Oral Presentations
May 25, 11:00 a.m. - 12:00 p.m.
- Utilizing a User-centered Approach to Develop Explainable Machine Learning-based CDS tools to Predict New Onset of Delirium
Siru Liu, Vanderbilt University Medical Center
- Usability and Clinical Workflow Assessment of an AI-Empowered Perio-Risk Scoring System
Jay Patel, IU School of Dentistry
- Usability and Workflow Considerations for the Implementation of Deep Learning models at the Point of Care to Predict COVID-19 Outcomes
Deevakar Rogith, The University of Texas Health Science Center at Houston (UTHealth)
- Design, EHR Integration and Evaluation of Clinical Decision Support Workflows Driven by a Mortality Prediction Model to Promote Goal Concordant Care
Laura Roberts, City of Hope
- Implementation, Usability, and Workflow Integration of an ML-based CDS Tool: A Qualitative Evaluation of User Requirements
Lu Zheng, Arizona State University
Poster Sessions
May 24, 5:15 p.m. - 6:15 p.m.
May 25, 4:15 p.m. - 5:15 p.m.
- Usability and workflow evaluation of the Intensive care Warning INdex system (I-WIN) for early prediction of clinical deterioration and intervention
Fuchiang (Rich) Tsui, Children's Hospital of Philadelphia
- Evaluating Vendor-derived Pediatric Sepsis Predictive Model in Acute Care Settings
Feliciano Yu, Arkansas Children's Hospital
- Instrumenting Machine Learning Operations for Clinical Research with MLHO
Hossein Estiri, Harvard Medical School
*This presentation will not be presented onsite but will be part of the Pre-recorded Virtual Collection
- Exploring the Technical Feasibility and the Context of Use of a Machine Learning Model Predicting Unplanned Cancer Readmissions
Danny Wu, University of Cincinnati
- Usability of Real-Time Sepsis Risk Prediction Workflows for Hematopoietic Cell Transplant Recipients
Cameron Carlin, City of Hope National Medical Center
- Automated and Accessible Diagnosis in Age-related Macular Degeneration (AMD): a Usability Evaluation of Diagnostic Accuracy and Efficiency assisted by machine learning model predictions
Qingyu Chen, National Institutes of Health
*This presentation will not be presented onsite but will be part of the Pre-recorded Virtual Collection
- Emergency Department Wait Time Prediction based on Cyclical Features by Deep Neural Networks
Zhaohui Liang, York University