Development and Validation of ML-DQA -- a Machine Learning Data Quality Assurance Framework for Healthcare. (arXiv:2208.02670v1 [stat.ML])

The approaches by which the machine learning and clinical research
communities utilize real world data (RWD), including data captured in the
electronic health record (EHR), vary dramatically. While clinical researchers
cautiously use RWD for clinical investigations, ML for healthcare teams consume
public datasets with minimal scrutiny to develop new algorithms. This study
bridges this gap by developing and validating ML-DQA, a data quality assurance
framework grounded in RWD best practices. The ML-DQA framework is applied to
five ML projects across …

arxiv data data quality development framework healthcare learning machine machine learning ml quality quality assurance validation