The SIG Beat
News From and About the NIH Scientific Interest Groups
BCIG: A MIMIC-II Story
Monday, September 23
Natcher Conference Center (Building 45), Room B
The NIH Biomedical Computing Interest Group (BCIG) presents “Knowledge Discovery for Critical Care: A MIMIC-II Story” by Mengling Feng (Harvard–MIT), Thomas Brennan (MIT), and Leo Anthony Celi (Harvard–Beth Israel). The data generated in the process of medical care have historically not just been underused, it has been wasted. This waste was due in part to the difficulty of accessing, organizing, and using data entered on paper charts. In addition, variability in clinical documentation methods and quality made the problem even more challenging.
Without a practical way to systematically capture, analyze, and integrate the information contained in the massive amount of data generated during patient care, medicine has remained largely an ad hoc process in which the disconnected application of individual experiences and subjective preferences continues to thwart continuous improvement and consistent delivery of best practices to all patients.
The intensive-care unit (ICU) presents an especially compelling case for clinical data analysis. The value of many treatments and interventions in the ICU is unproven, and high-quality data supporting or challenging specific practices are embarrassingly sparse. Over the past decade, the Massachusetts Institute of Technology, Beth Israel Deaconess Medical Center (BIDMC), and Philips Healthcare, with support from the National Institute of Biomedical Imaging and Bioinformatics, have partnered to build and maintain the Multi-parameter Intelligent Monitoring in Intensive Care database. This public-access database, which now holds clinical data from more than 40,000 stays in BIDMC ICUs, has been meticulously de-identified and is freely shared online with the research community via PhysioNet. Our vision is for the development of a care system consisting of “clinical informatics without walls,” in which the creation of evidence and clinical decision–support tools is initiated, updated, honed, and enhanced by crowd sourcing. In this collaborative medical culture, knowledge generation would become routine and fully integrated into the clinical workflow. This system would use individual data to benefit the care of populations and population data to benefit the care of individuals.
For more information, contact Jim DeLeo at email@example.com.
This page was last updated on Thursday, April 28, 2022