ArticleExperimental biology and medicine (Maywood, N.J.)2023
Explainable hierarchical clustering for patient subtyping and risk prediction.
Article in Experimental biology and medicine (Maywood, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Article
- Machine-learning-based Phenomapping of Patients with Keratinocyte Carcinoma: Data-driven Subgrouping by Disease Burden, Comorbidities and Socioeconomic Status.Acta dermato-venereologica · 2026Article
- RiTex: Harmonization of Radiomic Features Based on Riemannian Geometry.Journal of imaging · 2026Article
- A hierarchical conformal framework for uncertainty-aware length of stay prediction in multi-hospital settings.Scientific reports · 2026Article
- Unravelling lumbar disc herniation severity beyond MRI : integrated transcriptomic and metabolomic analyses highlight glycerophospholipid metabolism and inform a machine-learning diagnostic model: a pilot study.Bone & joint research · 2025Article
- Improving explainability of post-separation suicide attempt prediction models for transitioning service members: insights from the Army Study to Assess Risk and Resilience in Servicemembers - Longitudinal Study.Translational psychiatry · 2025Article
- The DBCV index is more informative than DCSI, CDbw, and VIASCKDE indices for unsupervised clustering internal assessment of concave-shaped and density-based clusters.PeerJ. Computer science · 2025Article
- A data-driven method for surgeon-specific difficulty assessment in third molar extraction.Frontiers in medicine · 2025Article
- A new distal radius fracture classification depending on the specific fragments through machine learning clustering method.BMC musculoskeletal disorders · 2024Article
- Creative and generative artificial intelligence for personalized medicine and healthcare: Hype, reality, or hyperreality?Experimental biology and medicine (Maywood, N.J.) · 2023Article
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8 authors.
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Abstract
We present a pipeline in which machine learning techniques are used to automatically identify and evaluate subtypes of hospital patients admitted between 2017 and 2021 in a large UK teaching hospital. Patient clusters are determined using routinely collected hospital data, such as those used in the UK's National Early Warning Score 2 (NEWS2). An iterative, hierarchical clustering process was used to identify the minimum set of relevant features for cluster separation. With the use of state-of-the-art explainability techniques, the identified subtypes are interpreted and assigned clinical meaning, illustrating their robustness. In parallel, clinicians assessed intracluster similarities and intercluster differences of the identified patient subtypes within the context of their clinical knowledge. For each cluster, outcome prediction models were trained and their forecasting ability was illustrated against the NEWS2 of the unclustered patient cohort. These preliminary results suggest that subtype models can outperform the established NEWS2 method, providing improved prediction of patient deterioration. By considering both the computational outputs and clinician-based explanations in patient subtyping, we aim to highlight the mutual benefit of combining machine learning techniques with clinical expertise.
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