Machine learning detects hidden treatment response patterns only in the presence of comprehensive clinical phenotyping

Stephen D. Auger; Gregory Scott

doi:10.1371/journal.pone.0334858

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Open Access

Peer-reviewed

Research Article

Machine learning detects hidden treatment response patterns only in the presence of comprehensive clinical phenotyping

Stephen D. Auger ,

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: s.auger21@imperial.ac.uk

Affiliations Department of Brain Sciences, Imperial College London, London, United Kingdom, UK DRI Care Research and Technology Centre, Imperial College London, London, United Kingdom, Department of Neurology, Imperial College NHS Trust, London, United Kingdom

https://orcid.org/0000-0002-5911-7049

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Gregory Scott

Roles Conceptualization, Writing – review & editing

Affiliations Department of Brain Sciences, Imperial College London, London, United Kingdom, UK DRI Care Research and Technology Centre, Imperial College London, London, United Kingdom, Department of Neurology, Imperial College NHS Trust, London, United Kingdom
⨯

Machine learning detects hidden treatment response patterns only in the presence of comprehensive clinical phenotyping

Stephen D. Auger,
Gregory Scott

Published: October 21, 2025
https://doi.org/10.1371/journal.pone.0334858

About the Authors

Stephen D. Auger

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: s.auger21@imperial.ac.uk

Affiliations Department of Brain Sciences, Imperial College London, London, United Kingdom, UK DRI Care Research and Technology Centre, Imperial College London, London, United Kingdom, Department of Neurology, Imperial College NHS Trust, London, United Kingdom

https://orcid.org/0000-0002-5911-7049

Gregory Scott

Roles Conceptualization, Writing – review & editing

Competing Interests

The authors have declared that no competing interests exist.

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Randomized controlled trials
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Machine learning
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Electronic medical records
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Medical risk factors
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Cardiovascular therapy
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Headaches
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Simulation and modeling
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Statistical methods
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