How predictive medicine leads to solidarity gaps in health

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Abstract

The current shift in healthcare towards AI-driven P4 medicine challenges practices of solidarity, with implications for EU health policy.

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P4 medicine: how systems medicine will transform the healthcare sector and society.

Mauricio Flores, Gustavo Glusman, Kristin Brogaard … (2013)

Ten years ago, the proposition that healthcare is evolving from reactive disease care to care that is predictive, preventive, personalized and participatory was regarded as highly speculative. Today, the core elements of that vision are widely accepted and have been articulated in a series of recent reports by the US Institute of Medicine. Systems approaches to biology and medicine are now beginning to provide patients, consumers and physicians with personalized information about each individual's unique health experience of both health and disease at the molecular, cellular and organ levels. This information will make disease care radically more cost effective by personalizing care to each person's unique biology and by treating the causes rather than the symptoms of disease. It will also provide the basis for concrete action by consumers to improve their health as they observe the impact of lifestyle decisions. Working together in digitally powered familial and affinity networks, consumers will be able to reduce the incidence of the complex chronic diseases that currently account for 75% of disease-care costs in the USA.

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Predictive, personalized, preventive, participatory (P4) cancer medicine.

Richard Friend, Leroy Hood (2011)

Medicine will move from a reactive to a proactive discipline over the next decade--a discipline that is predictive, personalized, preventive and participatory (P4). P4 medicine will be fueled by systems approaches to disease, emerging technologies and analytical tools. There will be two major challenges to achieving P4 medicine--technical and societal barriers--and the societal barriers will prove the most challenging. How do we bring patients, physicians and members of the health-care community into alignment with the enormous opportunities of P4 medicine? In part, this will be done by the creation of new types of strategic partnerships--between patients, large clinical centers, consortia of clinical centers and patient-advocate groups. For some clinical trials it will necessary to recruit very large numbers of patients--and one powerful approach to this challenge is the crowd-sourced recruitment of patients by bringing large clinical centers together with patient-advocate groups.

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A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction

Annette Spooner, Emily Chen, Arcot Sowmya … (2020)

Data collected from clinical trials and cohort studies, such as dementia studies, are often high-dimensional, censored, heterogeneous and contain missing information, presenting challenges to traditional statistical analysis. There is an urgent need for methods that can overcome these challenges to model this complex data. At present there is no cure for dementia and no treatment that can successfully change the course of the disease. Machine learning models that can predict the time until a patient develops dementia are important tools in helping understand dementia risks and can give more accurate results than traditional statistical methods when modelling high-dimensional, heterogeneous, clinical data. This work compares the performance and stability of ten machine learning algorithms, combined with eight feature selection methods, capable of performing survival analysis of high-dimensional, heterogeneous, clinical data. We developed models that predict survival to dementia using baseline data from two different studies. The Sydney Memory and Ageing Study (MAS) is a longitudinal cohort study of 1037 participants, aged 70–90 years, that aims to determine the effects of ageing on cognition. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a longitudinal study aimed at identifying biomarkers for the early detection and tracking of Alzheimer's disease. Using the concordance index as a measure of performance, our models achieve maximum performance values of 0.82 for MAS and 0.93 For ADNI.

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Author and article information

Contributors

Matthias Braun:

ORCID: http://orcid.org/0000-0002-6687-6027

matthias.braun@uni-bonn.de

Journal

Journal ID (nlm-ta): NPJ Digit Med

Journal ID (iso-abbrev): NPJ Digit Med

Title: NPJ Digital Medicine

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2398-6352

Publication date (Electronic): 18 February 2025

Publication date PMC-release: 18 February 2025

Publication date Collection: 2025

Volume: 8

Electronic Location Identifier: 111

Affiliations

Department of Social Ethics, University of Bonn, ( https://ror.org/041nas322) Bonn, Germany

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Matthias Braun http://orcid.org/0000-0002-6687-6027

Article

Publisher ID: 1497

DOI: 10.1038/s41746-025-01497-2

PMC ID: 11836224

PubMed ID: 39966662

SO-VID: c7048062-67bd-41b3-be92-c620b040eaa0

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 12 July 2024

Date accepted : 1 February 2025

Funding

Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);

Award ID: 101076822

Award Recipient : Matthias Braun

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Keywords: ethics,society

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Keywords: ethics, society

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How predictive medicine leads to solidarity gaps in health

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Most cited references 20

P4 medicine: how systems medicine will transform the healthcare sector and society.

Predictive, personalized, preventive, participatory (P4) cancer medicine.

A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction

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Most referenced authors 5,176