ArticleJournal of the American Medical Informatics Association : JAMIA2020
On classifying sepsis heterogeneity in the ICU: insight using machine learning.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.
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Who cites it
41 citing papers in PubMed, 3 syntheses or guidelines pooled it, 75 citations in OpenAlex.
- Artificial Intelligence-Based Predictive Modeling for Early Detection of Sepsis in Hospitalized Patients: A Systematic Review and Meta-Analysis.Critical care explorations · 2025Pooled it
- Revolutionizing sepsis diagnosis using machine learning and deep learning models: a systematic literature review.BMC infectious diseases · 2025Pooled it
- Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.Frontiers in medicine · 2024Pooled it
- Artificial Intelligence- and Machine Learning-Assisted Subphenotyping for Personalized Immunotherapy in Sepsis.Journal of personalized medicine · 2026Review
- Article
- Deep Learning for Early Prediction of Adverse Events in Intensive Care Units Using Electronic Health Records: A Methodological Review.Risk management and healthcare policy · 2026Review
- Elevated C-reactive protein-to-albumin ratio as an independent prognostic marker for mortality in sepsis: a multicenter cohort study.Frontiers in cellular and infection microbiology · 2026Article
- Predicting Metabolic Dysfunction-Associated Fatty Liver Disease Phenotypes Among Adults: 2-Stage Contrastive Learning Method.JMIR medical informatics · 2025Article
- Machine Learning and Artificial Intelligence in Intensive Care Medicine: Critical Recalibrations from Rule-Based Systems to Frontier Models.Journal of clinical medicine · 2025Review
- Reformulating patient stratification for targeting interventions by accounting for severity of downstream outcomes resulting from disease onset: a case study in sepsis.Journal of the American Medical Informatics Association : JAMIA · 2025Observational
- Application of causal forests to randomised controlled trial data to identify heterogeneous treatment effects: a case study.BMC medical research methodology · 2025Article
- Developing a rapid screening tool for high-risk ICU patients of sepsis: integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients-model establishment, internal and external validation, and visualization.Journal of translational medicine · 2025Article
- Sepsis subphenotypes: bridging the gaps in sepsis treatment strategies.Frontiers in immunology · 2025Review
- A Multivariate Phenotypical Approach of Sepsis and Septic Shock-A Comprehensive Narrative Literature Review.Medicina (Kaunas, Lithuania) · 2024Review
- Investigating computational models for diagnosis and prognosis of sepsis based on clinical parameters: Opportunities, challenges, and future research directions.Journal of intensive medicine · 2024Review
- Association between sub-phenotypes identified using latent class analysis and neurological outcomes in patients with out-of-hospital cardiac arrest in Japan.BMC cardiovascular disorders · 2024Article
- Soft phenotyping for sepsis via EHR time-aware soft clustering.Journal of biomedical informatics · 2024Article
- Review
- Characteristics and Cluster Analysis of 18,030 Sepsis Patients Who Were Admitted to Thailand's Largest National Tertiary Referral Center during 2014-2020 to Identify Distinct Subtypes of Sepsis in Thai Population.Critical care research and practice · 2024Article
- Machine Learning-Based Early Prediction of Sepsis Using Electronic Health Records: A Systematic Review.Journal of clinical medicine · 2023Review
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
Abstract
objectivesCurrent machine learning models aiming to predict sepsis from electronic health records (EHR) do not account 20 for the heterogeneity of the condition despite its emerging importance in prognosis and treatment. This work demonstrates the added value of stratifying the types of organ dysfunction observed in patients who develop sepsis in the intensive care unit (ICU) in improving the ability to recognize patients at risk of sepsis from their EHR data. MATERIALS AND
methodsUsing an ICU dataset of 13 728 records, we identify clinically significant sepsis subpopulations with distinct organ dysfunction patterns. We perform classification experiments with random forest, gradient boost trees, and support vector machines, using the identified subpopulations to distinguish patients who develop sepsis in the ICU from those who do not.
resultsThe classification results show that features selected using sepsis subpopulations as background knowledge yield a superior performance in distinguishing septic from non-septic patients regardless of the classification model used. The improved performance is especially pronounced in specificity, which is a current bottleneck in sepsis prediction machine learning models.
conclusionOur findings can steer machine learning efforts toward more personalized models for complex conditions including sepsis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.