CHAI Seminar Series

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Event Series
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Publication Search Results

Now showing 1 - 10 of 15
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    Big Data in Pediatric Cardiac ICU
    ( 2020-02-27) Aljiffry, Alaa
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    Automation of Evidence Matching and Systemic Reviews Using Web-Based Medical Literature
    ( 2020-02-25) Ho, Joyce
    Mining biomedical text can be useful for validating new disease subgroups or summarizing information to guide policies and decision making. Yet, existing work predominately focuses on efficient information retrieval. There are other applications where mining biomedical text can be useful. As two motivating examples, researchers are discovering new disease subgroups from secondary analyses of electronic health records. However, such subgroups need to be validated or aligned with current literature. Similarly, systematic reviews serve as a mechanism to summarize current evidence related to a research question. In both scenarios, the abundance of articles can be overwhelming to process manually. In this talk, I will first introduce a scalable framework that produces evidence sets (or relevant articles) using a large corpus of online medical literature. I will discuss some of the challenges associated with term representation and mining biomedical text. I will then present recent work on automating the screening process to allow health services researchers to more efficiently summarize the current findings.
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    Epidemiology and Population Health
    ( 2019-04-18) Patzer, Rachel
    This talk will discuss the principles of epidemiologic thinking and a foundation for population health sciences. Basic epidemiologic concepts including how best to answer a scientific question, consider causality, and conduct research of high impact to the population will be discussed.
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    Scalable Graph Analytics on GPU Accelerators
    ( 2019-04-16) Green, Oded
    Sparse data computations are ubiquitous in science and engineering. Two widely used applications requiring sparse data computations are graph algorithms and linear algebra operations such as Sparse Matrix-Vector Multiplication (SpMV). In contrast to their dense data counterparts, sparse-data computations have less locality and more irregularity in their execution - making them significantly more challenging to optimize. This is especially true for accelerators and many core systems. In today's talk, I will cover NVIDIA's and the graph community's effort to overcome these challenges and to create a simple to use framework that will enable both programmers and data scientists to get high performance graph algorithms, with high productivity, and an easy to use API that does not require broad HPC knowledge.
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    Deep Learning for Healthcare: Opportunities, Innovations, and Challenges
    ( 2019-01-24) Xiao, Cao (Danica)
    The massive generation and collection of digitized health data, e.g. electronic health records (EHRs), along with these notable, longstanding healthcare deficiencies including diagnostic errors and medication mistakes are calling for more advanced techniques to power health data for improving healthcare. Deep learning provides great potential in transforming diverse health analytics tasks. In this talk, we mainly explore the following ones: Accurate medical embedding across correlated and multimodal medical entities to facilitate disease diagnosis or adverse drug reaction detection. Personalized and safe medication recommendation under evolving health conditions. In my talk, I will present our recent works, and talk about how we take deep learning approaches to address these challenges. In addition, I will also discuss several open challenges for applying deep learning in healthcare applications.
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    How Do We Accelerate Data Driven Health Care?
    ( 2018-04-19) Stewart, Walter
    By driving the digitization of healthcare data, the 2009 HITECH Act set the stage for business and business practice transformation. Today, the amount of healthcare data doubles every two years and growth is accelerating. It is now widely accepted that the future of healthcare will strongly depend on the effective and maximal use of the growing diversity and volume of healthcare data to transform care in ways that substantially improve patient accessibility, quality, and affordability of care and patient and provider experience. But, achieving that vision – transforming healthcare through the continual creation of analytical insights and their translation into clinical practice – is exceedingly difficult in the complex business of health care because it requires diverse knowledge, talent, assets, and endless financial investment. No single entity or market has all that is required. Today, these capabilities and assets rest within distinctly different organizations that largely act independently of each other. In this talk I will describe an organizational model that bridges the divides among organizations to accelerate transformation of health care through data driven science, methods, and solutions.
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    Translating from Chemistry to Clinic with Deep Learning: Modeling the Metabolism and Subsequent Reactivity of Drugs
    ( 2018-04-12) Swamidass, S. Joshua
    Many medicines become toxic only after bioactivation by metabolizing enzymes. Often, metabolic enzymes transformed them into chemically reactive species, which subsequently conjugate to proteins and cause adverse events. For example, carbamazepine is epoxidized by P450 enzymes in the liver, but then conjugates to proteins, causing Steven Johnsons Syndrome in some patients. The most difficult to predict drug reactions, idiosyncratic adverse drug reactions (IADRs), often depend on bioactivation. Our group has been using deep learning to model the metabolism of diverse chemicals, and the subsequent reactivity of their metabolites. Deep learning systematically summarizes the information from thousands of publications into quantitative models of bioactivation, modeling precisely how medicines are modified by metabolic enzymes. These models are giving deeper understanding of why some drugs become toxic, and others do not. At the same time, deep learning can be used to understand drug toxicity as it arises in clinical data, and why some patients are affected, but not others. A conversation between the basic and clinical sciences is now possible, where patient outcomes can be understood in light of bioactivation mechanisms, and these mechanisms can explain why some patients are susceptible to drug toxicity, and others are not.
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    Causal Network Discovery from Biomedical and Clinical Data
    ( 2018-04-05) Cooper, Gregory
    This talk will provide an introduction to concepts and methods for learning causal relationships in the form of causal networks from biomedical and clinical data, including solely observational data. Examples will be given of applying these methods to biomedical data. The talk will also provide pointers to software for learning causal networks from data, including data containing thousands of variables.
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    Big Data in Behavioral Medicine
    ( 2018-03-06) Rehg, James M.
    The explosion of health-related data in the form of electronic health records and genomics has captured the attention of both the machine learning and medical informatics communities. In his talk, Dr. Rehg will describe an emerging opportunity to bring data analytics to bear on the behavioral dimension of health. Among other things, he will discuss advances in mobile sensing, from classical activity monitoring to the recent advent of wearable cameras, and how these rapidly evolving technologies provide new opportunities to continuously-measure behaviors under naturalistic conditions and construct novel predictive models for adverse behavioral outcomes.