ArticleJAMA2019
New Phenotypes for Sepsis: The Promise and Problem of Applying Machine Learning and Artificial Intelligence in Clinical Research.
Article in JAMA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Association between the changing trends of platelet distribution width and in-hospital mortality in critically ill patients with sepsis: a multicenter study based on machine learning.BMC infectious diseases · 2025Article
- Sepsis subphenotypes: bridging the gaps in sepsis treatment strategies.Frontiers in immunology · 2025Review
- Development and validation of a machine learning-based model for post-sepsis frailty.ERJ open research · 2024Article
- Article
- Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future.Journal of intensive medicine · 2024Review
- Clinical Sepsis Phenotypes in Critically Ill Patients.Microorganisms · 2023Review
- Clustering analysis of geriatric and acute characteristics in a cohort of very old patients on admission to ICU.Intensive care medicine · 2022Article
- Interpretable Machine Learning to Optimize Early In-Hospital Mortality Prediction for Elderly Patients with Sepsis: A Discovery Study.Computational and mathematical methods in medicine · 2022Article
- Recent Developments in Privacy-Preserving Mining of Clinical Data.ACM/IMS transactions on data science · 2021Article
- Early detection of sepsis using artificial intelligence: a scoping review protocol.Systematic reviews · 2021Article
- Registered Trials on Artificial Intelligence Conducted in Emergency Department and Intensive Care Unit: A Cross-Sectional Study on ClinicalTrials.gov.Frontiers in medicine · 2021Article
- Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective.Frontiers in medicine · 2021Article
- Identification of acute kidney injury subphenotypes.Current opinion in critical care · 2020Review
- Survival prediction of patients with sepsis from age, sex, and septic episode number alone.Scientific reports · 2020Article
- Accuracy and Effects of Clinical Decision Support Systems Integrated With BMJ Best Practice-Aided Diagnosis: Interrupted Time Series Study.JMIR medical informatics · 2020Article
- Article
- Sepsis Induces Prolonged Epigenetic Modifications in Bone Marrow and Peripheral Macrophages Impairing Inflammation and Wound Healing.Arteriosclerosis, thrombosis, and vascular biology · 2019Article
Corrections and comments
- Comment on
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
31104067What OpenQuestion holds
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.