Communicating exploratory unsupervised machine learning analysis in age clustering for paediatric disease Article Swipe
Joshua Spear
,
Eleni Pissaridou
,
Stuart A Bowyer
,
William Bryant
,
Daniel Key
,
John Booth
,
Anastassia Spiridou
,
Spiros Denaxas
,
Rebecca Pope
,
Andrew M. Taylor
,
Harry Hemingway
,
Neil J. Sebire
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1136/bmjhci-2023-100963
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1136/bmjhci-2023-100963
Background Despite the increasing availability of electronic healthcare record (EHR) data and wide availability of plug-and-play machine learning (ML) Application Programming Interfaces, the adoption of data-driven decision-making within routine hospital workflows thus far, has remained limited. Through the lens of deriving clusters of diagnoses by age, this study investigated the type of ML analysis that can be performed using EHR data and how results could be communicated to lay stakeholders. Methods Observational EHR data from a tertiary paediatric hospital, containing 61 522 unique patients and 3315 unique ICD-10 diagnosis codes was used, after preprocessing. K-means clustering was applied to identify age distributions of patient diagnoses. The final model was selected using quantitative metrics and expert assessment of the clinical validity of the clusters. Additionally, uncertainty over preprocessing decisions was analysed. Findings Four age clusters of diseases were identified, broadly aligning to ages between: 0 and 1; 1 and 5; 5 and 13; 13 and 18. Diagnoses, within the clusters, aligned to existing knowledge regarding the propensity of presentation at different ages, and sequential clusters presented known disease progressions. The results validated similar methodologies within the literature. The impact of uncertainty induced by preprocessing decisions was large at the individual diagnoses but not at a population level. Strategies for mitigating, or communicating, this uncertainty were successfully demonstrated. Conclusion Unsupervised ML applied to EHR data identifies clinically relevant age distributions of diagnoses which can augment existing decision making. However, biases within healthcare datasets dramatically impact results if not appropriately mitigated or communicated.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1136/bmjhci-2023-100963
- OA Status
- gold
- Cited By
- 1
- References
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401074591
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401074591Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1136/bmjhci-2023-100963Digital Object Identifier
- Title
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Communicating exploratory unsupervised machine learning analysis in age clustering for paediatric diseaseWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-01Full publication date if available
- Authors
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Joshua Spear, Eleni Pissaridou, Stuart A Bowyer, William Bryant, Daniel Key, John Booth, Anastassia Spiridou, Spiros Denaxas, Rebecca Pope, Andrew M. Taylor, Harry Hemingway, Neil J. SebireList of authors in order
- Landing page
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https://doi.org/10.1136/bmjhci-2023-100963Publisher landing page
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1136/bmjhci-2023-100963Direct OA link when available
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Medical diagnosis, Cluster analysis, Computer science, Workflow, Preprocessor, Population, Decision support system, Health care, Artificial intelligence, Machine learning, Data science, Data mining, Medicine, Database, Pathology, Environmental health, Economic growth, EconomicsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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13Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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