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Dubey S, Verma DK, Kumar M. Real-time infectious disease endurance indicator system for scientific decisions using machine learning and rapid data processing. PeerJ Comput Sci 2024; 10:e2062. [PMID: 39145255 PMCID: PMC11323025 DOI: 10.7717/peerj-cs.2062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 04/25/2024] [Indexed: 08/16/2024]
Abstract
The SARS-CoV-2 virus, which induces an acute respiratory illness commonly referred to as COVID-19, had been designated as a pandemic by the World Health Organization due to its highly infectious nature and the associated public health risks it poses globally. Identifying the critical factors for predicting mortality is essential for improving patient therapy. Unlike other data types, such as computed tomography scans, x-radiation, and ultrasounds, basic blood test results are widely accessible and can aid in predicting mortality. The present research advocates the utilization of machine learning (ML) methodologies for predicting the likelihood of infectious disease like COVID-19 mortality by leveraging blood test data. Age, LDH (lactate dehydrogenase), lymphocytes, neutrophils, and hs-CRP (high-sensitivity C-reactive protein) are five extremely potent characteristics that, when combined, can accurately predict mortality in 96% of cases. By combining XGBoost feature importance with neural network classification, the optimal approach can predict mortality with exceptional accuracy from infectious disease, along with achieving a precision rate of 90% up to 16 days before the event. The studies suggested model's excellent predictive performance and practicality were confirmed through testing with three instances that depended on the days to the outcome. By carefully analyzing and identifying patterns in these significant biomarkers insightful information has been obtained for simple application. This study offers potential remedies that could accelerate decision-making for targeted medical treatments within healthcare systems, utilizing a timely, accurate, and reliable method.
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Affiliation(s)
- Shivendra Dubey
- Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India
| | - Dinesh Kumar Verma
- Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India
| | - Mahesh Kumar
- Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India
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Chen MY, Cao MQ, Xu TY. Progress in the application of artificial intelligence in skin wound assessment and prediction of healing time. Am J Transl Res 2024; 16:2765-2776. [PMID: 39114681 PMCID: PMC11301465 DOI: 10.62347/myhe3488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 05/22/2024] [Indexed: 08/10/2024]
Abstract
Since the 1970s, artificial intelligence (AI) has played an increasingly pivotal role in the medical field, enhancing the efficiency of disease diagnosis and treatment. Amidst an aging population and the proliferation of chronic disease, the prevalence of complex surgeries for high-risk multimorbid patients and hard-to-heal wounds has escalated. Healthcare professionals face the challenge of delivering safe and effective care to all patients concurrently. Inadequate management of skin wounds exacerbates the risk of infection and complications, which can obstruct the healing process and diminish patients' quality of life. AI shows substantial promise in revolutionizing wound care and management, thus enhancing the treatment of hospitalized patients and enabling healthcare workers to allocate their time more effectively. This review details the advancements in applying AI for skin wound assessment and the prediction of healing timelines. It emphasizes the use of diverse algorithms to automate and streamline the measurement, classification, and identification of chronic wound healing stages, and to predict wound healing times. Moreover, the review addresses existing limitations and explores future directions.
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Affiliation(s)
- Ming-Yao Chen
- Department of Anesthetic Pharmacology, School of Anesthesiology, Second Military Medical University/Naval Medical UniversityShanghai 200433, China
| | - Ming-Qi Cao
- Department of Anesthetic Pharmacology, School of Anesthesiology, Second Military Medical University/Naval Medical UniversityShanghai 200433, China
- College of Basic Medicine, Second Military Medical University/Naval Medical UniversityShanghai 200433, China
| | - Tian-Ying Xu
- Department of Anesthetic Pharmacology, School of Anesthesiology, Second Military Medical University/Naval Medical UniversityShanghai 200433, China
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Anjum M, Min H, Ahmed Z. Trivial State Fuzzy Processing for Error Reduction in Healthcare Big Data Analysis towards Precision Diagnosis. Bioengineering (Basel) 2024; 11:539. [PMID: 38927776 PMCID: PMC11200935 DOI: 10.3390/bioengineering11060539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/16/2024] [Accepted: 05/20/2024] [Indexed: 06/28/2024] Open
Abstract
There is a significant public health concern regarding medical diagnosis errors, which are a major cause of mortality. Identifying the root cause of these errors is challenging, and even if one is identified, implementing an effective treatment to prevent their recurrence is difficult. Optimization-based analysis in healthcare data management is a reliable method for improving diagnostic precision. Analyzing healthcare data requires pre-classification and the identification of precise information for precision-oriented outcomes. This article introduces a Cooperative-Trivial State Fuzzy Processing method for significant data analysis with possible derivatives. Trivial State Fuzzy Processing operates on the principle of fuzzy logic-based processing applied to structured healthcare data, focusing on mitigating errors and uncertainties inherent in the data. The derivatives are aided by identifying and grouping diagnosis-related and irrelevant data. The proposed method mitigates invertible derivative analysis issues in similar data grouping and irrelevance estimation. In the grouping and detection process, recent knowledge of the diagnosis progression is exploited to identify the functional data for analysis. Such analysis improves the impact of trivial diagnosis data compared to a voluminous diagnosis history. The cooperative derivative states under different data irrelevance factors reduce trivial state errors in healthcare big data analysis.
