ArticleNature machine intelligence2023
Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis.
Article in Nature machine intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 29 citations in OpenAlex.
- Diagnostic Uncertainty and Antibiotic Timing in Sepsis: Observational Validation of a Dual-Axis Decision Model.Critical care explorations · 2026Observational
- A generalist precision medication framework using temporal causal inference based on treatment-free physiological profiles.Nature communications · 2026Article
- Prediction of preterm and low birth weight risk using a physiology based artificial neural network integrating hematological, dental, and periodontal index markers: a cross sectional study based on machine learning.BMC pregnancy and childbirth · 2026Article
- Progression-free survival outcomes of PARP inhibitors in ovarian cancer: an exploratory analysis of treatment heterogeneity based on organ vulnerability.Frontiers in oncology · 2026Article
- Observational
- Explainable AI unravels sepsis heterogeneity via coagulation-inflammation profiles for prognosis and stratification.Nature communications · 2025Observational
- External validation of a widely available, localizable sepsis early detection model in the emergency department setting.The American journal of emergency medicine · 2025Article
- AI-based identification of patients who benefit from revascularization: a multicenter study.medRxiv : the preprint server for health sciences · 2025Article
- Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap.NPJ digital medicine · 2025Review
- A machine learning and centrifugal microfluidics platform for bedside prediction of sepsis.Nature communications · 2025Article
- Change of Heart: Can Artificial Intelligence Transform Infective Endocarditis Management?Pathogens (Basel, Switzerland) · 2025Review
- Opinion on "Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare".Frontiers in immunology · 2025Article
- Gut Microbiota Metabolites Targeting the Immune Response in Sepsis: Mechanisms and Therapies.International journal of general medicine · 2025Review
- An optimal antibiotic selection framework for Sepsis patients using Artificial Intelligence.NPJ digital medicine · 2024Article
- G-Transformer: Counterfactual Outcome Prediction under Dynamic and Time-varying Treatment Regimes.Proceedings of machine learning research · 2024Article
- Causal machine learning for predicting treatment outcomes.Nature medicine · 2024Review
- The potential immunological mechanisms of sepsis.Frontiers in immunology · 2024Review
- The application of artificial intelligence in the management of sepsis.Medical review (2021) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Sepsis is a life-threatening condition with a high in-hospital mortality rate. The timing of antibiotic administration poses a critical problem for sepsis management. Existing work studying antibiotic timing either ignores the temporality of the observational data or the heterogeneity of the treatment effects. Here we propose a novel method (called T4) to estimate treatment effects for time-to-treatment antibiotic stewardship in sepsis. T4 estimates individual treatment effects by recurrently encoding temporal and static variables as potential confounders, and then decoding the outcomes under different treatment sequences. We propose mini-batch balancing matching that mimics the randomized controlled trial process to adjust the confounding. The model achieves interpretability through a global-level attention mechanism and a variable-level importance examination. Meanwhile, we equip T4 with an uncertainty quantification to help prevent overconfident recommendations. We demonstrate that T4 can identify effective treatment timing with estimated individual treatment effects for antibiotic stewardship on two real-world datasets. Moreover, comprehensive experiments on a synthetic dataset exhibit the outstanding performance of T4 compared with the state-of-the-art models on estimation of individual treatment effect.
Identifiers
What 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.