A novel data-driven approach for Personas validation in healthcare using self-supervised machine learning.

Journal: Journal of biomedical informatics

PMID: 40090432

Abstract

OBJECTIVE: Persona validation is a challenging task, often relying on costly external validation methods. The aim of this study was the development of a novel method for Personas validation based on data already available during their creation.

Authors

Emanuele Tauro

Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy; Department of Cardiology, Cardiology Research Laboratory, Istituto Auxologico Italiano IRCCS, Milan, Italy. Electronic address: emanuele.tauro@polimi.it.
Alessandra Gorini

Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy; Istituti Clinici Scientifici Maugeri IRCCS, Milano, Calmadoli, Italy.
Grzegorz Bilo

Department of Cardiology, Istituto Auxologico Italiano, IRCCS, Milan.
Enrico Gianluca Caiani

Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy. enrico.caiani@polimi.it.

Keywords

Algorithms Cluster Analysis Delivery of Health Care Female Humans Machine Learning Male Reproducibility of Results Supervised Machine Learning Surveys and Questionnaires

External Resources

View on PubMed Access via DOI PubMed (40090432)

A novel data-driven approach for Personas validation in healthcare using self-supervised machine learning.

Abstract

Authors

Keywords

External Resources

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