Abstract

Predictive analytics in health is an emerging transdisciplinary field utilizing techniques from computer science (eg, machine learning, signal processing), statistics, clinical medicine, and social and behavioral sciences to predict individual and group-level health outcomes [1, 2]. It involves the collection, merging, and analysis of multiple types of individual-level data such as electronic health records, publiclyavailable administrative data, mobile phone activity, and increasingly, passive sensing technologies [3]. With these advances, concerns have emerged regarding data privacy, ownership, and the risk/benefits related to predicting sensitive, individual-level health outcomes and behaviors [4]. This is especially relevant in low-income, racial/ethnic minority populations that often have limited trust in and access to research opportunities [5, 6]. Team science approaches prioritizing broad stakeholder engagement in research leadership may facilitate advances in predictive analytics by integrating knowledge and priorities across disciplines and perspectives.
As noted by the Institute of Medicine [7] and the National Institutes of Health [8], team science is a collaborative, transdisciplinary approach recommended for accelerating development of clinical translational science into public health impact. Team science addresses complex, multi-faceted questions by including scientists from diverse disciplines, and in some models, the end-users of biomedical research (eg, community members, patients, clinicians, healthcare systems, payers). To foster effective team science, the Institute of Medicine report included several key

Metadata

publication

Journal of Clinical and Translational Science 2, 178-182, 2018

year

2018

publication date

2018

authors

Armen C Arevian, Doug Bell, Mark Kretzman, Connie Kasari, Shrikanth Narayanan, Carl Kesselman, Shinyi Wu, Paul Di Capua, William Hsu, Mathew Keener, Joshua Pevnick, Kenneth B Wells, Bowen Chung

link

https://sail.usc.edu/publications/files/participatory_methods_to_support_team_science_development_for_predictive_analytics_in_health.pdf

resource_link

https://sail.usc.edu/publications/files/participatory_methods_to_support_team_science_development_for_predictive_analytics_in_health.pdf

journal

Journal of Clinical and Translational Science

volume

2

pages

178-182