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.

翻译：我们提出了一个管道,利用不受监督的机器学习技术,自动识别2017-2021年到2021年之间在英国一所大型教学医院住院病人的子类型;通过使用最先进的解释技术,对所确定的子类型进行解释和指定临床含义;同时,临床医生在其临床知识范围内评估了所确定的病人子类型在集群内的相似性和集群间差异;通过应对自动解释和临床解释的结果,我们力求强调将机器学习技术与临床专门知识相结合的互利。