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Affiliation(s)
- Mohd Anjum
- Department of Computer Engineering, Aligarh Muslim University, Aligarh 202002, India;
| | - Hong Min
- School of Computing, Gachon University, Seongnam 13120, Republic of Korea
| | - Zubair Ahmed
- Department of Zoology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
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Foucher J, Azizi L, Öijerstedt L, Kläppe U, Ingre C. The usage of population and disease registries as pre-screening tools for clinical trials, a systematic review. Syst Rev 2024; 13:111. [PMID: 38654383 PMCID: PMC11040983 DOI: 10.1186/s13643-024-02533-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 04/12/2024] [Indexed: 04/25/2024] Open
Abstract
OBJECTIVE This systematic review aims to outline the use of population and disease registries for clinical trial pre-screening. MATERIALS AND METHODS The search was conducted in the time period of January 2014 to December 2022 in three databases: MEDLINE, Embase, and Web of Science Core Collection. References were screened using the Rayyan software, firstly based on titles and abstracts only, and secondly through full text review. Quality of the included studies was assessed using the List of Included Studies and quality Assurance in Review tool, enabling inclusion of publications of only moderate to high quality. RESULTS The search originally identified 1430 citations, but only 24 studies were included, reporting the use of population and/or disease registries for trial pre-screening. Nine disease domains were represented, with 54% of studies using registries based in the USA, and 62.5% of the studies using national registries. Half of the studies reported usage for drug trials, and over 478,679 patients were identified through registries in this review. Main advantages of the pre-screening methodology were reduced financial burden and time reduction. DISCUSSION AND CONCLUSION The use of registries for trial pre-screening increases reproducibility of the pre-screening process across trials and sites, allowing for implementation and improvement of a quality assurance process. Pre-screening strategies seem under-reported, and we encourage more trials to use and describe their pre-screening processes, as there is a need for standardized methodological guidelines.
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Affiliation(s)
- Juliette Foucher
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
- Department of Neurology, Karolinska University Hospital, Stockholm, Sweden.
