Towards a framework for interoperability and reproducibility of predictive models

Abstract

The development and deployment of machine learning (ML) models for biomedical research and healthcare currently lacks standard methodologies. Although tools for model replication are numerous, without a unifying blueprint it remains difficult to scientifically reproduce predictive ML models for any number of reasons (e.g., assumptions regarding data distributions and preprocessing, unclear test metrics, etc.) and ultimately, questions around generalizability and transportability are not readily answered. To facilitate scientific reproducibility, we built upon the Predictive Model Markup Language (PMML) to capture essential information. As a key component of the PREdictive Model Index and Exchange REpository (PREMIERE) platform, we present the Automated Metadata Pipeline (AMP) for conversion of a given predictive ML model into an extended PMML file that autocompletes an ML-based checklist, assessing model elements for interoperability and reproducibility. We demonstrate this pipeline on multiple test cases with three different ML algorithms and health-related datasets, providing a foundation for future predictive model reproducibility, sharing, and comparison. Problem: The development and deployment of machine learning (ML) models for biomedical research and healthcare currently lack standard methodologies, leading to problems of scientific reproducibility and interoperability. What is Already Known: Although there are many tools for model replication, without a unifying blueprint, it remains difficult to scientifically reproduce predictive ML models for any number of reasons. Moreover, questions around generalizability and transportability are not readily answered. What this Paper Adds: This study builds upon the Predictive Model Markup Language (PMML) to capture essential information and presents the Automated Metadata Pipeline (AMP) for conversion of a given predictive ML model into an extended PMML file that auto-completes an ML-based checklist, assessing model elements for interoperability and reproducibility. We demonstrate this pipeline on multiple test cases with three different ML algorithms and health-related datasets. The proposed AMP provides a framework for automating the completion and evaluation of a comprehensive ML model checklist, increasing compliance and ultimately, predictive model reproducibility, sharing, and comparison of predictive models by ensuring all appropriate information is available.

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