Events

Seminar Series

Presenter:

Dr. Chi-Ren Shyu

Date:

05-09-2019

Time:

3:30pm-5:00pm

Location:

Leadership Auditorium, 2501 Student Center

Discovery of Homogeneous Subgroups from Heterogeneous Populations for Precision Health – A Deep Exploratory Mining and X2AI Approach

Today, six of the top ten highest-grossing drugs in the US are effective in less than 10% of patients and even the most effective drugs from that list have positive responses in only 25% of patients. This “imprecision medicine” practice not only harms certain populations of patients, it also burdens the healthcare system financially. By finding meaningful and homogeneous subgroups prior to conducting costly clinical trials, researchers can further study focused populations and identify potential risk factors through slicing and dicing from complex phenotypic/genotypic information sources. Advancements in machine learning algorithms have shown promising results in many biomedical applications and may provide a potential solution for subgroup discoveries. Unfortunately, many of the high-performing approaches result in black boxes that are not explainable and often fail to close the known gap between computational innovation and clinical practice. In this talk, I will introduce a novel deep exploratory mining framework that is designed to answer the following two questions: “What hypotheses are likely to be novel and produce clinically relevant results with well thought-out study designs?” and “Which subgroups of patients might benefit from interventions that are likely to be effective for the selected populations?” Innovations in actionable and explainable AI (X2AI) and applications of Big Data technologies make it feasible to tackle this complex biomedical informatics problem. Although this framework is domain independent, I will use a case study in autism spectrum disorder (ASD) to explore a large cohort of ASD patients and find subgroups with similar behavioral and communication characteristics that share underlying genetic patterns for potential interventions.

Seminar Series

Presenter:

Katie Wilkinson

Date:

04-11-2019

Time:

4:00PM-4:30PM

Location:

Leadership Auditorium, 2501 Student Center

Linking EMR and Exposome Data for Risk Prediction and Interventions: A Translational Approach

Precision medicine (PM) is a medical model that proposes the customization of healthcare, with medical decisions, treatments, practices, or products being tailored to the individual patient.  An individual’s “Social Determinants of Health” (SDOH) have been demonstrated as a key factor in obtaining successful clinical outcomes for individual patients which necessitate individualized interventions.  Two major problems exist in addressing SDOH within a clinical setting.  First, interventions that have shown to be successful in addressing challenges presented by various Social Determinants of Health often scarce and span outside of those services that are available and/or reimbursed within a healthcare setting.  Because of this, it is critical that these resources can be deployed to the patients who will benefit the most.  Second, identifying those patients who will most benefit from those interventions is problematic because of the limited information on SDOH contained in a typical Electronic Medical Record (EMR).  This talk will walk through a proposed research design to use geo-spatial SDOH data, the exposome, as a proxy for individual-level SDOH data.  I propose to test the hypothesis that exposome data, linked with EMR data, can better predict risk of hypertension.  I will also propose a method for external validation and discuss how this might be applied to specific care interventions.

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