A weighted K-means plus decision-tree pipeline learns multi-action policies from observational data and is applied to HCV treatment choices for HIV co-infected patients, finding a high-clearance subgroup and potential cost savings of CAN$3.6-4.9 million.
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A modular multimodal generative AI framework produces synthetic residential building data from public sources, with reported overlaps exceeding 65% against a national reference dataset.
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Policy Learning with Observational Data: The Case of Hepatitis C Treatment for HIV/HCV Co-Infected Patients
A weighted K-means plus decision-tree pipeline learns multi-action policies from observational data and is applied to HCV treatment choices for HIV co-infected patients, finding a high-clearance subgroup and potential cost savings of CAN$3.6-4.9 million.
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Synthetic Homes: A Multimodal Generative AI Pipeline for Residential Building Data Generation under Data Scarcity
A modular multimodal generative AI framework produces synthetic residential building data from public sources, with reported overlaps exceeding 65% against a national reference dataset.