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- W4386363389 abstract "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." @default.
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- W4386363389 date "2023-01-01" @default.
- W4386363389 modified "2023-09-27" @default.
- W4386363389 title "Identification, Explanation and Clinical Evaluation of Hospital Patient Subtypes" @default.
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- W4386363389 doi "https://doi.org/10.1007/978-3-031-36938-4_11" @default.
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