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Clinical and Translational Informatics

Share Last Updated: January 09, 2018


The Biomedical Informatics Department is both a leader and a collaborator in a variety of research projects that call for acquisition, storage, retrieval and analysis of clinical and biological datasets. These projects are driven by the requirements of our colleagues, who are involved in clinical work, basic science, or healthcare and university IT. They conduct hypothesis-driven and exploratory biomedical research, lead initiatives to improve the quality of healthcare, and coordinate the development of research IT resources at Emory and elsewhere.

Working with computer science and engineering co-investigators at Emory and Georgia Tech, our research and development teams often implement ideas through creation of  production-quality software systems. Our projects include:

  • new methods for accessing and querying clinical databases and data warehouses,
  • integration and analysis of diverse biological and clinical datasets,
  • development of enhanced disease registries.
  • advanced machine learning-based predictive analytics techniques for early prediction of clinical events
  • application of deep learning and reinforcement learning to optimization of treatment strategies

Related Projects

Analytic Information Warehouse (AIW)

ACTSI

Related Papers

 

Shamim Nemati, Andre Holder, Fereshteh Razmi, Matthew D. Stanley, Gari D. Clifford, Timothy Buchman. 2016. “An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU .” Crit Care Med. 2017 Dec 26;PubMed PMID: 29286945.

 Nemati, Shamim, Mohammad M. Ghassemi, and Gari D. Clifford. 2016. “Optimal Medication Dosing from Suboptimal Clinical Examples: A Deep Reinforcement Learning Approach.” 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, 2016, pp. 2978-2981. doi: 10.1109/EMBC.2016.7591355

Shashikumar SP, Shah AJ, Li Q, Clifford GD, Nemati S. 2016. “ A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology .” InBiomedical & Health Informatics (BHI), 2017 IEEE EMBS International Conference on 2017 Feb 16 (pp. 141-144).

Andrew Post, Tahsin Kurc, Sharath Cholleti, Jingjing Gao, Xia Lin, William Bornstein, Dedra Cantrell, David Levine, Sam Hohmann, Joel Saltz. "The Analytics Information Warehouse (AIW): a Platform for Analytics using Electronic Health Record Data". Journal of Biomedical Informatics. February 2013.

Sharath Cholleti, Andrew Post, Jingjing Gao, Xia Lin, William Bornstein, Dedra Cantrell, Joel Saltz. "Leveraging Derived Data Elements in Data Analytic Models for Understanding and Predicting Hospital Readmissions." AMIA 2012 Annual Symposium. Nov. 3-7, 2012. Chicago, IL.

Awards and Recognition

The Editorial Board of the 2014 IMIA Yearbook of Medical Informatics has selected the following article for listing in the Yearbook as one of the best articles from the literature in the ‘Big Data –Smart Health Strategies’ subfield of medical informatics published in the past year:

Post AR, Kurc T, Cholleti S, Gao J, Lin X, Bornstein W, Cantrell D, Levine D, Hohmann S, Saltz JH. The Analytic Information Warehouse (AIW): a platform for analytics using electronic health record data. J Biomed Inform 2013;46(3):410-424

 

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