ICPE 2023: What Can Be Learned From Machines

MODERATORS: Jeremy Rassen | Katia Verhamme

Opportunities and Challenges Surrounding the Incorporation of Laboratory Test-Result Information within High-Dimensional Confounder Adjustment Procedures [279]

AUTHORS: John Tazare | Jeremy Brown | Daniel Morales | Elizabeth Williamson Ian Douglas (United Kingdom)

Comparison of Stable Balancing Weights Vs. Propensity Score Weighting for RWE or External Clinical Study Comparison Arms to Single Arm Clinical Trials [280]

AUTHORS: Stephen Johnston | Pranjal Tewari | Paul Coplan (United States)

Standardization Over Disease Risk Score Versus Propensity Score for Confounding Control When Using Random Forests for Model Fitting [281]

AUTHORS: Yi Li | Tibor Schuster | Kazuki Yoshida | Robert Platt (Canada)

High-dimensional Iterative Causal Forest (hdiCF): A Novel Algorithm for Subgroup Identification in Claims Data [282]

AUTHORS: Tiansheng Wang | Virginia Pate | John Buse | Richard Wyss Til Stürmer (United States)

Can Machine Learning Approaches Help Accelerating Rare Diseases Diagnosis? The Acromegaly Case Study [283]

AUTHORS: Salvatore Crisafulli | Luca L'Abbate | Andrea Fontana Giacomo Vitturi | Daniele Gianfrilli | Alessia Cozzolino Maria Cristina De Martino | Gianluca Trifirò (Italy)

Severity Score Extraction from Clinical Notes Using Natural Language Processing: Applications to Dermatology [284]

AUTHORS: Vikas Kumar | Lawrence Rasouliyan | Amanda Althoff Stella Chang | Stacey Long (United States)

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