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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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. |
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DOI: | 10.48550/arxiv.2301.08019 |