Medical & Imaging Informatics research encompasses several areas of interrelated foci. Building on a longstanding history of NIH and NSF funding, our work spans the spectrum from cutting-edge methodological (algorithm) development through to translational applications and evaluation. We draw on interdisciplinary efforts and team science to create new paradigms for biomedical informatics research and healthcare.
Medical Informatics recruitment will be held via Zoom November 5th from 9am-10am and November 12th from 9am-10am. Please see Zoom information down below:
Time: Nov 5, 2020 09:00 AM Pacific Time
Join Zoom Meeting link | Password: @6NV.@
https://uclahs.zoom.us/j/93487127470?pwd=aUthYUlyTWhOZmpJNG0xd3paNVkrdz09
Time: Nov 12, 2020 09:00 AM Pacific Time
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We just published a new mHealth paper in JAMIA Open. Initial findings from a pilot using the Biomedical REAl-Time Health Evaluation (BREATHE) platform. Congratulations to the LA PRISMS team! You can access the paper here.
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Sorry, couldn't resist the joke. We have a new look! We're saying a fond farewell to our old MII website, and have updated major content for this launch. While we make some tweaks here and there and complete the refresh, you can see all of MII's ongoing research and learn about our programs at UCLA.
As part of CDSC, Dr. William Hsu co-authored a paper on using a 3D self-attention CNN for enhancing low-dose computed tomography (LDCT) images. The paper is entitled, SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising with Self-supervised Perceptual Loss Network, and you can read it here.
Medical imaging is a mainstay in the screening and diagnosis of human disease, and in treatment response assessment. Imaging informatics is concerned with the management and analysis of such data (e.g., CT, MR, digital pathology, etc.) including quantitative analysis (e.g., radiomics) and its usage in the broader context of healthcare.
Biomedical natural language processing (NLP) encompasses methods for the automatic retrieval, extraction, and summarization of information found in clinical notes, published literature, social media, and other (unstructured) free-text data sources.
Coalescing data into a useful form that can be reasoned with requires new ways of organization supporting efficient querying/discovery with deeper semantics. Biomedical knowledge representation is concerned with the data models, ontologies, and formalizations that structure data into useful information and insights.
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.
Clinical decision support (CDS) aims to provide important, timely information to physicians and patients. Research in this area draws on techniques from contemporary computational methods (e.g., sequential decision making); human-computer interaction; and behavioral and implementation science, helping optimize outcomes and healthcare delivery through informed decision-making.
Increased access to health devices and information create new opportunities for gathering data (e.g., through mobile health platforms, mHealth) as well as communicating and educating individuals (e.g., through patient portals). Digital health focuses on these emergent areas and socio-technological themes.