The University of Pittsburgh proposes a five-year renewal of its training program in Biomedical Informatics. The T15 grant is currently entering its 25th consecutive year. Our program is notable for the long and distinguished history of biomedical informatics research in Pittsburgh, the continuous evolution and refinement of our educational programs, the strong institutional commitment to biomedical informatics and our training program, and the rich biomedical and computational research environment in which our training program is set. The program has an administrative home in the Department of Biomedical Informatics (DBMI) within the University of Pittsburgh School of Medicine. DBMI provides space, equipment, and financial support for training program administration, faculty, graduate students and postdoctoral scholars. The program is supported by an interdepartmental core faculty of 29 faculty members, including all 17 faculty members with primary appointments in DBMI. The Training Program Director, a tightly knit leadership group of faculty co- directors, and two experienced staff members support the overall operation of the program. The Pittsburgh BMI Training Program offers research training in all four sub-disciplines of Biomedical Informatics: translational bioinformatics, clinical research informatics, healthcare/clinical informatics, and public health informatics. Additionally, we also offer specialized research training in dental informatics. Students in our T15-funded training program may pursue an MS or PhD in Biomedical Informatics, an MS or PhD in Intelligent Systems - Biomedical Informatics Track, an MD/PhD through the Medical Scientist Training Program, or advanced postdoctoral research. The training program has undergone significant enhancements during the past funding period including a new core curriculum, improved advising structure, improved program evaluation plan, and enhanced efforts to recruit trainee candidates, including candidates from under-represented minorities and disadvantaged backgrounds. Enhancements for the proposed funding period include new advanced graduate seminars, new professional development content, and enhanced training in the responsible conduct of research. We have a strong track record of success in training biomedical informatics researchers in all sub- disciplines. Trainees from our program are publishing research articles in high impact journals in the field, winning national awards for their research, writing successful K grants and individual fellowship awards, and securing research positions in academics, industry and government upon graduation.

Public Health Relevance

This grant proposes the continuation of the university of Pittsburgh biomedical informatics training program. The grant would support predoctoral and postdoctoral students for research training in all four sub-disciplines of biomedical informatics: healthcare/clinical informatics, translational bioinformatics, clinical research informatics, and public health informatics.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Continuing Education Training Grants (T15)
Project #
3T15LM007059-30S1
Application #
9378111
Study Section
Special Emphasis Panel (ZLM1)
Program Officer
Florance, Valerie
Project Start
2016-12-01
Project End
2017-06-30
Budget Start
2016-12-01
Budget End
2017-06-30
Support Year
30
Fiscal Year
2017
Total Cost
Indirect Cost
Name
University of Pittsburgh
Department
Miscellaneous
Type
Schools of Medicine
DUNS #
004514360
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
Schmithorst, Vincent J; Votava-Smith, Jodie K; Tran, Nhu et al. (2018) Structural network topology correlates of microstructural brain dysmaturation in term infants with congenital heart disease. Hum Brain Mapp 39:4593-4610
Ceschin, Rafael; Zahner, Alexandria; Reynolds, William et al. (2018) A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks. Neuroimage 178:183-197
Gertsvolf, Nina; Votava-Smith, Jodie K; Ceschin, Rafael et al. (2018) Association between Subcortical Morphology and Cerebral White Matter Energy Metabolism in Neonates with Congenital Heart Disease. Sci Rep 8:14057
Ding, Michael Q; Chen, Lujia; Cooper, Gregory F et al. (2018) Precision Oncology beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics. Mol Cancer Res 16:269-278
Ogoe, Henry A; Asamani, James A; Hochheiser, Harry et al. (2018) Assessing Ghana's eHealth workforce: implications for planning and training. Hum Resour Health 16:65
Dasgupta, Pritika; VanSwearingen, Jessie; Sejdic, Ervin (2018) ""You can tell by the way I use my walk."" Predicting the presence of cognitive load with gait measurements. Biomed Eng Online 17:122
Fisher, Arielle M; Mtonga, Timothy M; Espino, Jeremy U et al. (2018) User-centered design and usability testing of RxMAGIC: a prescription management and general inventory control system for free clinic dispensaries. BMC Health Serv Res 18:703
Aronis, John M; Millett, Nicholas E; Wagner, Michael M et al. (2017) A Bayesian system to detect and characterize overlapping outbreaks. J Biomed Inform 73:171-181
Chen, Vicky; Paisley, John; Lu, Xinghua (2017) Revealing common disease mechanisms shared by tumors of different tissues of origin through semantic representation of genomic alterations and topic modeling. BMC Genomics 18:105
Liu, Yuzhe; Gopalakrishnan, Vanathi (2017) An Overview and Evaluation of Recent Machine Learning Imputation Methods Using Cardiac Imaging Data. Data (Basel) 2:

Showing the most recent 10 out of 183 publications