Article

A Framework for Locally Imputing and Predicting Biomarker Trajectories Under Irregular Monitoring: Application to Chronic Myeloid Leukemia

2026-01-07

Abstract excerpt

<title>Abstract</title> <p>Irregular monitoring and missing data limit the utility of longitudinal biomarkers in real-world practice. We developed a generalizable framework that combines interval-aligned preprocessing, localized multiple imputation, and machine-learning forecasting to generate complete trajectories and predict future biomarker values under routine clinical conditions. Using BCR::ABL1 monitoring i...

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Literature Corpus work: 3368d8f3-442e-5685-ac01-a09c055ecb7d
DOI: 10.21203/rs.3.rs-8420996/v1

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A Framework for Locally Imputing and Predicting Biomarker Trajectories Under Irregular Monitoring: Application to Chronic Myeloid LeukemiaDOI 10.21203/rs.3.rs-8420996/v1Read

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