Optimizing Stroke Treatment

Annually, it is estimated that more than 795,000 Americans experience a stroke. The severity of neurological damage due to an acute stroke is mitigated by the early restoration of blood flow to the affected area. In this project, new probabilistic modeling methods are being developed to optimally select treatments.


The Center for Domain-Specific Computing (CDSC) is an NSF-InTrans award, focusing on next-generation hardware and software acceleration of algorithms in the biomedical sciences. CDSC is a collaboration between UCLA, Rice University, Oregon Health Sciences University, and Intel Research.


Many of us go online to get information about our medical conditions. But do you really understand the data in your medical record? The Retrieving Understandable Medical Information (RUMI) project aims to provide tailored, context-sensitive information to patients.

Understanding Brain Cancer

Glioblastoma multiforme (GBM) is a deadly cancer, and the current prognosis for patients diagnosed with this disease remains sub-optimal. Our project is looking at how we can better predict the prognosis of a specific individual, and suggest treatments that optimize survival and quality of life.


With the increasing amount of data in the electronic health record, new methods are required to help expedite a healthcare provider’s understanding of a patient’s medical history. The PARSE project explores the use of topic models for summarizing large, unstructured data collections to support PCPs.


Are you an undergraduate interested in biomedical informatics? The MII Research in Informatics Summer Experience (MII RISE) is an opportunity for undergraduates to work with our faculty to gain experience in research and biomedical informatics.


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Research news

As clinical practice moves towards a more quantitative image evaluation to achieve better diagnosis, new needs have emerged in order to objectively evaluate the effect of a decision in disease progression and outcome. Especially when diagnosing and treating brain tumors, it is crucial to have accurate measurements and to know to what degree the variation of the tumor mass represents true disease progression to be able to generate insights about treatment effectiveness. This project centers on increasing the utility of imaging features from magnetic resonance imaging by developing a method for tumor segmentation variability quantification, towards a more objective disease understanding and improved decision making.

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