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Work in the Ayse Gurses Lab examines several topics related to human factors, including methods for improving patient safety in the cardiac operating room, care coordination, transitions of care and compliance of providers with evidence-based guidelines. Our team also has an interest in research that explores the working conditions of nurses. We collaborate on studies related to the development of geriatrics health service delivery at all levels of the health system.
Principal Investigator
Department
Research in the Casey Overby Lab focuses on the intersection of public health genomics and biomedical informatics. We’re currently developing applications to support the translation of genomic research to clinical and population-based health care settings. We’re also working to develop knowledge-based ways to use big data — including electronic health records — to improve population health.
Research in the Hadi Kharrazi Lab focuses primarily on contextualizing clinical decision support (CDS) into population health informatics (PHI) to be used at different HIT levels of managed care, including electronic health records (EHRs) and consumer health informatics (CHI) solutions. Our team has modified and regenerated electronic quality measures (eQM) based on PHI-derived CDS to represent a population aspect of the health quality measurements. Through the Center for Population Health IT (CPHIT) at the Bloomberg School of Public Health, we are pursuing PHI research that provides direct population-based CDS to providers, patients and payers.
Researchers in the Harold Lehmann Lab study evidence-based medicine. We are currently examining the informatics infrastructure of research and the secondary use of electronic health record data for research. The team is also evaluating the value of classic evidence reports versus social media versus links to community services for community health workers.
The Mark Dredze Lab investigates topics such as natural language processing, speech, machine learning and intelligent user interfaces. Our team is currently exploring several key health information applications, including information extraction from social media and biomedical and clinical texts. Our recent research in these areas include vaccine communication during the Disneyland measles outbreak; the validity of online drug forums for estimating trends in drug use; and the use of Twitter to examine social rationales for vaccine refusal.
The Jacobs lab is within the Division of Cancer Imaging Research in the Department of Radiology and Radiological Science. The lab translates radiological imaging (MRI/PET/CT) from research to the clinical setting. The Jacobs lab is establishing the use of multi-parametric/multinuclear/modality imaging to monitor treatment response in different cancers and co-developed a new metric for DWI/ADC mapping to discern treatment response. They are developing and implementing a new method for diagnosis of cancer using machine and deep learning to measure different types of lesions. The Jacobs lab is also developing novel segmentation of radiological images using non-linear dimensionality reduction. In addition, we are investigating methods to integrate Radiomics and Informatics and prognostic markers for disease. Other research areas include diagnostic medical physics and novel computer science applications. The medical physics research includes MRI quality assessments, X-ray, fluoroscopy, ultr...asound and applications to therapeutic medical physics. We are developing a residency using the Commission on Accreditation of Medical Physics Education Program in Diagnostic Medical Physics. view more
Researchers in the Robert Greenberg Lab examine anesthesiology and critical care-related topics that include critical airway management, non-invasive fetal monitoring, neural blockade monitoring, pediatric acute pain management, cuffed oropharyngeal airway (COPA), pain informatics, and pediatric pain education and innovation.
The Suchi Saria Lab, part of the Institute for Computational Medicine, explores topics within the fields of machine learning and computational statistics, with a focus on computational solutions for problems in health informatics. Our team investigates the applications of machine learning and computational statistics to domains where one has to draw inferences from observing a complex, real-world system evolve over time. We use Bayesian and probabilistic graphical modeling approaches to address the challenges that emerge with modeling and prediction in real-world temporal systems.
Research conducted in the Todd Dorman Lab examines the use of informatics in intensive care settings as it relates to remote patient monitoring, safety and management strategies. Specific areas of interest include the surgical stress response; aminoglycoside antibiotics; fungal infections; renal failure; pharmacokinetic models of drug administration; and ICU triage and its impact on disaster preparedness.
The William Weiss Lab studies the monitoring, assessment and evaluation of international health care programs, particularly in low- and middle-income countries. Our research uses health informatics, rapid-assessment methods, mobile health applications and surveillance systems to investigate the access to health care—in particular mental health services—in populations experiencing trauma due to violence and natural disaster. Study populations include Syrian refugee children in Jordan, survivors of torture and militant attacks in Southern Iraq and survivors of natural disasters such as the Pakistan floods, the Haiti earthquake and the Indonesian tsunami.
Wilmer Bioinformatics has been mainly focused on ocular informatics. Specifically, the group develops and applies bioinformatics approaches to study gene regulation and signaling networks, with particular but not exclusive attention to the mammalian retina. Understanding the molecular basis of tissue specific gene regulation and signaling will contribute to better prevention, diagnosis and treatment of retinal disease.
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