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  2. Knowledge discovery

Knowledge discovery

Machine learning (ML) and reinforcement learning algorithms are making it possible to uncover new insights from the EHR and related observational datasets. This area of research aims to develop and apply new techniques to characterize and detect disease and to optimize actions. Issues related to how to systematically capture these insights in models and properly evaluate their efficacy are studied.


current projects

  • blue-yellow kidneys
    Understanding chronic kidney disease
  • Blue digital data
    Making biomedical ML reproducible
  • Inhaler usage
    Advancing mHealth Informatics
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DEPARTMENTAL PROGRAMS

Integrated Diagnostics (IDx)
Computational Diagnostics (CDx)

DEPARTMENTS AND SCHOOLS

Department of Radiological Sciences
David Geffen School of Medicine
Bioengineering Department
Henry Samueli School of Engineering

AFFILIATED INSTITUTES AND CENTERS

UCLA CTSI
UCLA Institute for Precision Health
Center for SMART Health
Center for Domain-Specific Computing
Scalable Analytics Institute (ScAI)

TRAINING PROGRAMS

GPB: Medical Informatics Home Area
NIH T32: iDISCOVER
NIH T32: Medical Imaging Informatics
NSF NRT: Social Data Science
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COPYRIGHT © UCLA Medical Imaging Informatics. ALL RIGHTS RESERVED. | Site by Pendari