Data for healthcare applications are typically customized for specific purposes but are often difficult to access due to high costs and privacy concerns. Rather than prepare separate datasets for individual applications, we propose a novel approach: building a general-purpose generative model applicable to virtually any type of healthcare application. This generative model encompasses a broad range of human attributes, including age, sex, anthropometric measurements, blood components, physical performance metrics, and numerous healthcare-related questionnaire responses. To achieve this goal, we integrated the results of multiple clinical studies into a unified training dataset and developed a generative model to replicate its characteristics. The model can estimate missing attribute values from known attribute values and generate synthetic datasets for various applications. Our analysis confirmed that the model captures key statistical properties of the training dataset, including univariate distributions and bivariate relationships. We demonstrate the practical utility of the model through multiple real-world applications, illustrating its potential impact on predictive, preventive, and personalized medicine.

KB, YS, MS, SK, and AK are employees of Kao Corporation (Tokyo, Japan). KO, NC, ZG, HI, MY, YO, HO, KA, SY, YS and SM are employees of Preferred Networks Incorporated (Tokyo, Japan). HM is an executive fellow of Kao Corporation and a senior advisor of Preferred Networks Incorporated.

https://www.researchprotocols.org/2023/1/e47024

The primary sponsor of this study is Kao Corporation (Tokyo, Japan). None of the authors have received any specific funding for this study. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

IRB of Kao Corporation gave ethical approvals for this work IRB of Preferred Networks, Inc. gave ethical approvals for this work

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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The data are not publicly available owing to ethical restrictions. Owing to the nature of this research, the participants of this study did not agree to share their data publicly, and thus, supporting data are not available. The statistical properties of the data are approximated in the model and are available via an application program interface.