Critical care units are home to some of the most sophisticated patient technology within hospitals. The result- ing data have the potential to improve our understanding of disease and to improve clinical care. Critically ill patients are an ideal population for clinical database investigations because the value of many treatments and interventions they receive remains largely unproven, and high-quality studies supporting or discouraging speci?c practices are relatively sparse [4]. Standardized critical care guidelines currently in use are dependent on an evidence base that is surprisingly weak considering the amount of data generated in the ICU [13]. The MIT Laboratory for Computational Physiology (LCP) developed and maintains the publicly available Medical Information Mart for Intensive Care (MIMIC), containing highly detailed data associated with 53,423 distinct adult ICU admissions at the Beth Israel Deaconess Medical Center in Boston [21]. MIMIC is now a widely used resource worldwide for clinical research studies, exploratory and validation analyses performed by pharmaceutical and medical technology companies, as well as for university, conference and online courses, tutorials and workshops. LCP recently released the open eICU Collaborative Research Database [24] in collaboration with Philips Healthcare, comprising de-identi?ed clinical data associated with approximately 200,000 critical care admissions to over two hundred hospitals throughout the United States. We now intend to expand the success of our open-access, open-source approach to critical care research by releasing large new intra-operative, emergency department and imaging datasets. Importantly, we have made exciting progress with the global consortium our group is spearheading around the development of high resolution critical care databases. With our assistance, colleagues at Oxford, London, Paris, Sao Paulo, Madrid, and Beijing have made signi?cant progress in building their own versions of MIMIC and transforming them into the OMOP common data model. Multi-center research is challenging, because different institutions collect and store data in (sometimes dras- tically) different formats. The adoption and harmonization of data standards is a critical requirement in order for the data to be properly archived, integrated across institutions, and shared for reuse. This proposal seeks funding to: (a) support and expand our publicly available critical care data resources into new domains including pre-ICU care in the ED and OR, and serial chest X-ray imaging; b) develop the technical infrastructure needed to integrate data from international critical care units; and c) conduct research aimed at understanding and addressing the complexities of using multicenter and federated datasets in the development of predictive and clinical decision support tools, as well as in observational retrospective studies.
Enormous quantities of data are collected from patients in hospital intensive care units, operating rooms and emergency departments, but most of these data are not analyzed to discover new knowledge. This research project will collect, organize and publicly distribute detailed de-identi?ed clinical and physiological data to the worldwide research community and will transform that data into new knowledge to improve patient care. To broaden and enrich the research resource, tools will be developed and processes will be established to facilitate sharing of health data across institutional and national boundaries.
| Johnson, Alistair E W; Aboab, Jerome; Raffa, Jesse D et al. (2018) A Comparative Analysis of Sepsis Identification Methods in an Electronic Database. Crit Care Med 46:494-499 |
| Wu, Joy T; Dernoncourt, Franck; Gehrmann, Sebastian et al. (2018) Behind the scenes: A medical natural language processing project. Int J Med Inform 112:68-73 |
| Stretch, Robert; Della Penna, Nicolás; Celi, Leo A et al. (2018) The authors reply. Crit Care Med 46:e1020-e1021 |
| Tyler, Patrick Donnelly; Rush, Barret; Celi, Leo A (2018) The authors reply. Crit Care Med 46:e730-e731 |
| Rush, Barret; Stone, David J; Celi, Leo Anthony (2018) From Big Data to Artificial Intelligence: Harnessing Data Routinely Collected in the Process of Care. Crit Care Med 46:345-346 |
| Stretch, Robert; Della Penna, Nicolás; Celi, Leo Anthony et al. (2018) Effect of Boarding on Mortality in ICUs. Crit Care Med 46:525-531 |
| Rush, Barret; Wiskar, Katie; Celi, Leo Anthony et al. (2018) Association of Household Income Level and In-Hospital Mortality in Patients With Sepsis: A Nationwide Retrospective Cohort Analysis. J Intensive Care Med 33:551-556 |
| Lehman, Li-Wei H; Mark, Roger G; Nemati, Shamim (2018) A Model-Based Machine Learning Approach to Probing Autonomic Regulation From Nonstationary Vital-Sign Time Series. IEEE J Biomed Health Inform 22:56-66 |
| Piza, Felipe Maia de Toledo; Celi, Leo Anthony; Deliberato, Rodrigo Octavio et al. (2018) Assessing team effectiveness and affective learning in a datathon. Int J Med Inform 112:40-44 |
| Rush, Barret; Celi, Leo Anthony; Stone, David J (2018) Applying machine learning to continuously monitored physiological data. J Clin Monit Comput : |
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