MLHOps: Machine Learning Health Operations

MLHOps: Machine Learning Health Operations

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 t...

Full description

Saved in:
Bibliographic Details
Main Authors: Faiza Khan Khattak, Vallijah Subasri, Amrit Krishnan, Chloe Pou-Prom, Sedef Akinli-Kocak, Elham Dolatabadi, Deval Pandya, Laleh Seyyed-Kalantari, Frank Rudzicz
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Computational and artificial intelligence
machine learning
engineering in medicine and biology
electronic healthcare
health information management
hospitals
Online Access:https://ieeexplore.ieee.org/document/10811924/
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 and 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. We also include a checklist to ensure thorough verification of each step in the process.
ISSN:2169-3536

Similar Items