Good things come in threes : evaluating clinical utility of machine learning-derived clusters
Lisik, Daniil ; De Kok, Jip W.T.M. ; Bermúdez Barón, Nicolás ; Vanfleteren, Lowie E.G.W. ; Nwaru, Bright I. and Basna, Rani LU
(2026)
In JAMIA Open
9(3).
- Abstract
Background: Machine learning (ML)-based cluster analysis is a common method for subtyping medical conditions and presentations of disease states. Recent advancements in algorithms and increasing access to vast healthcare data have further increased the use of such ML models. However, practical implementation is lacking, largely due to methodological limitations and insufficient reporting and clinical contextualization in the extant literature, with no existing guidelines for this purpose. Objective: To propose a general framework to assess and ensure clinical utility of ML-derived clusters. Discussion: The proposed framework encompasses three domains, focusing on clinical relevance, stability and generalizability, and ease of... (More)
Background: Machine learning (ML)-based cluster analysis is a common method for subtyping medical conditions and presentations of disease states. Recent advancements in algorithms and increasing access to vast healthcare data have further increased the use of such ML models. However, practical implementation is lacking, largely due to methodological limitations and insufficient reporting and clinical contextualization in the extant literature, with no existing guidelines for this purpose. Objective: To propose a general framework to assess and ensure clinical utility of ML-derived clusters. Discussion: The proposed framework encompasses three domains, focusing on clinical relevance, stability and generalizability, and ease of identification, enabling researchers and readers to ensure clinical utility of ML-generated subgroups.
(Less)
- author
- Lisik, Daniil
; De Kok, Jip W.T.M.
; Bermúdez Barón, Nicolás
; Vanfleteren, Lowie E.G.W.
; Nwaru, Bright I.
and Basna, Rani
LU
- organization
- publishing date
- 2026-06
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Artificial intelligence, cluster analysis, clusters, machine learning, phenotypes
- in
- JAMIA Open
- volume
- 9
- issue
- 3
- article number
- ooag098
- publisher
- Oxford University Press
- external identifiers
-
- scopus:105041605810
- pmid:42290926
- DOI
- 10.1093/jamiaopen/ooag098
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © The Author(s) 2026. Published by Oxford University Press on behalf of the American Medical Informatics Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.
- id
- f2832e56-6f19-410d-9236-1e2b67288e31
- date added to LUP
- 2026-08-11 14:55:39
- date last changed
- 2026-09-22 17:33:16
@article{f2832e56-6f19-410d-9236-1e2b67288e31,
abstract = {{<p>Background: Machine learning (ML)-based cluster analysis is a common method for subtyping medical conditions and presentations of disease states. Recent advancements in algorithms and increasing access to vast healthcare data have further increased the use of such ML models. However, practical implementation is lacking, largely due to methodological limitations and insufficient reporting and clinical contextualization in the extant literature, with no existing guidelines for this purpose. Objective: To propose a general framework to assess and ensure clinical utility of ML-derived clusters. Discussion: The proposed framework encompasses three domains, focusing on clinical relevance, stability and generalizability, and ease of identification, enabling researchers and readers to ensure clinical utility of ML-generated subgroups.</p>}},
author = {{Lisik, Daniil and De Kok, Jip W.T.M. and Bermúdez Barón, Nicolás and Vanfleteren, Lowie E.G.W. and Nwaru, Bright I. and Basna, Rani}},
keywords = {{Artificial intelligence; cluster analysis; clusters; machine learning; phenotypes}},
language = {{eng}},
number = {{3}},
publisher = {{Oxford University Press}},
series = {{JAMIA Open}},
title = {{Good things come in threes : evaluating clinical utility of machine learning-derived clusters}},
url = {{http://dx.doi.org/10.1093/jamiaopen/ooag098}},
doi = {{10.1093/jamiaopen/ooag098}},
volume = {{9}},
year = {{2026}},
}