We propose to create a massively scalable toolkit to enable large, multi-center Patient-centered Information Commons (PIC) at local, regional and, national scale, where the focus is the alignment of all available biomedical data per individual. Such a Commons is a prerequisite for conducting the large-N, Big Data, longitudinal studies essential for understanding causation in the Precision Medicine framework while simultaneously addressing key complexities of Patient Centric Outcome Research studies required under ACA (Affordable Care Act). This agenda entails the four following aims:
Aim 1 : Create an individual patient data identification and retrieval toolkit that is robust across distributed data of wide variety and geographically scattered. Robustness with regard to a variety of organizational structures and national scalability is emphasized.
Aim 2 : Generate a complete diagnostic and prognostic 'data' picture of a patient across multiple sources of data, some of which are noisy and sparse.
Aim 3. Enable robust decentralized computation on large-scale data with the Patient-centered Information Commons Big Data Science Platform (PIC-DSP), particularly in configurations where data are generated in locations other than where computational resources are most available.
Aim 4 : Create three patient-centered information commons instances (PICIs) to test all aspects of the toolkit developed. We have selected neurodevelopmental disorders as our first PICI, as it fulfills several criteria (wide variety of data types and scales, collaborator engagement, multiple healthcare institutions, and opportunity to rigorously test and refine features of the tool).
The proposed Patient-centered Information Commons will allow investigators to link and analyze patient level data on a large scale in population size but also data variety: from clinical health record, to prospectively gathered research data, survey and administrative data, genomic, imaging, socio-behavioral, and environmental data. This will allow these investigators to achieve new levels of precision in diagnosis and prognosis as well as measuring the conduct and quality of medical practice.
| Kerpedjiev, Peter; Abdennur, Nezar; Lekschas, Fritz et al. (2018) HiGlass: web-based visual exploration and analysis of genome interaction maps. Genome Biol 19:125 |
| Kothari, Cartik; Wack, Maxime; Hassen-Khodja, Claire et al. (2018) Phelan-McDermid syndrome data network: Integrating patient reported outcomes with clinical notes and curated genetic reports. Am J Med Genet B Neuropsychiatr Genet 177:613-624 |
| Kartoun, Uri; Aggarwal, Rahul; Beam, Andrew L et al. (2018) Development of an Algorithm to Identify Patients with Physician-Documented Insomnia. Sci Rep 8:7862 |
| Diao, James A; Kohane, Isaac S; Manrai, Arjun K (2018) Biomedical informatics and machine learning for clinical genomics. Hum Mol Genet 27:R29-R34 |
| Hejblum, Boris P; Cui, Jing; Lahey, Lauren J et al. (2018) Association Between Anti-Citrullinated Fibrinogen Antibodies and Coronary Artery Disease in Rheumatoid Arthritis. Arthritis Care Res (Hoboken) 70:1113-1117 |
| Can, Anil; Castro, Victor M; Dligach, Dmitriy et al. (2018) Elevated International Normalized Ratio Is Associated With Ruptured Aneurysms. Stroke 49:2046-2052 |
| Yu, Sheng; Ma, Yumeng; Gronsbell, Jessica et al. (2018) Enabling phenotypic big data with PheNorm. J Am Med Inform Assoc 25:54-60 |
| Can, Anil; Rudy, Robert F; Castro, Victor M et al. (2018) Low Serum Calcium and Magnesium Levels and Rupture of Intracranial Aneurysms. Stroke 49:1747-1750 |
| Wilson, Ander; Zigler, Corwin M; Patel, Chirag J et al. (2018) Model-averaged confounder adjustment for estimating multivariate exposure effects with linear regression. Biometrics 74:1034-1044 |
| Gutiérrez-Sacristán, Alba; Guedj, Romain; Korodi, Gabor et al. (2018) Rcupcake: an R package for querying and analyzing biomedical data through the BD2K PIC-SURE RESTful API. Bioinformatics 34:1431-1432 |
Showing the most recent 10 out of 60 publications
