What Can Be Learned From Machines

Co-Moderator(s)

• 

  Jeremy Rassen, ScD, FISPE

  Co-founder and President
  Aetion
  Sharon, United States
• 

  Katia Verhamme, MD, PhD (she/her/hers)

  Associate Professor
  Department of Medical Informatics, Erasmus MC University
  Rotterdam, Netherlands

Presentations:

• 1:30 PM – 1:45 PM ADT

  Opportunities and challenges surrounding the incorporation of laboratory test-result information within high-dimensional confounder adjustment procedures
  

  Presenting Author: John R. Tazare, PhD – London School of Hygiene and Tropical Medicine
• 1:45 PM – 2:00 PM ADT

  Comparison of Stable Balancing Weights vs Propensity Score Weighting for RWE or External Clinical Study Comparison Arms to Single Arm Clinical Trials
  

  Presenting Author: Stephen S. Johnston, PhD – Johnson & Johnson
• 2:00 PM – 2:15 PM ADT

  Standardization over disease risk score versus propensity score for confounding control when using random forests for model fitting
  

  Presenting Author: Yi Li, MS (she/her/hers) – Department of Epidemiology, Biostatistics and Occupational Health, McGill University
• 2:15 PM – 2:30 PM ADT

  High-dimensional Iterative Causal Forest (hdiCF): a Novel Algorithm for Subgroup Identification in Claims Data
  

  Presenting Author: Tiansheng Wang, PharmD, PhD – University of North Carolina at Chapel Hill, Department of Epidemiology
• 2:30 PM – 2:45 PM ADT

  Can Machine Learning approaches help accelerating rare diseases diagnosis? The Acromegaly Case Study
  

  Presenting Author: Salvatore Crisafulli, PharmD – Department of Medicine, University of Verona, Verona, Italy
• 2:45 PM – 3:00 PM ADT

  Severity Score Extraction from Clinical Notes Using Natural Language Processing: Applications to Dermatology
  

  Presenting Author: Vikas Kumar, MD, MS – OMNY Health