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Oral Presentation

March 22, 1:30 p.m. - 3:00 p.m.

Poster Sessions

March 21, 5:00 p.m. – 6:30 p.m.

March 22, 5:00 p.m. – 6:30 p.m.

  • AI Showcase, Stage I, Technical Study: Lessons Learned from Developing and Deploying a Real-time Machine Learning Sepsis Prediction Model for Hematopoietic Cell Transplant Recipients
    Zahra Eftekhari, City of Hope

  • AI-aided Genetic Diagnosis and Disease-gene Discovery: Scaling Up and Evaluation of an Automated Deep Phenotyping Algorithm on two External EHR Datasets
    Ling Luo, National Library of Medicine, National Institutes of Health
  • Augmenting the Quality Assurance Process with Human-centered Machine Learning in Radiation Oncology
    Malvika Pillai, UNC Chapel Hill

  • Automated And Accessible Prediction of Progression in Age-related Macular Degeneration (AMD): External Validation from the Perspectives of Methods, Populations, and Users
    Qingyu Chen, National Institutes of Health

  • Comparing Multiple Implementable Predictive Models for Pediatric Clinical Deterioration
    Sameh Saleh, Children's Hospital of Philadelphia/University of Pennsylvania

  • A Deep Understanding Approach for Case Identification and Autocoding System of Cancer Pathology Reports for a Population Cancer Registry
    *This presentation will not be presented onsite but will be part of the Digital Collection
    Jon Patrick, Health Language Analytics Global

  • Developing a Chest Radiography Generative Adversarial Network (CXR-GAN) for Medical Education Simulation
    Parth Shah, Perelman School of Medicine, University of Pennsylvania

  • Emergency Department Wait Time Prediction Based on Cyclical Features by Deep Neural Networks
    *This presentation will not be presented onsite but will be part of the Digital Collection
    Zhaohui Liang, York University

  • Evaluating the Translational Aspects of AI Application in Healthcare
    Piyush Mathur, Cleveland Clinic

  • Evaluating Vendor-derived Pediatric Sepsis Predictive Model in Acute Care Settings
    Feliciano Yu, Arkansas Children's Hospital
  • I-WIN: an Intensive Care Warning INdex System for Early Prediction of Clinical deterioration and Intervention Using Data-driven Machine Learning Modeling of EHR Data
    Fuchiang Rich Tsui, Children's Hospital of Philadelphia

  • A Multi-aspect Technical Performance Evaluation of Deep Learning Based Model For Predicting COVID-19 Patients Outcomes on Admission
    Laila Rasmy, University of Texas Health Science Center in Houston
  • Predicting Thirty-day Unplanned Cancer Readmissions using Machine Learning and Artificial Intelligence
    Danny Wu, University of Cincinnati

  • Technical Performance Evaluation of a Deep Learning-based Vancomycin Therapeutic Drug Monitoring Model
    Bingyu Mao, The University of Texas Health Science Center at Houston
  • Towards an Optimal Policy of Mass Casualty Trauma Triage
    Alexandrea Ramnarine, Northwestern University
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