%.

Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

Purpose

The proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.

Design/methodology/approach

Fora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.

Findings

BD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.

Research limitations/implications

The proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.

Originality/value

There are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

Information system for epidemic control: a computational solution addressing successful experiences and main challenges

Purpose

The SARS-CoV-2 pandemic has caused a major impact on worldwide public health and economics. The lessons learned from the successful attempts to contain the pandemic escalation revealed that the wise usage of contact tracing and information systems can widely help the containment work of any contagious disease. In this context, this paper investigates other researches on this domain, as well as the main issues related to the practical implementation of such systems and specifies a technical solution.

Design/methodology/approach

The proposed solution is based on the automatic identification of relevant contacts between infected or suspected people with susceptible people; inference of contamination risk based on symptoms history, user navigation records and contact information; real-time georeferenced information of population density of infected or suspect people; and automatic individual social distancing recommendation calculated through the individual contamination risk and the worsening of clinical condition risk.

Findings

The solution was specified, prototyped and evaluated by potential users and health authorities. The proposed solution has the potential of becoming a reference on how to coordinate the efforts of health authorities and the population on epidemic control.

Originality/value

This paper proposed an original information system for epidemic control which was applied for the SARS-CoV-2 pandemic and could be easily extended to other epidemics.

Data Handling for E-Mental Health Professionals

Digital psychiatry and e-mental health have proliferated and permeated vastly in the current landscape of mental health care provision. The COVID-19 crisis has accelerated this digital transformation, and changes that usually take many years to translate into clinical practice have been implemented in a matter of weeks. These have outpaced the checks and balances that would typically accompany such changes, which has brought into focus a need to have a proper approach for digital data handling. Health care data is sensitive, and is prone to hacking due to the lack of stringent protocols regarding its storage and access. Mental health care data need to be more secure due to the stigma associated with having a mental health condition. Thus, there is a need to emphasize proper data handling by mental health professionals, and policies to ensure safeguarding patient's privacy are required. The aim of useful, free, and fair use of mental health care data for clinical, business, and research purposes should be balanced with the need to ensure the data is accessible to only those who are authorized. Systems and policies should be in place to ensure that data storage, access, and disposal are systematic and conform to data safety norms.

An efficient reachability query based pruning algorithm in e-health scenario

We propose a Disease-Symptom graph database for our mobile-assisted e-healthcare application. A large Disease-Symptom graph is stored in the cloud and accessed using mobile devices over the Internet. Query and search are the fundamental operations of graph databases. However, while searching the Disease-Symptom graph for making preliminary diagnosis of diseases, queries become complex due to the complex structure of data and also queries are too hard to write and interpret. Moreover, it is not possible to access the graph frequently due to limited bandwidth of the network, transmission delay, and higher cost. Subgraph generation or pruning algorithm for appropriate inputs is one of the solutions to this problem. In this paper, we propose an efficient pruning algorithm by introducing a new approach to decompose the Disease-Symptom graph into a series of symptom trees (ST). All the Symptom trees are merged to build a pruned subgraph which is our requirement. We demonstrate the efficiency and effectiveness of our pruning algorithm both analytically and empirically and validate on Disease-Symptom graph database, as well as other real graph databases. Also a comparison is done with an efficient existing reachability based Chain Cover algorithm after modifying it ChainCoverPrune as pruning algorithm. These two algorithms are tested for storage and access parametric measures for querying the synthetic and real directed databases to show the efficiency of the proposed algorithm.

Multiparametric Monitoring in Equatorian Tomato Greenhouses (III): Environmental Measurement Dynamics

World population growth currently brings unequal access to food, whereas crop yields are not increasing at a similar rate, so that future food demand could be unmet. Many recent research works address the use of optimization techniques and technological resources on precision agriculture, especially in large demand crops, including climatic variables monitoring using wireless sensor networks (WSNs). However, few studies have focused on analyzing the dynamics of the environmental measurement properties in greenhouses. In the two companion papers, we describe the design and implementation of three WSNs with different technologies and topologies further scrutinizing their comparative performance, and a detailed analysis of their energy consumption dynamics is also presented, both considering tomato greenhouses in the Andean region of Ecuador. The three WSNs use ZigBee with star topology, ZigBee with mesh topology (referred to here as DigiMesh), and WiFi with access point topology. The present study provides a systematic and detailed analysis of the environmental measurement dynamics from multiparametric monitoring in Ecuadorian tomato greenhouses. A set of monitored variables (including CO2, air temperature, and wind direction, among others) are first analyzed in terms of their intrinsic variability and their short-term (circadian) rhythmometric behavior. Then, their cross-information is scrutinized in terms of scatter representations and mutual information analysis. Based on Bland⁻Altman diagrams, good quality rhythmometric models were obtained at high-rate sampling signals during four days when using moderate regularization and preprocessing filtering with 100-coefficient order. Accordingly, and especially for the adjustment of fast transition variables, it is appropriate to use high sampling rates and then to filter the signal to discriminate against false peaks and noise. In addition, for variables with similar behavior, a longer period of data acquisition is required for the adequate processing, which makes more precise the long-term modeling of the environmental signals.