Both the genomics revolution and the widespread implementation of electronic medical records have led to an explosion of biomedical Big Data. By Combining Novel Datasets to Understand Illness Trajectory (CONDUIT) our lab seeks to utilize large-scale multimodal data repositories to conduct research in critical illness and injury.
The care of patients in Intensive Care Units (ICUs) is a resource-intensive task, accounting for a substantial proportion of total health care costs. With few positive results emerging from randomized controlled trials, new approaches to research in this area are needed. The emergence of genome science, wearable technologies, and massive medical datasets presents an unprecedented opportunity to develop such novel approaches, based on understanding critical illness. Concurrently, advances in data science and the emergence of Big Data resources offer powerful new ways to merge genomic, clinical, and physiologic data, and derive actionable evidence for clinical practice.
Our research aims include the development novel datasets with the capacity to merge biomedical data types, as well as the analytic tools needed to translate raw data into medical knowledge. Our objectives include the development of knowledge representations of biomedical data that will allow researchers at different sites to share data, and collaborate more effectively. We also develop methods to collect and validate clinical data from electronic medical records, as well as waveform data from bedside monitors, and to combine these using novel data structures. Properly leveraged, the immense density of ICU data stand to enhance the precision of care provided to critically ill patients.