| | - Louisa Azizi
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Linn Öijerstedt
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neurology, Karolinska University Hospital, Stockholm, Sweden
| | - Ulf Kläppe
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neurology, Karolinska University Hospital, Stockholm, Sweden
| | - Caroline Ingre
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neurology, Karolinska University Hospital, Stockholm, Sweden
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Sundaram A, Subramaniam H, Ab Hamid SH, Mohamad Nor A. An adaptive data-driven architecture for mental health care applications. PeerJ 2024; 12:e17133. [PMID: 38563009 PMCID: PMC10984189 DOI: 10.7717/peerj.17133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 02/27/2024] [Indexed: 04/04/2024] Open
Abstract
Background In the current era of rapid technological innovation, our lives are becoming more closely intertwined with digital systems. Consequently, every human action generates a valuable repository of digital data. In this context, data-driven architectures are pivotal for organizing, manipulating, and presenting data to facilitate positive computing through ensemble machine learning models. Moreover, the COVID-19 pandemic underscored a substantial need for a flexible mental health care architecture. This architecture, inclusive of machine learning predictive models, has the potential to benefit a larger population by identifying individuals at a heightened risk of developing various mental disorders. Objective Therefore, this research aims to create a flexible mental health care architecture that leverages data-driven methodologies and ensemble machine learning models. The objective is to proficiently structure, process, and present data for positive computing. The adaptive data-driven architecture facilitates customized interventions for diverse mental disorders, fostering positive computing. Consequently, improved mental health care outcomes and enhanced accessibility for individuals with varied mental health conditions are anticipated. Method Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, the researchers conducted a systematic literature review in databases indexed in Web of Science to identify the existing strengths and limitations of software architecture relevant to our adaptive design. The systematic review was registered in PROSPERO (CRD42023444661). Additionally, a mapping process was employed to derive essential paradigms serving as the foundation for the research architectural design. To validate the architecture based on its features, professional experts utilized a Likert scale. Results Through the review, the authors identified six fundamental paradigms crucial for designing architecture. Leveraging these paradigms, the authors crafted an adaptive data-driven architecture, subsequently validated by professional experts. The validation resulted in a mean score exceeding four for each evaluated feature, confirming the architecture's effectiveness. To further assess the architecture's practical application, a prototype architecture for predicting pandemic anxiety was developed.
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Affiliation(s)
- Aishwarya Sundaram
- Institute for Advanced Studies, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Hema Subramaniam
- Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Siti Hafizah Ab Hamid
- Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Azmawaty Mohamad Nor
- Department of Educational Psychology and Counselling, Faculty of Education, Universiti Malaya, Kuala Lumpur, Malaysia
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Bachofner Y, Seifert A, Sepahniya S, Fabian C. Exploring online health information seeking and sharing among older adults: a mini-review about acceptance, potentials, and barriers. Front Digit Health 2024; 6:1336430. [PMID: 38313862 PMCID: PMC10834747 DOI: 10.3389/fdgth.2024.1336430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 01/11/2024] [Indexed: 02/06/2024] Open
Abstract
Online health information seeking (OHIS) is understood by health care, health promotion, and disease prevention experts as a resource for healthy aging. It is particularly relevant for older adults since this population can benefit significantly from the accessibility and convenience of online health platforms and health information. Nevertheless, empirical findings regarding the acceptance, potentials, and barriers of OHIS among older adults are limited. This mini-review aims to explore the level of acceptance of OHIS, including passive reading of information and active interactions with peers, among the older population. Furthermore, it examines the potentials and barriers associated with such practices. The findings ultimately emphasize the evolving landscape of internet health information exploration among older adults and the potential advantages and challenges that may arise, especially in the context of active interactions with peers.
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Affiliation(s)
- Yves Bachofner
- Institute for Social Work and Health, School of Social Work, University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland
| | - Alexander Seifert
- Institute for Integration and Participation, School of Social Work, University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland
| | - Samin Sepahniya
- Institute for Social Work and Health, School of Social Work, University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland
| | - Carlo Fabian
- Institute for Social Work and Health, School of Social Work, University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland
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Kumar P, Paul RK, Roy HS, Yeasin M, Ajit, Paul AK. Big Data Analysis in Computational Biology and Bioinformatics. Methods Mol Biol 2024; 2719:181-197. [PMID: 37803119 DOI: 10.1007/978-1-0716-3461-5_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Abstract
Advancements in high-throughput technologies, genomics, transcriptomics, and metabolomics play an important role in obtaining biological information about living organisms. The field of computational biology and bioinformatics has experienced significant growth with the advent of high-throughput sequencing technologies and other high-throughput techniques. The resulting large amounts of data present both opportunities and challenges for data analysis. Big data analysis has become essential for extracting meaningful insights from the massive amount of data. In this chapter, we provide an overview of the current status of big data analysis in computational biology and bioinformatics. We discuss the various aspects of big data analysis, including data acquisition, storage, processing, and analysis. We also highlight some of the challenges and opportunities of big data analysis in this area of research. Despite the challenges, big data analysis presents significant opportunities like development of efficient and fast computing algorithms for advancing our understanding of biological processes, identifying novel biomarkers for breeding research and developments, predicting disease, and identifying potential drug targets for drug development programs.
