MLHOps: Machine Learning for Healthcare Operations
Abstract
Machine Learning Health Operations (MLHOps) is the combination of processes for reliable, efficient, usable, and ethical deployment and maintenance of machine learning models in healthcare settings. This paper provides both a survey of work in this area and guidelines for developers and clinicians to deploy and maintain their own models in clinical practice. We cover the foundational concepts of general machine learning operations, describe the initial setup of MLHOps pipelines (including data sources, preparation, engineering, and tools). We then describe long-term monitoring and updating (including data distribution shifts and model updating) and ethical considerations (including bias, fairness, interpretability, and privacy). This work therefore provides guidance across the full pipeline of MLHOps from conception to initial and ongoing deployment.
Cite
@article{arxiv.2305.02474,
title = {MLHOps: Machine Learning for Healthcare Operations},
author = {Faiza Khan Khattak and Vallijah Subasri and Amrit Krishnan and Elham Dolatabadi and Deval Pandya and Laleh Seyyed-Kalantari and Frank Rudzicz},
journal= {arXiv preprint arXiv:2305.02474},
year = {2023}
}