The caBIG(R) Life Science Business Architecture Model

The caBIG(R) Life Science Business Architecture Model.  Lauren Becnel Boyd; Scott P. Hunicke-Smith; Grace A. Stafford; Elaine T. Freund; Michele Ehlman; Uma Chandran; Robert Dennis; Anna T. Fernandez; Stephen Goldstein; David Steffen; Benjamin Tycko; Juli D. Klemm.  Bioinformatics 2011; doi: 10.1093/bioinformatics/btr141

Motivation: Business Architecture Models (BAMs) describe what a business does, who performs the activities, where and when activities are performed, how activities are accomplished, and which data are present. The purpose of a BAM is to provide a common resource for understanding business functions and requirements and to guide software development. The cancer Biomedical Informatics Grid (caBIG®) Life Science BAM (LS BAM) provides a shared understanding of the vocabulary, goals and processes that are common in the business of LS research. Results: Results: LS BAM 1.1 includes 90 goals and 61 people and groups within Use Case and Activity Unified Modeling Language (UML) Diagrams. Here we report on the model's current release, LS BAM 1.1, its utility and usage, and plans for future use and continuing development for future releases.

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The FaceBase Consortium: A Comprehensive Program to Facilitate Craniofacial Research

The FaceBase Consortium: A Comprehensive Program to Facilitate Craniofacial Research. Harry Hochheiser, Bruce J. Aronow, Kristin Artinger, Terri H.Beaty, James F. Brinkley, Yang Chai, David Clouthier, Michael L. Cunningham, Michael Dixon, Leah Rae Donahue, Scott E. Fraser, Junichi Iwata, Mary L. Marazita11, Jeffrey C. Murray, Stephen Murray, John Postlethwait, Steven Potter, Linda Shapiro, Richard Spritz, Axel Visel, Seth M. Weinberg and Paul A. Trainor*, for the FaceBase Consortium.

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Data-Driven Modeling of Usual Clinical Care

Clinical care is complex and often fast paced. Preventable medical errors can and do occur, as has been well documented in recent years. Clinical guidelines and rule-based alerts provide clinical decision support that is intended to reduce medical errors. These methods are driven by expert knowledge. As such, they tend to focus on high impact areas in which medical errors are either prevalent, serious, or both. However, the coverage of such methods is relatively narrow.

Clinical Predictive Modeling

Clinical care involves making many predictions under uncertainty, including risk assessment, diagnosis, prognosis and therapeutic management. The better those predictions can be made, the better clinical care is likely to be. The increasing availability and richness of electronic health records (EHRs) are increasing the opportunities for developing and deploying computer-based clinical prediction methods. Such methods can serve as key components of computer-based decision support systems. The data in EHRs can be used to construct prediction models using machine learning methods.

Health Informatics for the Underserved

Gerald Douglas, PhD is leading research efforts that will contribute to the elimination of health disparities, improve health care quality, encourage the adoption of personal wellness strategies, and provide support for the development and advancement of underserved communities. Dr. Douglas implemented several innovative approaches to using technology in health care, including the use of a low-power, robust and inexpensive touch screen workstation for clinicians at the point of care in Malawi, Africa.


Over many years, Roger S. Day, ScD has studied how computational and modeling tools could help people create better biological understanding, then apply it to better individual treatment decisions.

Feasibility of Using a Telemedicine Medication Delivery Unit for Older Adults that Require Medication Assistance During Transition from Hospital to Home

Funding Agency: 
Pennsylvania Department of Aging
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10/07/2010 to 10/06/2011

Care transition interventions have been successful in reducing medication-related problems and associated rehospitalization primarily by focusing on medication reconciliation conducted by trained healthcare professionals.  The goal of this project is to assess the feasibility of using a telemedicine medication delivery unit rather than trained healthcare professionals for frail older adults that require medication assistance in their home immediately following hospitalization.

Dental Informatics and Oral Health Translational Research

The Center for Informatics in Oral Health Translational Research (CIOHTR) at the School of Dental Medicine, University of Pittsburgh, is affiliated with DBMI. Dr. Heiko Spallek, Executive Director, and Dr. Tanja Bekhuis, Director of Oral Health Translational Methods, are members of the DBMI Training Program Faculty. They encourage multidisciplinary collaboration among trainees and faculty interested in dental informatics and translational research.

Short-and-long-term costs of laparoscopic colectomy are significantly less than open colectomy

Eisenberg DP, Wey J, Bao PQ, Saul M, Watson AR, Schraut WH, Lee KK, James Moser A, Hughes SJ. Short-and-long-term costs of laparoscopic colectomy are significantly less than open colectomy. Surgery Endoscopy 2010 Feb 21 (epub) PMID: 20174941

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Influence of medications and diagnoses on fall risk in psychiatric inpatients

Lavsa SM, Fabian TJ, Saul MI, Corman SL, Coley KC. Influence of medications and diagnoses on fall risk in psychiatric inpatients. American Journal of Health System Pharmacy 2010 Aug;67(15):1274-80. PMID: 20651318.

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Lavsa SM, Fabian TJ, Saul MI, Corman SL, Coley KC