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Affiliation(s)
- Prakash Kumar
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
| | - Ranjit Kumar Paul
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
| | - Himadri Shekhar Roy
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
| | - Md Yeasin
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
| | - Ajit
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
| | - Amrit Kumar Paul
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
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Khan S, Biswas MR, Shah Z. Longitudinal analysis of behavioral factors and techniques used to identify vaccine hesitancy among Twitter users: Scoping review. Hum Vaccin Immunother 2023; 19:2278377. [PMID: 37981842 PMCID: PMC10760397 DOI: 10.1080/21645515.2023.2278377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 10/29/2023] [Indexed: 11/21/2023] Open
Abstract
While vaccines have played a pivotal role in the fight against infectious diseases, individuals engage in online resources to find vaccine-related support and information. The benefits and consequences of these online peers are unclear and mainly cause a behavioral shift in user sentiment toward vaccination. This scoping review aims to identify the community and individual factors that longitudinally influence public behavior toward vaccination. The secondary aim is to gain insight into techniques and methodologies used to extract these factors from Twitter data. We followed PRISMA-ScR guidelines to search various online repositories. From this search process, a total of 28 most relevant articles out of 705 relevant studies. Three main themes emerged including individual and community factors influencing public attitude toward vaccination, and techniques employed to identify these factors. Anti-vax, Pro-vax, and neutral are the major communities, while misinformation, vaccine campaign, and user demographics are the common individual factors assessed during this reviewing process. Twitter user sentiment (positive, negative, and neutral) and emotions (fear, trust, sadness) were also discussed to identify the intentions to accept or refuse vaccines. SVM, LDA, BERT are the techniques used for topic modeling, while Louvain, NodeXL, and Infomap algorithms are used for community detection. This research is notable for being the first systematic review that emphasizes the dearth of longitudinal studies and the methodological and underlying practical constraints underpinning the lucrative implementation of an explainable and longitudinal behavior analysis system. Moreover, new possible research directions are suggested for the researchers to perform accurate human behavior analysis.
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Affiliation(s)
- Sulaiman Khan
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Md. Rafiul Biswas
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Zubair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
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Son YS, Kwon KH. Utilization of smart devices and the evolution of customized healthcare services focusing on big data: a systematic review. Mhealth 2023; 10:7. [PMID: 38323151 PMCID: PMC10839508 DOI: 10.21037/mhealth-23-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 10/24/2023] [Indexed: 02/08/2024] Open
Abstract
Background Currently, smart devices can prevent diseases by continuously collecting user information and providing health-related feedback. Smart devices big data provide personalized, faster, and more accurate health care. By examining existing studies, we suggest a new healthcare evolution and health promotion through information technology (IT) convergence. A big data systematic review examined the evolution of new health care and their potential for health promotion by monitoring physical activities, preventing diseases, and analyzing health data smart devices. Methods Therefore, this evaluates whether a new healthcare industry combining smart devices and big-data-based customized health care services can promote health. This study searched PubMed, Google Scholar, Scopus, and Research Information Sharing Service (RISS) for keywords related to big data, smart devices, healthcare, customized health services, health apps, and mobile health. This study comprised 43 of 453 publications from 2007 to 2023. Among them, a total of 43 articles were successfully completed in this study using the PRISMA flowchart in the final stage. Results Smart devices centered on big data enable personalized health care, and app technologies that promote well-being to prepare for aging society have many applications in clinical, prevention, public health, and rehabilitation settings. Smart devices and tailored healthcare services using big data to inform individuals about exercise, health status, diagnosis, and health information will expand into major sectors. By reviewing previous studies, the convergence of the IT technology field, which allows you to easily identify individual health and receive faster and more accurate medical services through customized health care services, has future-oriented values as, new health care services evolve. The systematic review of big data herein can monitor physical activity and prevent diseases using smart devices, thus promoting a healthy lifestyle. Conclusions Smart devices that analyze data to provide personal exercise and health conditions, checkups, and information, are making our lives easier. The information service using big data will continue to evolve into a personalized management service and provide basic healthcare data as it grows into an expected industry in the future.
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Affiliation(s)
- Youn Sun Son
- Division of Beauty Arts Care, Department of Practical Arts, Graduate School of Culture and Arts, Dongguk University, Seoul, Korea
| | - Ki Han Kwon
- College of General Education, Kookmin University, Seoul, Korea
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