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DTSTART;TZID=America/New_York:20241107T120000
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SUMMARY:Tech Talk: Using Cross-Classified Multilevel Models To Estimate The Impact Of Patient-Level Risk Factors And Time-Varying Hospital Until On Healthcare-Associated Clostridioides Difficile Infection
DESCRIPTION:Thursday November 7\, 2024 from 12:00-1:00pm\nVirtual Teams Presentation \nThe use of multilevel modeling in epidemiology allows health researchers to examine how a person’s physical or socially constructed environment may influence their health exposures or disease occurrence. This TechTalk will provide an overview of how multilevel modeling can be used in epidemiologic research in a clinical setting. A traditional example of a multilevel data structure considers how a person’s health may be influenced by their neighborhood or place of residence. In an inpatient setting\, we can consider how a patient’s health status may be influenced by their physical environment\, such as their room\, unit\, or hospital. We will walk through an example of this method using data from a case-control study of ChristianaCare patients\, evaluating patient-level risk factors for Clostridioides difficile infection\, accounting for the patient’s physical environment throughout their hospitalization. Challenges in conducting research in this setting will be discussed and analytic solutions proposed. \nMeet the Speaker\n   \n\n\nJessica Webster\, PhD\, MPH\nCenter for Health Care Quality\, HealthcareAssociated Infections ProgramCalifornia Dept of Public HealthLos Angeles\, CA \nJessica Webster is an epidemiologist with research interests in infectious diseases (particularly healthcare-associated infections) and epidemiologic methods. She received her PhD in Epidemiology from Drexel University’s Dornsife School of Public Health in 2024\, where her dissertation work focused on exploring challenges in exposure characterization for healthcare-associated infections such as Clostridioides difficile. Jessica is currently working at the California Department of Public Health as an Antimicrobial Stewardship Epidemiologist. \n\n\n \nMicrosoft TeamsJoin the meeting nowMeeting ID: 283 724 980 920Passcode: dAFUdV\nDial in by phone+1 302-483-7154\,\,485722297# United States\, WilmingtonFind a local numberPhone conference ID: 485 722 297# \n\n\n\nPlease RSVP to Debra.Reese@ChristianaCare.org Include your name\, email address\, and institution/organization.  We will provide instructions on obtaining CME credit for attendance. \nWork supported by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under grant number U54-GM104941 (PI:Hicks) and the State of Delaware.
URL:https://www.de-ctr.org/event/tech-talk-using-cross-classified-multilevel-models-to-estimate-the-impact-of-patient-level-risk-factors-and-time-varying-hospital-until-on-healthcare-associated-clostridioides-difficile-infection/
LOCATION:Microsoft Teams
CATEGORIES:Tech Talk Series
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DTSTART;TZID=America/New_York:20250306T120000
DTEND;TZID=America/New_York:20250225T130000
DTSTAMP:20260422T181705
CREATED:20250225T170021Z
LAST-MODIFIED:20250225T205940Z
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SUMMARY:Tech Talk: Integrating Multiscale modeling and Machine Learning In Computational Medicine
DESCRIPTION:Thursday March 6\, 2025 from 12:00-1:00pm\nVirtual Teams Presentation \nComputational models have revolutionized our understanding of complex biological systems. These models typically fall into two categories: multiscale models and machine learning models. Multiscale models\, grounded in the fundamental principles of physics and chemistry\, can dissect the intricate causalities in disease progression. In parallel\, machine learning models\, based on rich datasets\, have yielded unique insights into complex pattern recognition within these systems. \nIn this talk\, Dr. Deng will demonstrate how multiscale models are developed to probe disease-mediated changes in blood dynamics. She will also show how various machine learning models\, with a focus on deep learning\, are designed to enhance accurate prediction\, optimize treatment plans\, and distill extensive knowledge of different diseases. \nTo conclude\, she will discuss the pivotal factors—such as age and sex—that are critical to customizing treatments in the realm of precision medicine\, and to synergistically integrate multiscale modeling with machine learning to enable the design of more comprehensive personalized medicine. \nMeet the Speaker\n  \n\n\nYixiang Deng\, Ph.D.\nAssistant Professor\, Computer & Information Sciences University of Delaware \nYixiang Deng is an assistant professor at the University of Delaware in the Department of Computer and Information Sciences.  She has worked  as a Postdoctoral Fellow at the Ragon Institute of Mass General\, MIT\, and Harvard\, with a joint appointment at the MIT Department of Biological Engineering.  Her research focuses on integrating multiscale modeling and machine learning to probe the mechanisms of diseases and the effectiveness  of therapeutics. Before joining the Ragon Institute\, she completed her Ph.D. in the School of Engineering at Brown University in 2021 and a B.Eng. degree in Engineering Mechanics at Shanghai Jiao Tong University. \n\n\n \n\nMicrosoft TeamsJoin the meeting nowMeeting ID: 230 763 563 409Passcode: pY269cz7 \n\nDial in by phone+1 302-483-7154\,\,815866681# United States\, WilmingtonFind a local numberPhone conference ID: 815 866 681# \n\n\n\nPlease RSVP to Debra.Reese@ChristianaCare.org Include your name\, email address\, and institution/organization.  We will provide instructions on obtaining CME credit for attendance. \nWork supported by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under grant number U54-GM104941 (PI:Hicks) and the State of Delaware.
URL:https://www.de-ctr.org/event/tech-talk-integrating-multiscale-modeling-and-machine-learning-in-computational-medicine/
LOCATION:Microsoft Teams
CATEGORIES:Tech Talk Series
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