Synthetic Patient Data Generation and Evaluation in Disease Prediction Using Small and Imbalanced Datasets

Rodriguez-Almeida, Antonio; Fabelo, Himar; Ortega, Samuel; Déniz, Alejandro; Balea-Fernández, Francisco; Quevedo, Eduardo; Soguero-Ruíz, Cristina; Wägner, Ana María; Callicó, Gustavo M.

doi:10.1109/jbhi.2022.3196697

Cited by 16 publications

(6 citation statements)

References 49 publications

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“…This approach is in line with the AI field pivoting away from the common perception that it is almost synonymous with ‘big data’ [ 36 ] to a focus on small data to deliver valuable biological insights. This could be achieved using synthetic data [ 37 ], artificially generated data that imitate the characteristics and patterns of real-world data without containing actual information from individual observations. Such data are produced using algorithms to simulate the statistical properties, distributions and relationships present in ‘authentic’ clinical datasets.…”

Section: Using Artificial Intelligence To Drive a Precision Medicine ...mentioning

confidence: 99%

Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data

Teixeira,

Battelino,

Carlsson

et al. 2024

Diabetologia

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The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare providers, regulatory authorities and payers, screening programmes need to show that the testing variables allow accurate risk prediction and that individualised risk-informed monitoring plans are established, as well as operational feasibility, cost-effectiveness and acceptance at population level. Artificial intelligence (AI) has the potential to contribute to solving these issues, starting with the identification and stratification of at-risk individuals. ASSET (AI for Sustainable Prevention of Autoimmunity in the Society; www.asset.healthcare) is a public/private consortium that was established to contribute to research around screening for type 1 diabetes and particularly to how AI can drive the implementation of a precision medicine approach to disease prevention. ASSET will additionally focus on issues pertaining to operational implementation of screening. The authors of this article, researchers and clinicians active in the field of type 1 diabetes, met in an open forum to independently debate key issues around screening for type 1 diabetes and to advise ASSET. The potential use of AI in the analysis of longitudinal data from observational cohort studies to inform the design of improved, more individualised screening programmes was also discussed. A key issue was whether AI would allow the research community and industry to capitalise on large publicly available data repositories to design screening programmes that allow the early detection of individuals at high risk and enable clinical evaluation of preventive therapies. Overall, AI has the potential to revolutionise type 1 diabetes screening, in particular to help identify individuals who are at increased risk of disease and aid in the design of appropriate follow-up plans. We hope that this initiative will stimulate further research on this very timely topic. Graphical Abstract

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Section: Using Artificial Intelligence To Drive a Precision Medicine ...mentioning

confidence: 99%

Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data

Teixeira,

Battelino,

Carlsson

et al. 2024

Diabetologia

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“…In the domain of medical data analysis, this study provided a framework by utilizing algorithms for generating synthetic data on eight different tabular medical datasets [37]. With an eye on ensuring the fidelity of the synthetic data, the research used valid statistical metrics to measure the integrity of the data.…”

Section: Healthcarementioning

confidence: 99%

Exploring Innovative Approaches to Synthetic Tabular Data Generation

Papadaki,

Vrahatis,

Kotsiantis

2024

Electronics

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The rapid advancement of data generation techniques has spurred innovation across multiple domains. This comprehensive review delves into the realm of data generation methodologies, with a keen focus on statistical and machine learning-based approaches. Notably, novel strategies like the divide-and-conquer (DC) approach and cutting-edge models such as GANBLR have emerged to tackle a spectrum of challenges, spanning from preserving intricate data relationships to enhancing interpretability. Furthermore, the integration of generative adversarial networks (GANs) has sparked a revolution in data generation across sectors like healthcare, cybersecurity, and retail. This review meticulously examines how these techniques mitigate issues such as class imbalance, data scarcity, and privacy concerns. Through a meticulous analysis of evaluation metrics and diverse applications, it underscores the efficacy and potential of synthetic data in refining predictive models and decision-making software. Concluding with insights into prospective research trajectories and the evolving role of synthetic data in propelling machine learning and data-driven solutions across disciplines, this work provides a holistic understanding of the transformative power of contemporary data generation methodologies.

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“…Synthetic data generation is an emerging field that needs to be studied further but has shown promising results in the medical field. Using a framework based on synthetic data generation combined with different machine learning classifiers, Rodriguez-Almeida et al [128] tested 8 medical tabular data sets and assessed the feasibility of using synthetic data to preserve data integrity and maintain classification performance using AI algorithms in the medical domain.…”

Section: Ai Implementation In Mhealthmentioning

confidence: 99%

Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges

Deniz-Garcia¹,

Fabelo²,

Rodriguez-Almeida³

et al. 2023

J Med Internet Res

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The use of artificial intelligence (AI) and big data in medicine has increased in recent years. Indeed, the use of AI in mobile health (mHealth) apps could considerably assist both individuals and health care professionals in the prevention and management of chronic diseases, in a person-centered manner. Nonetheless, there are several challenges that must be overcome to provide high-quality, usable, and effective mHealth apps. Here, we review the rationale and guidelines for the implementation of mHealth apps and the challenges regarding quality, usability, and user engagement and behavior change, with a special focus on the prevention and management of noncommunicable diseases. We suggest that a cocreation-based framework is the best method to address these challenges. Finally, we describe the current and future roles of AI in improving personalized medicine and provide recommendations for developing AI-based mHealth apps. We conclude that the implementation of AI and mHealth apps for routine clinical practice and remote health care will not be feasible until we overcome the main challenges regarding data privacy and security, quality assessment, and the reproducibility and uncertainty of AI results. Moreover, there is a lack of both standardized methods to measure the clinical outcomes of mHealth apps and techniques to encourage user engagement and behavior changes in the long term. We expect that in the near future, these obstacles will be overcome and that the ongoing European project, Watching the risk factors (WARIFA), will provide considerable advances in the implementation of AI-based mHealth apps for disease prevention and health promotion.

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Synthetic Patient Data Generation and Evaluation in Disease Prediction Using Small and Imbalanced Datasets

Cited by 16 publications

References 49 publications

Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data

Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data

Exploring Innovative Approaches to Synthetic Tabular Data Generation

Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges

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