CHANESE FORTE, GRADUATE STUDENT, ENVIRONMENTAL HEALTH SCIENCES & SCIENTIFIC COMPUTING

Bio: Chanese is a Dual PhD student pursuing a degree in the Environmental Health Sciences and Scientific Computing. Chanese’s research interests lie in chemical exposure in agriculture workers and cellular alteration.

ASCERTAINING PESTICIDE EXPOSURE AND BIOACTIVITY USING OPEN SOURCE DATA: Pesticides are known to be harmful chemicals to human health, however, they are still heavily used in agriculture. Using large publicly available datasets, this study aims to quantify pesticide exposure levels of the US general population in comparison to farmworkers. The National Health and Nutrition Examination Survey (NHANES) is a cross-sectional study representative of the US population. NHANES was used to quantify pesticide exposure among US farmworkers and the general population who responded to NHANES. It compares and analyzes, using regression, the US pesticide exposure levels to the bioactivity of these same pesticides within the human body. By comparing population-level data with toxicological assay data in future projects, we hope to create a more overarching idea of how pesticides may be affecting the body and the human population level.

HYEON JOO, GRADUATE STUDENT, HEALTH INFRASTRUCTURES AND LEARNING SYSTEMS & SCIENTIFIC COMPUTING

Bio: Hyeon Joo is a second year PhD student in the Health Infrastructures and Learning Systems program of the Department of Health Learning Systems (Michigan Medical School). He completed his MS in Computer Science and Engineering, and Master of Health Informatics from the University of Michigan, Ann Arbor. His research focuses on developing and implementing computational data-driven algorithms, systems or tools to help users identify gaps and make informed decisions. He loves working in the field of health care as a data scientist and a software engineer.

EARLY PREDICTION OF HEART FAILURE USING ATTENTION MODELS USING EHR DATA: Heart Failure (HF) is a severe and progressive chronic condition affecting over 5.8 million patients with a 5-year mortality rate of 45-60% in the United States. Despite significant efforts and advanced HF management, diagnosing HF in the early stages remains challenging due to its syndromic nature and non-specific disease presentation. In this seminar, I will present a single attention recurrent network and a hierarchical attention convolutional neural networks to detect the early stage of HF at a tertiary hospital. I will also describe various methods of feature selection to reduce the computation time and improve the performance of the models. Lastly, I will present the challenges of adopting models in clinical practice which leads to my next research steps.

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This event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.

Questions? Email [email protected]