ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2021
A novel artificial intelligence based intensive care unit monitoring system: using physiological waveforms to identify sepsis.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.
- AI-Based Sepsis Prediction in Hospitalized Adults: Systematic Review, Subgroup Meta-Analysis, and Contextual Analysis of Clinical Burden.Journal of medical Internet research · 2026Pooled it
- Morphological changes in the arterial pressure waveform following hemodynamic therapies in critical care: A clinical proof-of-concept study in older adults.Physiological reports · 2026Observational
- Physiological Data Integration and Predictive Modeling in Intensive Care.Life (Basel, Switzerland) · 2026Review
- Early prediction of sepsis in the ICU: a comparative analysis of multiple machine-learning algorithms using the MIMIC-III database.BMC medical informatics and decision making · 2026Article
- Sepsis insult generates different vascular response phenotypes: an unsupervised time-series clustering preclinical study.Scientific reports · 2026Article
- Organ cross-talk in anaesthesia: Heart, brain, kidney, lung interactions, and the emerging role of artificial intelligence.Indian journal of anaesthesia · 2026Article
- Artificial intelligence based predictive models for early sepsis detection in intensive care units: a scoping review.Frontiers in digital health · 2026Review
- Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review.Health data science · 2026Review
- Early sepsis mortality prediction model based on interpretable machine learning approach: development and validation study.Internal and emergency medicine · 2025Article
- Beyond the Beat: A Multifaceted Review of Atrial Fibrillation in Sepsis: Risk Factors, Management Strategies, and Economic Impact.Cardiology research · 2025Review
- User Acceptance of a Home Robotic Assistant for Individuals With Physical Disabilities: Explorative Qualitative Study.JMIR rehabilitation and assistive technologies · 2025Article
- Enhancing Infection Control in ICUS Through AI: A Literature Review.Health science reports · 2025Article
- Use of a continuous single lead electrocardiogram analytic to predict patient deterioration requiring rapid response team activation.PLOS digital health · 2024Article
- Parsimonious Waveform-derived Features consisting of Pulse Arrival Time and Heart Rate Variability Predicts the Onset of Septic Shock.Biomedical signal processing and control · 2024Article
- A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability.Critical care (London, England) · 2024Article
- [Artificial intelligence in intensive care medicine].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2024Review
- Review
- Identifying prognostic factors for survival in intensive care unit patients with SIRS or sepsis by machine learning analysis on electronic health records.PLOS digital health · 2024Article
- Assessment of Sepsis Risk at Admission to the Emergency Department: Clinical Interpretable Prediction Model.Diagnostics (Basel, Switzerland) · 2024Article
- Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring.Frontiers in medicine · 2024Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
A massive amount of multimodal data are continuously collected in the intensive care unit (ICU) along each patient stay, offering a great opportunity for the development of smart monitoring devices based on artificial intelligence (AI). The two main sources of relevant information collected in the ICU are the electronic health records (EHRs) and vital sign waveforms continuously recorded at the bedside. While EHRs are already widely processed by AI algorithms for prompt diagnosis and prognosis, AI-based assessments of the patients' pathophysiological state using waveforms are less developed, and their use is still limited to real-time monitoring for basic visual vital sign feedback at the bedside. This study uses data from the MIMIC-III database (PhysioNet) to propose a novel AI approach in ICU patient monitoring that incorporates features estimated by a closed-loop cardiovascular model, with the specific goal of identifying sepsis within the first hour of admission. Our top benchmark results (AUROC = 0.92, AUPRC = 0.90) suggest that features derived by cardiovascular control models may play a key role in identifying sepsis, by continuous monitoring performed through advanced multivariate modelling of vital sign waveforms. This work lays foundations for a deeper data integration paradigm which will help clinicians in their decision-making processes. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.
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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.