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"Computational Medicine: A Gateway to Computer-Aided Decision-Making"
"Computational Medicine: A Gateway to Computer-Aided Decision-Making"
The Biomedical and Clinical Informatics Lab (BCIL) is committed to developing advanced computational methods to address complex problems in medicine and biology.
The Biomedical and Clinical Informatics Lab (BCIL) is committed to developing advanced computational methods to address complex problems in medicine and biology.
Research projects are driven by a wide range of federal and private organizations, including the American Heart Association, the National Science Foundation, the National Institutes of Health, the US Department of Defense, the Michigan Center for Integrative Research in Critical Care (MCIRCC) , and Michigan Medicine.
The philosophy behind our efforts is threefold:
The philosophy behind our efforts is threefold:
Enjoy your work.
Satisfy your scientific curiosity by developing and applying innovative and sophisticated methodologies.
Help save lives by creating new and novel solutions!
The focus of the research conducted in our lab is in the development of computer-assisted decision-making systems.
The focus of the research conducted in our lab is in the development of computer-assisted decision-making systems.
Using insightful bio-markers and underlying patterns extracted from raw data via signal processing, image processing, and machine learning techniques, these systems can provide real-time, on-the-fly treatment recommendations and outcome predictions at every stage of care. By assisting physicians in this manner, patient care can be improved, costs can be reduced and expertise can be provided in cases where medical experts are not immediately available.
Example applications of these systems are in cases of traumatic injury and hemorrhagic shock; where all available structured and unstructured health data of the patient are processed as it becomes available to produce diagnostic, predictive, and prescriptive analysis of patient conditions in real time. The raw data we analyze includes time-series waveform data such as ECG, PPG, and ABP; medical imaging data such as CT, X-Ray, MRI, and fMRI; and all other relevant information available within the electronic health record (EHR) such as demographics, medications, and lab results.
These recommendations and predictions are then communicated to physicians and caregivers via simple, effective, purpose designed user-interfaces in an easy-to-understand and interactive format, along with clear and descriptive visualizations, which communicate the computed outputs and the reasoning behind it.
Computational medicine encompasses the techniques that apply mathematical tools to extract important diagnostic information from biomedical and biological data. Data mining techniques can then be applied to train expert systems on existing data and classify new examples. The core research and development of the lab is primarily focused on developing novel mathematical techniques of signal processing, image processing, and machine learning towards developing novel solutions for medicine.
However, due to the size, complexity, and disparate nature of medical data, we also use and develop a variety of techniques for data wrangling, data imputation, data aggregation, alignment, multi-core/multi-threaded processing, and distributed computing, which help us develop effective, robust, and commercial grade medical decision support systems.
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