ArticleScientific reports2022
Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07163767 (Acute Myocardial Infarction Prediction Using Artificial Intelligence Applied to Electrocardiogram Images), which is not on this map. Cited by 13 papers, 2 of them syntheses that pooled it.
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.
Acute Myocardial Infarction Prediction Using Artificial Intelligence Applied to Electrocardiogram Images
Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 28 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
- Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.Frontiers in medicine · 2024Pooled it
- Evaluating deep learning sepsis prediction models in ICUs under distribution shift: a multi-centre retrospective cohort study.NPJ digital medicine · 2026Article
- Optimizing sepsis mortality prediction using hybrid federated learning and explainable AI framework.Scientific reports · 2026Article
- Bug Wars: Artificial Intelligence Strikes Back in Sepsis Management.Diagnostics (Basel, Switzerland) · 2025Review
- Early warning score and feasible complementary approach using artificial intelligence-based bio-signal monitoring system: a review.Biomedical engineering letters · 2025Review
- Machine Learning and Deep Learning Models for Early Sepsis Prediction: A Scoping Review.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2025Review
- A methodological systematic review of validation and performance of sepsis real-time prediction models.NPJ digital medicine · 2025Article
- Opinion on "Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare".Frontiers in immunology · 2025Article
- Comparison of discrimination and calibration performance of ECG-based machine learning models for prediction of new-onset atrial fibrillation.BMC medical research methodology · 2023Article
- Article
- A Review of Recent Advances in Vital Signals Monitoring of Sports and Health via Flexible Wearable Sensors.Sensors (Basel, Switzerland) · 2022Review
- Novel machine learning models to predict pneumonia events in supratentorial intracerebral hemorrhage populations: An analysis of theFrontiers in neurology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objective of this work is to develop a fusion artificial intelligence (AI) model that combines patient electronic medical record (EMR) and physiological sensor data to accurately predict early risk of sepsis. The fusion AI model has two components-an on-chip AI model that continuously analyzes patient electrocardiogram (ECG) data and a cloud AI model that combines EMR and prediction scores from on-chip AI model to predict fusion sepsis onset score. The on-chip AI model is designed using analog circuits for sepsis prediction with high energy efficiency for integration with resource constrained wearable device. Combination of EMR and sensor physiological data improves prediction performance compared to EMR or physiological data alone, and the late fusion model has an accuracy of 93% in predicting sepsis 4 h before onset. The key differentiation of this work over existing sepsis prediction literature is the use of single modality patient vital (ECG) and simple demographic information, instead of comprehensive laboratory test results and multiple vital signs. Such simple configuration and high accuracy makes our solution favorable for real-time, at-home use for self-monitoring.
Indexed as
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.