Research Topics
- Machine learning, data mining and artificial intelligence
- Virtual Pediatric Intensive Care Unit (VPICU)
Machine Learning, Data Mining and Artificial Intelligence
Researchers are using advanced computational techniques and artificial intelligence to detect categories in raw medical data from disparate data sources, allowing the most recent information about large numbers of critically ill children to be data mined. This enables researchers to find similarities between a patient and historical populations with known outcomes to provide decision support for diagnosis, management and therapy, while aiding disease detection, directing patient care, and ensuring quality and safety.
Randall Wetzel, MD, has been awarded a Challenge Grant in Health and Science Research by the National Institutes of Health (NIH) National Library of Medicine to research advanced computational frameworks for decision support in critically ill children. The study aims to provide information for patient management by providing outcomes data on previous patients with similar diagnosis and symptoms.
Virtual PICU
Every day doctors observe the outcomes of the treatment they provide for thousands of patients. These experiences serve as practical therapeutic trials, conducted on a patient-by-patient basis. With the increasing adoption of electronic health care records, hospitals and clinics are collecting data from thousands of these experiments daily. However, much of it remains inaccessible and unsearchable, and providers are unable to learn from this vast accumulated experience. The Laura P. and Leland K. Whittier Virtual Pediatric Intensive Care Unit (VPICU) aims to create a common information space for the international community of caregivers providing critical care for children. The VPICU will build extended databases to allow inter-institutional data sharing to create extended, multi-center knowledge bases. The VPICU will also improve education, quality enhancement and telemedicine in this field.
