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Big Data in Health Care Information Systems
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Big Data in Health Care Information Systems

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 9744

Special Issue Editors

Dr. Oluwafemi A. Sarumi

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Marburg, 35037 Marburg, Germany
Interests: data science; AI in biomedicine; healthcare analytics; bioinformatics
Dr. Tobore Igbe

E-Mail Website
Guest Editor
Center for Diabetes Technology Department of Psychiatry & Neurobehavioral Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
Interests: clinical decision support systems; expert system; pattern recognition; artificial intelligence; health informatics
Dr. Halleluyah O. Aworinde

E-Mail Website
Guest Editor
Computer Science Department, Bowen University, P.M.B 284 Iwo, Osun State, Nigeria
Interests: machine learning & vision; intelligent systems; data science

Special Issue Information

Dear Colleagues, 

The emergence and usage of high-throughput computing technologies and machines in the medical and biomedical domains have paved the way for the fast curation of and easy access to a vast biomedical dataset–big data. This big data is awash with a wealth of information that can be harnessed to revolutionize the healthcare information system–using various data science techniques such as deep learning, machine learning, federated learning, and continuous machine learning. Therefore, in this Special Issue, we aim to collect novel ideas on exploiting the big data available in healthcare domains to develop more robust and responsive information systems for healthcare practitioners that will enhance the delivery of quality and more effective treatments for patients. We encourage potential authors to submit papers that show how healthcare practitioners can gain new insights and knowledge from the avalanche of data available in the healthcare domain. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Application of machine learning in biomedicine;
  • Application of data science to infectious disease control;
  • Bioinformatics;
  • Federated learning in the healthcare system;
  • Medical informatics;
  • Explainable ML in biomedicine;
  • Artificial intelligence and precision medicine;
  • Application blockchain in EHR systems;
  • Deep learning in biomedicine and bioinformatics;
  • AI-based applications for digital healthcare systems;
  • Transfer learning in healthcare systems;
  • Lifelong machine learning in healthcare information systems.

We look forward to receiving your contributions.

Dr. Oluwafemi A. Sarumi
Dr. Tobore Igbe
Dr. Halleluyah O. Aworinde
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data science
  • health analytics
  • big data
  • bioinformatics

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Published Papers (2 papers)

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Research

16 pages, 382 KiB  
Article
ZeroTrustBlock: Enhancing Security, Privacy, and Interoperability of Sensitive Data through ZeroTrust Permissioned Blockchain
by Pratik Thantharate and Anurag Thantharate
Big Data Cogn. Comput. 2023, 7(4), 165; https://doi.org/10.3390/bdcc7040165 - 17 Oct 2023
Cited by 19 | Viewed by 3462
Abstract
With the digitization of healthcare, an immense amount of sensitive medical data are generated and shared between various healthcare stakeholders—however, traditional health data management mechanisms present interoperability, security, and privacy challenges. The centralized nature of current health information systems leads to single points [...] Read more.
With the digitization of healthcare, an immense amount of sensitive medical data are generated and shared between various healthcare stakeholders—however, traditional health data management mechanisms present interoperability, security, and privacy challenges. The centralized nature of current health information systems leads to single points of failure, making the data vulnerable to cyberattacks. Patients also have little control over their medical records, raising privacy concerns. Blockchain technology presents a promising solution to these challenges through its decentralized, transparent, and immutable properties. This research proposes ZeroTrustBlock, a comprehensive blockchain framework for secure and private health information exchange. The decentralized ledger enhances integrity, while permissioned access and smart contracts enable patient-centric control over medical data sharing. A hybrid on-chain and off-chain storage model balances transparency with confidentiality. Integration gateways bridge ZeroTrustBlock protocols with existing systems like EHRs. Implemented on Hyperledger Fabric, ZeroTrustBlock demonstrates substantial security improvements over mainstream databases via cryptographic mechanisms, formal privacy-preserving protocols, and access policies enacting patient consent. Results validate the architecture’s effectiveness in achieving 14,200 TPS average throughput, 480 ms average latency for 100,000 concurrent transactions, and linear scalability up to 20 nodes. However, enhancements around performance, advanced cryptography, and real-world pilots are future work. Overall, ZeroTrustBlock provides a robust application of blockchain capabilities to transform security, privacy, interoperability, and patient agency in health data management. Full article
(This article belongs to the Special Issue Big Data in Health Care Information Systems)
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16 pages, 596 KiB  
Article
Enhancing the Early Detection of Chronic Kidney Disease: A Robust Machine Learning Model
by Muhammad Shoaib Arif, Aiman Mukheimer and Daniyal Asif
Big Data Cogn. Comput. 2023, 7(3), 144; https://doi.org/10.3390/bdcc7030144 - 16 Aug 2023
Cited by 27 | Viewed by 5455
Abstract
Clinical decision-making in chronic disorder prognosis is often hampered by high variance, leading to uncertainty and negative outcomes, especially in cases such as chronic kidney disease (CKD). Machine learning (ML) techniques have emerged as valuable tools for reducing randomness and enhancing clinical decision-making. [...] Read more.
Clinical decision-making in chronic disorder prognosis is often hampered by high variance, leading to uncertainty and negative outcomes, especially in cases such as chronic kidney disease (CKD). Machine learning (ML) techniques have emerged as valuable tools for reducing randomness and enhancing clinical decision-making. However, conventional methods for CKD detection often lack accuracy due to their reliance on limited sets of biological attributes. This research proposes a novel ML model for predicting CKD, incorporating various preprocessing steps, feature selection, a hyperparameter optimization technique, and ML algorithms. To address challenges in medical datasets, we employ iterative imputation for missing values and a novel sequential approach for data scaling, combining robust scaling, z-standardization, and min-max scaling. Feature selection is performed using the Boruta algorithm, and the model is developed using ML algorithms. The proposed model was validated on the UCI CKD dataset, achieving outstanding performance with 100% accuracy. Our approach, combining innovative preprocessing steps, the Boruta feature selection, and the k-nearest neighbors algorithm, along with a hyperparameter optimization using grid-search cross-validation (CV), demonstrates its effectiveness in enhancing the early detection of CKD. This research highlights the potential of ML techniques in improving clinical support systems and reducing the impact of uncertainty in chronic disorder prognosis. Full article
(This article belongs to the Special Issue Big Data in Health Care Information Systems)
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