Literature DB >> 33597683

The Project Baseline Health Study: a step towards a broader mission to map human health.

Kristine Arges1, Themistocles Assimes2, Vikram Bajaj2, Suresh Balu1, Mustafa R Bashir1, Laura Beskow3, Rosalia Blanco1, Robert Califf4, Paul Campbell1, Larry Carin1, Victoria Christian1, Scott Cousins1, Millie Das2, Marie Dockery1, Pamela S Douglas1, Ashley Dunham1, Julie Eckstrand1, Dominik Fleischmann2, Emily Ford1, Elizabeth Fraulo1, John French1, Sanjiv S Gambhir5, Geoffrey S Ginsburg1, Robert C Green6, Francois Haddad2, Adrian Hernandez1, John Hernandez7, Erich S Huang1, Glenn Jaffe1, Daniel King1, Lynne H Koweek1, Curtis Langlotz2, Yaping J Liao2, Kenneth W Mahaffey2, Kelly Marcom1, William J Marks2,4, David Maron2, Reid McCabe1, Shannon McCall1, Rebecca McCue2, Jessica Mega4, David Miller4, Lawrence H Muhlbaier1, Rajan Munshi2, L Kristin Newby1, Ezra Pak-Harvey1, Bray Patrick-Lake1, Michael Pencina1, Eric D Peterson1, Fatima Rodriguez2, Scarlet Shore4, Svati Shah1, Steven Shipes1, George Sledge2, Susie Spielman2, Ryan Spitler2, Terry Schaack8, Geeta Swamy1, Martin J Willemink2, Charlene A Wong1.   

Abstract

The Project Baseline Health Study (PBHS) was launched to map human health through a comprehensive understanding of both the health of an individual and how it relates to the broader population. The study will contribute to the creation of a biomedical information system that accounts for the highly complex interplay of biological, behavioral, environmental, and social systems. The PBHS is a prospective, multicenter, longitudinal cohort study that aims to enroll thousands of participants with diverse backgrounds who are representative of the entire health spectrum. Enrolled participants will be evaluated serially using clinical, molecular, imaging, sensor, self-reported, behavioral, psychological, environmental, and other health-related measurements. An initial deeply phenotyped cohort will inform the development of a large, expanded virtual cohort. The PBHS will contribute to precision health and medicine by integrating state of the art testing, longitudinal monitoring and participant engagement, and by contributing to the development of an improved platform for data sharing and analysis.

Year:  2020        PMID: 33597683     DOI: 10.1038/s41746-020-0290-y

Source DB:  PubMed          Journal:  NPJ Digit Med        ISSN: 2398-6352


  1 in total

1.  Doctor AI: Predicting Clinical Events via Recurrent Neural Networks.

Authors:  Edward Choi; Mohammad Taha Bahadori; Andy Schuetz; Walter F Stewart; Jimeng Sun
Journal:  JMLR Workshop Conf Proc       Date:  2016-12-10
  1 in total

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