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All Journal Indonesian Journal of Information System JOURNAL OF APPLIED INFORMATICS AND COMPUTING IJID (International Journal on Informatics for Development) Journal of Information Systems and Informatics Indonesian Journal of Innovation and Applied Sciences (IJIAS) The Indonesian Journal of Computer Science
Ndlovu, Belinda
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
A Systematic Review of Post-Quantum Cryptography for Healthcare Data Protection: Performance, Readiness, and Deployment Challenges Ngwenya, Taboka; Ndlovu, Belinda
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11836

Abstract

The traditional cryptographic methods used to protect healthcare data, especially for the long-term storage of medical imaging records, are becoming increasingly threatened by the quick development of quantum computing. The purpose of this study is to assess the challenges, efficacy, and preparedness of integrating Post-Quantum Cryptography (PQC) into healthcare information systems. Twenty peer-reviewed studies published between 2020 and 2025 were analysed following the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) protocol. The review was conducted using a systematic research design that included qualitative thematic synthesis, predetermined eligibility criteria, and database searching. According to the results, lattice-based PQC schemes, specifically, CRYSTALS-Kyber for encryption and CRYSTALS-Dilithium for authentication, show great promise because of their effectiveness, resilience, and suitability for decentralized architectures like blockchain and Internet-of-Medical-Things environments. Nonetheless, the review points out a notable deficiency of empirical assessment in actual healthcare settings, particularly with regard to cloud-based platforms and Picture Archiving and Communication Systems utilized in medical imaging processes. Scalability limitations, intricate key-management specifications, system interoperability restrictions, and the requirement for conformity with regulatory and compliance frameworks are some of the major issues noted. The results indicate that lattice-based PQC schemes have great promise, deployment readiness remains largely at the conceptual and experimental stage, particularly for cloud-based PACS environments. Real-world implementation validation in a healthcare setting has not been achieved.
Explainable Transformer and Machine Learning Models in Predicting Tuberculosis Treatment Outcomes. A Systematic Review Sibanda, Shumirai; Ndlovu, Belinda
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11846

Abstract

Tuberculosis (TB) remains a major health challenge, and predicting treatment outcomes continues to be difficult in real-world settings. Recent advances in Artificial Intelligence (AI), particularly transformer-based models, have shown promise in modelling longitudinal, multimodal, and heterogeneous TB data. However, their clinical adoption is constrained by limited interpretability, fairness concerns, and deployment challenges. This study presents a systematic literature review of explainable transformer and machine learning models used for TB prognosis. Following PRISMA guidelines, searches across ACM, IEEE Xplore, PubMed, and ScienceDirect identified 17 peer-reviewed studies published between 2020 and 2025 that met the inclusion criteria. The review synthesises evidence on predictive performance, explainability techniques, and deployment considerations. Findings indicate that transformer-based and deep learning models generally outperform conventional machine learning approaches on longitudinal and multimodal data. In contrast, traditional models remain competitive for tabular clinical datasets. Explainability approaches are dominated by feature importance methods and SHAP, with limited use of intrinsic transformer interpretability mechanisms. Persistent challenges include data scarcity, limited generalisability, computational overhead, insufficient evaluation of fairness, and weak alignment with real-world TB care workflows. Building on these findings, the study proposes the Explainable Transformer Adoption Model for TB Prognosis (ETAMTB) as a conceptual clinical adoption framework integrating multimodal transformers, explainability layers, clinician-facing interfaces, and deployment enablers. Overall, the review concludes that effective AI adoption in TB care requires balancing predictive performance, interpretability, and equity, and that explainable transformers should currently be viewed as promising but largely experimental tools rather than deployment-ready solutions.
Transformer-Based Models for Electronic Health Records and Omics in Healthcare: A Systematic Literature Review Machemedze, Joshua; Ndlovu, Belinda
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11893

