Observational studyJournal of medical Internet research2021
Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study.
Observational study in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 23 citations in OpenAlex.
- Machine Learning and Deep Learning Frameworks for Human-Virus Protein-Protein Interaction Prediction: Emerging Architectures, Methods, Benchmarks, and Challenges.International journal of molecular sciences · 2026Review
- Machine learning prediction of surgical approach and clinical heterogeneity in cervical spondylotic myelopathy.BMC musculoskeletal disorders · 2026Article
- An Introduction to AI for Clinicians: Tutorial.Interactive journal of medical research · 2026Article
- A Scoping Review of Machine Learning Applications Across Epidemiological Stages of Zoonotic Disease.Transboundary and emerging diseases · 2026Article
- Construction of a predictive model for blood transfusion in patients undergoing total hip arthroplasty and identification of clinical heterogeneity.Scientific reports · 2024Article
- COVID-19 patient profiles over four waves in Barcelona metropolitan area: A clustering approach.PloS one · 2024Article
- Individual-level precision diagnosis for coronavirus disease 2019 related severe outcome: an early study in New York.Scientific reports · 2023Article
- Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning.Annals of medicine · 2023Article
- Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19.Frontiers in medicine · 2023Article
- Observational
- Machine learning-based clustering in cervical spondylotic myelopathy patients to identify heterogeneous clinical characteristics.Frontiers in surgery · 2022Article
- Cluster-based analysis of COVID-19 cases using self-organizing map neural network and K-means methods to improve medical decision-making.Informatics in medicine unlocked · 2022Article
- Impact of COVID-19 on a brain damage unit.Diabetes & metabolic syndromeArticle
- Article
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Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly detection and intervention are the key factors for improving outcomes in patients with COVID-19.
objectiveThe objective of this observational longitudinal study was to identify nonoverlapping severity subgroups (ie, clusters) among patients with COVID-19, based exclusively on clinical data and standard laboratory tests obtained during patient assessment in the emergency department.
methodsWe applied unsupervised machine learning to a data set of 853 patients with COVID-19 from the HM group of hospitals (HM Hospitales) in Madrid, Spain. Age and sex were not considered while building the clusters, as these variables could introduce biases in machine learning algorithms and raise ethical implications or enable discrimination in triage protocols.
resultsFrom 850 clinical and laboratory variables, four tests-the serum levels of aspartate transaminase (AST), lactate dehydrogenase (LDH), C-reactive protein (CRP), and the number of neutrophils-were enough to segregate the entire patient pool into three separate clusters. Further, the percentage of monocytes and lymphocytes and the levels of alanine transaminase (ALT) distinguished cluster 3 patients from the other two clusters. The highest proportion of deceased patients; the highest levels of AST, ALT, LDH, and CRP; the highest number of neutrophils; and the lowest percentages of monocytes and lymphocytes characterized cluster 1. Cluster 2 included a lower proportion of deceased patients and intermediate levels of the previous laboratory tests. The lowest proportion of deceased patients; the lowest levels of AST, ALT, LDH, and CRP; the lowest number of neutrophils; and the highest percentages of monocytes and lymphocytes characterized cluster 3.
conclusionsA few standard laboratory tests, deemed available in all emergency departments, have shown good discriminative power for the characterization of severity subgroups among patients with COVID-19.
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