Systems medicine is a flourishing discipline that stimulates widespread research interests. The highlighted map in Fig.1 presents a health ecosystem, which is a connected community of living and non-living elements whose cooperation forms a complex space of relations, decisions and actions.
The wealth of health- and disease-related data from biomedical, environmental, clinical and social studies is the product of such ecosystem, which continues to generate new challenges together with opportunities for collaborative research.
Since massive heterogeneous information is generated, innovative methodologies for analysis, modelling and visualization of these "Big Data" are imperative needs. A blend of data structures obtained from multi-type evidences need to be merged, assimilated and interpreted. Such multi-sourced data call for linking technologies, and require cross-matching methods to ensure analysis at all the embedded scales, from individual to community.
Active data are derived from various measurements (electronic health records, real time monitoring of behavior, socio-demographic factors, etc.). Passive data are also relevant, and derived from sensors measuring disease signs and/or unexpected disturbances otherwise not detectable (during sleep, physical activity, stress conditions etc.), panel and linked database resources, environmental influences etc.
In order to guarantee a synergistic integration of the ecosystem-related data into high-impact health solutions, three concepts need to be connected. Thus, social, technical and methodological interoperability and sustainability are the real bottlenecks for the challenges ahead.
Big Data for Health, as depicted in Fig. 1, suggests two main directions:
1. Optimizing multiple information entry points to the Health Ecosystem
2. Breaking down the ecosystem into component parts, in an attempt to understand the specific contributions and criticalities, and reconcile them within the Health context.
It can be safely stated that these two directions are currently pursued. Looking ahead, a shift of paradigm is required to further progress along the lines indicated in Fig.2.
The horizontal dimension is a matter of information flow from active and passive data, multi-type evidences, high-throughput experiments, etc. reflecting vast and disparate amounts of structured and unstructured inputs.
The vertical dimension indicates the need of integrating information pervasively generated from all technology-driven and geo-localized open sourced health ecosystem components.
Solutions covering data warehousing & management, and aimed at information governance are thus required.
Among the expected impacts, the following are prioritized:
- Paving Crossroads Digital Medicine
- Science of Healthy Aging
- Medicine as Information Science
- Leveraging Actionable Crowdsourced Health
Authors:Enrico Capobianco, M.Giovanna Trivella IFC CNR Pisa
Keywords: Health; Big Data; Systems Medicine.