ArticleNature communications2025
Identification of predictive subphenotypes for clinical outcomes using real world data and machine learning.
Article in Nature communications, 2025. 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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Who cites it
10 citing papers in PubMed.
- Heart Failure sub-phenotyping and in-hospital and 28-day mortality prediction based on mean arterial pressure trajectory modeling.American heart journal plus : cardiology research and practice · 2026Article
- A multicenter study on the prediction model for chronic low back pain after lumbar decompression surgery in patients with diabetes mellitus: integration of metabolic and paraspinal muscle features.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- The role of AI in oncology: present applications and future horizons.NPJ precision oncology · 2026Review
- Five tenets for advancing evidence-based precision medicine.Nature medicine · 2026Review
- Stratifying mortality risk in hypotension using outcome-oriented clustering of vital sign trajectories.iScience · 2026Article
- Clinical phenotypes and risk stratification for limb-threatening outcomes in acute compartment syndrome: a multicenter externally validated study.Frontiers in artificial intelligence · 2026Article
- Application of explainable artificial intelligence integrating with electronic health record in oncology.Exploration of targeted anti-tumor therapy · 2026Review
- Unsupervised clustering based on a graph attention network reveals high-risk subgroups among ICU dementia patients for enhanced mortality prediction.European journal of medical research · 2025Article
- Prognostic risk factors and a survival prediction model for immune checkpoint inhibitor related myocarditis in patients with lung cancer: a multicenter study.Scientific reports · 2025Observational
- Identification of predictive subphenotypes for clinical outcomes using real world data and machine learning.Nature communications · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting treatment response is an important problem in real-world applications, where the heterogeneity of the treatment response remains a significant challenge in practice. Unsupervised machine learning methods have been proposed to address this challenge by clustering patients with similar electronic health record (EHR) data. However, they cannot guarantee coherent outcomes within the groups. Here, we propose Graph-Encoded Mixture Survival (GEMS) as a general machine learning framework to identify distinct predictive subphenotypes that guarantee coherent survival and baseline characteristics within each subphenotype. We apply our method to a real-world dataset of advanced non-small cell lung cancer (aNSCLC) patients receiving first-line immune checkpoint inhibitor (ICI) therapy to predict overall survival (OS). Our method outperforms baseline methods for predicting OS and identifies three reproducible subphenotypes associated with distinct baseline clinical characteristics and OS. Our results demonstrate that our method can provide insights in the heterogeneity of treatment response and potentially influence treatment selection.
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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.