Communicating exploratory unsupervised machine learning analysis in age clustering for paediatric disease

BackgroundDespite 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. Thro...

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Veröffentlicht in:	BMJ health & care informatics Jg. 31; H. 1; S. e100963
Hauptverfasser:	Spear, Joshua William, Pissaridou, Eleni, Bowyer, Stuart, Bryant, William A, Key, Daniel, Booth, John, Spiridou, Anastasia, Denaxas, Spiros, Pope, Rebecca, Taylor, Andrew M, Hemingway, Harry, Sebire, Neil J
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England BMJ Publishing Group Ltd 29.07.2024 BMJ Publishing Group LTD BMJ Publishing Group
Schlagworte:	Adolescent Age Age Factors Algorithms Artificial intelligence Child Child, Preschool Cluster Analysis Clustering Data Science Data Visualization Decision making Electronic Health Records Female Hospitals Humans Infant Infant, Newborn Machine Learning Male Original Research Patients Pediatrics Statistical analysis Unsupervised Machine Learning Data Science Data Visualization Electronic Health Records Machine Learning
ISSN:	2632-1009, 2632-1009
Online-Zugang:	Volltext
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