Towards a systems approach for chronic diseases, based on health state modeling.

Rising pressure from chronic diseases means that we need to learn how to deal with challenges at a different level, including the use of that better connect across fragments, such as disciplines, stakeholders, institutions, and technologies. By learning from progress in leading areas of health innovation (including oncology and AIDS), as well as complementary indications (Alzheimer's disease), I try to extract the most enabling innovation paradigms, and discuss their extension to additional areas of application within a . To facilitate such work, a Precision, P4 or Systems Medicine platform is proposed, which is centered on the representation of that enable the definition of time in the vision to provide Modeling of such should allow iterative optimization, as longitudinal human data accumulate. This platform is designed to facilitate the discovery of links between opportunities related to a) the modernization of diagnosis, including the increased use of omics profiling, b) patient-centric approaches enabled by , including and connected devices, c) increasing understanding of the pathobiological, clinical and health economic aspects of disease progression stages, d) design of new interventions, including therapies as well as preventive measures, including sequential intervention approaches. Probabilistic of health states, e.g. those used for health economic analysis, are discussed as a simple starting point for the platform. A path towards extension into other indications, data types and uses is discussed, with a focus on and relevant pathobiology.