Real-world data mining meets clinical practice: Research challenges and perspective

Mandreoli, Federica; Ferrari, Davide; Guidetti, Veronica; Motta, Federico; Missier, Paolo

doi:10.3389/fdata.2022.1021621

Cited by 9 publications

(3 citation statements)

References 41 publications

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“…These challenges include dealing with sparse, scarce and unbalanced medical data and the ethical implications of medical AI tools. The African healthcare context, with its unique disease burden and resource constraints, presents additional difficulties in implementing advanced data mining techniques effectively (Mandreoli et al, 2022).…”

Section: Challenges In Implementing Advanced Data Techniques In Afric...mentioning

confidence: 99%

Functional data analysis in health informatics: A comparative review of developments and applications in the USA and Africa

Nzubechukwu Chukwudum Ohalete,

Oluwatoyin Ayo-Farai,

Chinyere Onwumere

et al. 2024

World J. Adv. Res. Rev.

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This study presents a comprehensive review of Functional Data Analysis (FDA) in Health Informatics, focusing on a comparative analysis between the United States and Africa. The background of the study is rooted in the growing importance of FDA in the healthcare sector, driven by technological advancements and the increasing complexity of health data. The aim of the paper is to explore and contrast the developments, applications and challenges of FDA in health informatics within these two diverse geographical contexts. The scope of the paper encompasses the historical evolution, theoretical frameworks, technological advancements, structural differences in health data management and ethical considerations in both the USA and Africa. Methodologically, the study employs a qualitative analysis framework, integrating a systematic review of relevant literature. This approach facilitates an in-depth examination of the divergent paths and shared challenges in FDA across the two regions. The main findings reveal significant disparities in the evolution, application and effectiveness of FDA in health informatics. The USA demonstrates a leading role with advanced technological integration and innovative applications, while Africa shows resilience and adaptability despite facing challenges of limited resources and infrastructure. The study concludes that while the USA has made significant strides in utilizing data to enhance public health and healthcare delivery, Africa presents a landscape of both challenges and opportunities. The divergence is influenced by disparities in educational infrastructure, access to technological resources and healthcare delivery systems. Recommendations include the need for global collaboration, investment in health informatics education, workforce development and the establishment of global standards for health data analysis and sharing.

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Section: Challenges In Implementing Advanced Data Techniques In Afric...mentioning

confidence: 99%

Functional data analysis in health informatics: A comparative review of developments and applications in the USA and Africa

Nzubechukwu Chukwudum Ohalete,

Oluwatoyin Ayo-Farai,

Chinyere Onwumere

et al. 2024

World J. Adv. Res. Rev.

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“…The enormous differences in the progress of these domains are highlighted by the quick expansion and broad acceptance of AI, which is not always accompanied by an equivalent comprehend of algorithms and outcomes. This discrepancy clarifies the medical community's opposition to incorporating AI into Healthcare procedures [2,3].…”

Section: Introductionmentioning

confidence: 98%

Exploring trends and autonomy levels of adaptive business intelligence in healthcare: A systematic review

Lopes,

Faria,

Santos

2024

PLoS ONE

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Objective In order to comprehensively understand the characteristics of Adaptive Business Intelligence (ABI) in Healthcare, this study is structured to provide insights into the common features and evolving patterns within this domain. Applying the Sheridan’s Classification as a framework, we aim to assess the degree of autonomy exhibited by various ABI components. Together, these objectives will contribute to a deeper understanding of ABI implementation and its implications within the Healthcare context. Methods A comprehensive search of academic databases was conducted to identify relevant studies, selecting AIS e-library (AISel), Decision Support Systems Journal (DSSJ), Nature, The Lancet Digital Health (TLDH), PubMed, Expert Systems with Application (ESWA) and npj Digital Medicine as information sources. Studies from 2006 to 2022 were included based on predefined eligibility criteria. PRISMA statements were used to report this study. Results The outcomes showed that ABI systems present distinct levels of development, autonomy and practical deployment. The high levels of autonomy were essentially associated with predictive components. However, the possibility of completely autonomous decisions by these systems is totally excluded. Lower levels of autonomy are also observed, particularly in connection with prescriptive components, granting users responsibility in the generation of decisions. Conclusion The study presented emphasizes the vital connection between desired outcomes and the inherent autonomy of these solutions, highlighting the critical need for additional research on the consequences of ABI systems and their constituent elements. Organizations should deploy these systems in a way consistent with their objectives and values, while also being mindful of potential adverse effects. Providing valuable insights for researchers, practitioners, and policymakers aiming to comprehend the diverse levels of ABI systems implementation, it contributes to well-informed decision-making in this dynamic field.

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“…Recent advances in Big Data and Artificial Intelligence (AI) have provided researchers with unprecedented capability to model complexities of real-world EHR data (1)(2)(3)(4)(5). Yet legitimate concerns about patient privacy and ethical AI have limited the availability of EHR data within the broader AI research community (6)(7)(8).…”

Section: Introductionmentioning

confidence: 99%

Synthetic Health Data Can Augment Community Research Efforts to Better Inform the Public During Emerging Pandemics

Prasanna,

Jing,

Plopper

et al. 2023

Preprint

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The COVID-19 pandemic had disproportionate effects on the Veteran population due to the increased prevalence of medical and environmental risk factors. Synthetic electronic health record (EHR) data can help meet the acute need for Veteran population-specific predictive modeling efforts by avoiding the strict barriers to access, currently present within Veteran Health Administration (VHA) datasets. The U.S. Food and Drug Administration (FDA) and the VHA launched the precisionFDA COVID-19 Risk Factor Modeling Challenge to develop COVID-19 diagnostic and prognostic models; identify Veteran population-specific risk factors; and test the usefulness of synthetic data as a substitute for real data. The use of synthetic data boosted challenge participation by providing a dataset that was accessible to all competitors. Models trained on synthetic data showed similar but systematically inflated model performance metrics to those trained on real data. The important risk factors identified in the synthetic data largely overlapped with those identified from the real data, and both sets of risk factors were validated in the literature. Tradeoffs exist between synthetic data generation approaches based on whether a real EHR dataset is required as input. Synthetic data generated directly from real EHR input will more closely align with the characteristics of the relevant cohort. This work shows that synthetic EHR data will have practical value to the Veterans’ health research community for the foreseeable future.

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Real-world data mining meets clinical practice: Research challenges and perspective

Cited by 9 publications

References 41 publications

Functional data analysis in health informatics: A comparative review of developments and applications in the USA and Africa

Functional data analysis in health informatics: A comparative review of developments and applications in the USA and Africa

Exploring trends and autonomy levels of adaptive business intelligence in healthcare: A systematic review

Synthetic Health Data Can Augment Community Research Efforts to Better Inform the Public During Emerging Pandemics

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