Abstract

Electronic Health Records (EHRs) have become central to modern healthcare. The emergence of transformer-based models has profoundly influenced how EHRs are used for modelling complex, longitudinal data. Integration with omics technologies improves the precision of disease identification and risk assessment during modelling. While several reviews have examined transformers in healthcare broadly, a systematic synthesis focused on their architectural design, empirical performance and integration of EHRs with omics data remains limited. This study presents a systematic literature review of transformer-based models applied to electronic health records (EHRs) and omics data, and of their integration into healthcare. Following PRISMA guidelines, peer-reviewed studies were retrieved from IEEE Xplore, ACM Digital Library, PubMed, and ScienceDirect, resulting in 14 eligible empirical studies published between 2020 and 2025. The review analyses transformer architectures, submodules, application domains, comparative performance, interpretability mechanisms, and limitations. Findings indicate that architectural design drives task-specific advantages in disease prediction, phenotyping, medication recommendation, and omics analysis. The integration of self-attention with deep learning, temporal modelling, and a pre-trained biomedical transformer improves performance. However, most studies remain centred on EHR, with limited empirical integration of omics data. Persistent challenges include limited generalisability, high computational cost, data quality issues, and insufficient interpretability for clinical deployment. The primary contribution of this review lies in synthesising architectural trends and methodological gaps. By consolidating current evidence, the study provides clear directions for the development of explainable, generalisable, and multimodal transformer-based systems in precision healthcare.
Transformer-based Models for Cardiovascular Disease Predictions from Electronic Health Records: A Systematic Review Chikumo, Onayi Theresa; Ndlovu, Belinda
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11899

Abstract

This systematic literature review (SLR) analyses 16 studies published between 2020 and 2025 that applied transformer-based or other machine learning models to predict cardiovascular disease (CVD) using electronic health records (EHRs). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the review ensures transparency in the identification, screening, and quality appraisal of eligible studies. The key findings reveal a rapid shift from traditional machine learning models, such as Random Forest, toward transformer architectures like the Bidirectional Encoder Representation from Transformers for Electronic Health Record (BEHRT) and its variants. These models demonstrate a superior discrimination (Area Under Curve:0.84 to 0.93) due to their capacity to model long-term temporal dependencies. Explainable AI (XAI) tools, such as attention visualisation, were frequently employed, yet clinical interpretability and integration into decision support remain underexplored. The review also highlights opportunities in federated and privacy-preserving learning, multimodal data fusion, and hybrid architectures that integrate transformers with traditional machine learning methods. This review addresses a gap in the past literature by being the first SLR to compare transformer variants for the prediction of CVDs. Other SLRs examined general CVD risk models, but the present SLR analyses interpretability, external validation and methodological limitations to transformer models. The findings of the recent SLR reported challenges that include data-shift limitations, model-poor population generalisation and their limitations to clinical adoption, which highlights the need for more evaluation protocols and clinicians’ interpretability frameworks.
Multi-Agent Retrieval Augmented Generation for Clinical Decision Support: A Systematic Review and Integrative Conceptual Framework Mugambiwa , Tarisai; Ndlovu, Belinda
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11900

Abstract

Multi agent retrieval augmented generation (RAG) systems are increasingly explored as advanced architectures for clinical decision support combining information retrieval, reasoning and verification through coordinated agent interactions. This study systematically reviews applications of agentic and multi agent RAG in clinical decision support systems (CDSS) and synthesizes an integrative conceptual framework linking technical design to technology adoption considerations. Following PRISMA guidelines, searches were conducted from PubMed, IEEE Xplore and ScienceDirect using structured Boolean strings combining terms for multi agent architectures, RAG and CDSS.The search yielded 12 studies published between 2020 and 2025 that met the inclusion criteria. The review synthesises evidence on multi agent role configurations retrieval and reasoning strategies, verification mechanisms and reported clinical contexts. Across studies, dominant challenges include data and corpus limitations retrieval quality dependency, limited clinical validation and computational overhead, alongside governance concerns such as privacy, bias and accountability. Building on the synthesis, we propose a four-agent CDSS framework retriever, reasoner, verifier, safety and map its deployment determinants to Technology Acceptance Model constructs perceived usefulness, perceived ease of use, trust and diffusion of Innovations attributes. The review concludes with design-oriented recommendations for safer, explainable, and adoption-ready multi-agent RAG CDSS, particularly for low-resource contexts.
Co-Authors Chamboko, Hazel Chikumo, Onayi Theresa Chipfumbu, Colletor T. Chizengwe, Kudzaishe Lawal Dube, Sibusisiwe Dube, Yangekile Hadebe, Sanele Kiwa, Fungai Jacqueline Mabikwa, Otis Machemedze, Joshua Maguraushe, Kudakwashe Maitireni, Peter Marambi, Sheltar Muderere, Vimbai Alice Muduva, Martin Mugambiwa , Tarisai Muzenda, Hillary Ncube, Vusumuzi Ndlovu, Sharmaine Ngwazi, Tinashe Ngwenya, Taboka Nyamupaguma, Tanyaradzwa Razo, Charnelle Sibanda, Shumirai Sibanda, Thando Vareta, Passmore