ReviewApplied clinical informatics2020
A Review of Predictive Analytics Solutions for Sepsis Patients.
Review in Applied clinical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- Evaluating Artificial Intelligence for Sepsis Prediction in Emergency Departments: A Systematic Review and Meta Analysis.Journal of medical systems · 2026Pooled it
- Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2022Pooled it
- Digital diagnostics, biomarkers and therapeutics in an evolving healthcare system: From promise to practice.British journal of clinical pharmacology · 2026Review
- Predictive variables for early detection of sepsis in adults admitted to intensive care units: protocol for a systematic review.BMJ open · 2026Article
- Performance of a novel medical artificial intelligence large language model on supporting decision-making for emergency patients with suspected sepsis.World journal of emergency medicine · 2025Article
- Refining a Machine Learning Model for Predicting Infant Sepsis: A Multidisciplinary Team Supported by Human-Centered Design Methods.Applied clinical informatics · 2025Article
- TCKAN: a novel integrated network model for predicting mortality risk in sepsis patients.Medical & biological engineering & computing · 2025Article
- A scoping review on pediatric sepsis prediction technologies in healthcare.NPJ digital medicine · 2024Article
- SEPRES: Intensive Care Unit Clinical Data Integration System to Predict Sepsis.Applied clinical informatics · 2023Article
- Assessing the Predictive and Analytics Capability of Electronic Clinical Data for High-Cost Patients.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Automated digital technologies for supporting sepsis prediction in children: a scoping review protocol.BMJ open · 2022Article
- Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning Approaches.Applied clinical informatics · 2022Article
- Application of a Machine Learning-Based Decision Support Tool to Improve an Injury Surveillance System Workflow.Applied clinical informatics · 2022Article
- Developing real-world evidence from real-world data: Transforming raw data into analytical datasets.Learning health systems · 2022Article
- A Graphical Toolkit for Longitudinal Dataset Maintenance and Predictive Model Training in Health Care.Applied clinical informatics · 2022Article
- Online Critical-State Detection of Sepsis Among ICU Patients using Jensen-Shannon Divergence.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Analysis of the Relevant Vital Signs and Infection of Sepsis Patients and to Explore the Influencing Factors of Acute Lung Injury/Acute Respiratory Distress Syndrome.Contrast media & molecular imaging · 2022Article
- Can Prehospital Data Improve Early Identification of Sepsis in Emergency Department? An Integrative Review of Machine Learning Approaches.Applied clinical informatics · 2022Review
- Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine.Diagnostics (Basel, Switzerland) · 2021Review
- Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective.Frontiers in medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundEarly detection and efficient management of sepsis are important for improving health care quality, effectiveness, and costs. Due to its high cost and prevalence, sepsis is a major focus area across institutions and many studies have emerged over the past years with different models or novel machine learning techniques in early detection of sepsis or potential mortality associated with sepsis.
objectiveTo understand predictive analytics solutions for sepsis patients, either in early detection of onset or mortality. METHODS AND
resultsWe performed a systematized narrative review and identified common and unique characteristics between their approaches and results in studies that used predictive analytics solutions for sepsis patients. After reviewing 148 retrieved papers, a total of 31 qualifying papers were analyzed with variances in model, including linear regression (
conclusionOverall, we found variation in the domain of predictive analytics tools for septic patients, from feature and population size to choice of method or algorithm. There are still limitations in transferability and generalizability of the algorithms or methods used. However, it is evident that implementing predictive analytics tools are beneficial in the early detection of sepsis or death related to sepsis. Since most of these studies were retrospective, the translational value in the real-world setting in different wards should be further investigated.
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.