INTELLIGENT METHODOLOGICAL ARCHITECT: ALGORITHMIC OPTIMIZATION OF VALIDITY COVERAGE IN CLINICAL PREDICTION MODELS
Published: 2026-04-18
Abstract
Methodological deficiencies in analytical pipelines in biomedical research—violations of events-per-variable (EPV) requirements, inadequate handling of missing data, and the systematic absence of calibration assessment—have been identified as a structural determinant of the reproducibility crisis. Wynants et al. (2020) identified a high risk of bias in 94% of 232 COVID-19 prediction models, primarily due to errors in analytical pipeline design. Existing AutoML systems optimize predictive performance while leaving threats to methodological validity unaddressed.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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[1]
Bilolov Komoliddin and Shakhnoza Kalankhodjaeva trans. 2026. INTELLIGENT METHODOLOGICAL ARCHITECT: ALGORITHMIC OPTIMIZATION OF VALIDITY COVERAGE IN CLINICAL PREDICTION MODELS. Uzbekistan Open Conference. 11 (Apr. 2026).