BAYESIAN CAUSAL INFERENCE MODELS FOR HEALTHCARE TREATMENT USING REAL WORLD PATIENT DATA

Computer implemented methods, systems, and computer readable medium are provided for performing causal inference analyses to determine the more effective treatment among alternative treatments in the healthcare setting using real world observational data. Both binary treatment and adaptive treatment...

Full description

Saved in:
Bibliographic Details
Main Author Huang, Bin
Format Patent
LanguageEnglish
Published 24.03.2022
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
PHYSICS
Online AccessGet full text

Cover

More Information
Summary:Computer implemented methods, systems, and computer readable medium are provided for performing causal inference analyses to determine the more effective treatment among alternative treatments in the healthcare setting using real world observational data. Both binary treatment and adaptive treatment strategies are considered in the analysis. The methods comprise generating a Bayesian marginal structural model and performing a single step of Bayesian regression that incorporates matching, weighting, and estimation processes and in which the matching process is performed using a Guassian process ("GP") prior covariance function.
Bibliography:Application Number: US202017310176