Evaluating automated machine learning platforms for use in healthcare

Author:

Scott Ian A12ORCID,De Guzman Keshia R34,Falconer Nazanin34,Canaris Stephen5,Bonilla Oscar5,McPhail Steven M56,Marxen Sven7,Van Garderen Aaron57,Abdel-Hafez Ahmad56,Barras Michael34

Affiliation:

1. Centre for Health Services Research, University of Queensland , Brisbane, 4102, Australia

2. Department of Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital , Brisbane, 4102, Australia

3. Department of Pharmacy, Princess Alexandra Hospital , Brisbane, 4102, Australia

4. School of Pharmacy, The University of Queensland , Brisbane, 4102, Australia

5. Digital Health and Informatics, Metro South Health , Brisbane, 4102, Australia

6. Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology , Brisbane, 4059, Australia

7. Pharmacy Service, Logan and Beaudesert Hospitals , Logan, 4131, Australia

Abstract

Abstract Objective To describe development and application of a checklist of criteria for selecting an automated machine learning (Auto ML) platform for use in creating clinical ML models. Materials and Methods Evaluation criteria for selecting an Auto ML platform suited to ML needs of a local health district were developed in 3 steps: (1) identification of key requirements, (2) a market scan, and (3) an assessment process with desired outcomes. Results The final checklist comprising 21 functional and 6 non-functional criteria was applied to vendor submissions in selecting a platform for creating a ML heparin dosing model as a use case. Discussion A team of clinicians, data scientists, and key stakeholders developed a checklist which can be adapted to ML needs of healthcare organizations, the use case providing a relevant example. Conclusion An evaluative checklist was developed for selecting Auto ML platforms which requires validation in larger multi-site studies.

Publisher

Oxford University Press (OUP)

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