Identification, explanation and clinical evaluation of hospital patient subtypes

We present a pipeline in which unsupervised machine learning techniques are used to automatically identify subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted...

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Hauptverfasser: Werner, Enrico, Clark, Jeffrey N, Bhamber, Ranjeet S, Ambler, Michael, Bourdeaux, Christopher P, Hepburn, Alexander, McWilliams, Christopher J, Santos-Rodriguez, Raul
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Sprache:eng
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Zusammenfassung:We present a pipeline in which unsupervised machine learning techniques are used to automatically identify subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted and assigned clinical meaning. In parallel, clinicians assessed intra-cluster similarities and inter-cluster differences of the identified patient subtypes within the context of their clinical knowledge. By confronting the outputs of both automatic and clinician-based explanations, we aim to highlight the mutual benefit of combining machine learning techniques with clinical expertise.
DOI:10.48550/arxiv.2301.08019