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Research
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The Big Data to Knowledge (BD2K) Centers Coordination Center
Critical to the success of the NIH BD2K Initiative is a specialized entity for promoting common interests and ensuring sustained impact of innovations birthed by the multiple BD2K Centers of Excellence (COEs). Both the immediate and long-term success of the BD2K Initiative is dependent upon how developed resources and tools are shared, integrated and mobilized, as well as effectively adopted by the broad scientific community. UCLA is home to the BD2K Centers-Coordinating Center (CCC). The ...
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The Center for Domain-Specific Computing
The current Center for Domain-Specific Computing (CDSC) is a collaboration between UCLA, Rice University, Oregon Health Sciences University (OHSU), and Intel Research. The scope of its efforts includes leveraging the research results from our past work, but significantly expanding and extending the research along the following research areas: 1. Accelerator-centric architectures (ACAs) in which novel methodologies and algorithms to automatically extract accelerator building blocks (ABBs) i...
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An Observational Stroke Model for Decision Support
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; and more people are now surviving strokes through earlier intervention with thrombolytic agents and interventional clot retrieval devices. Unfortunately, the rapid development of new drugs and devices in this area has made it difficult to provide treatment guidance for a given pat...
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A Predictive Prognostic Model for Brain Cancer
Each year, almost half of all diagnosed primary brain tumors in the United States are Grade IV glioblastoma multiforme (GBMs). While recent efforts have begun to uncover the genetic pathways involved in this cancer's etiology – and potential methods for treatment – arguably, no specific prognostic model has arisen (and been sufficiently validated) to provide widespread usability and individually tailored predictions about a patient's prognosis, let alone suggest optim...
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The Los Angeles PRISMS Center
Supported by NIH/NIBIB U54 EB022002 (PI: Alex Bui)
The Los Angeles Pediatric Research Integrating Sensor Monitoring Systems (LA PRISMS) Center is fostering the development and application of mobile health (mHealth) technologies that deepen our scientific understanding and clinical management of pediatric conditions. Bringing together leading experts from UCLA and USC in biomedical informatics, computer science, wireless health, environmental science and health, and pediatrics, t...
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Research Maps for Integrating Research and Planning Experiments
Causality is a central concept for both basic science and clinical medicine. In the last few decades, we have seen significant development of mathematical formalisms for modeling causality. Despite the existence of robust and expressive formalisms for causal modeling, such formalisms are surprisingly underused by biologists seeking to identify causal mechanisms and by clinicians seeking to understand the etiology of disease. MII is thus working to adapt state-of-the-art causal-discovery me...
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RUMI: Retrieving Understandable Medical Information
While decision-making for any cancer is complex, this is especially true for lung cancer as its treatment options often involve moderate to significant morbidities, and the disease itself has a high mortality rate. A patient's ability to cope with cancer is influenced by a number of factors, including knowledge about the disease and the resultant sense of empowerment to understand and participate in his/her own healthcare and to make decisions. Increasingly, oncology patients are goin...
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Topic Models for Automatic Summarization of Patient Records
Primary care physicians (PCPs) are responsible for reviewing and understanding a wide spectrum of a patient's medical history in order to make informed decisions regarding care. However, a variety of factors impede this process, including: the increasing complexity and number of diagnostic tests and treatments, health information exchange standards that may add more information to the medical record, and the need to efficiently see more patients in less time. The use of topic models f...
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