ReviewChest2018
Big Data and Data Science in Critical Care.
Review in Chest, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 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
122 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- From bytes to bedside: a systematic review on the use and readiness of artificial intelligence in the neonatal and pediatric intensive care unit.Intensive care medicine · 2024Pooled it
- Multisystem inflammatory syndrome in children: a microcosm of challenges and opportunities for translational bioinformatics in pediatric research.Current opinion in pediatrics · 2021Pooled it
- Assessment of Electronic Clinical Monitoring Systems in the Pediatric Intensive Care Unit: Prospective Concordance Study.JMIR formative research · 2026Article
- Development and External Validation of an Interpretable Machine Learning Framework for Predicting Pneumothorax-Associated Acute Kidney Injury: A Multicenter Retrospective Study.Journal of clinical medicine · 2026Article
- Machine learning models predicting extubation success in mechanically ventilated patients: a systematic review and meta-analysis.Intensive care medicine experimental · 2026Review
- Toward Child-Centred Artificial Intelligence in Pediatric Emergency Medicine: A Perspective on Clinical Decision Support, Stakeholder Engagement and Education.Pediatric reports · 2026Article
- Engineering biomarker representations of vital signs data enhances deep learning mortality prediction.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Construction and validation of a machine learning-based prediction model for social isolation in patients with colorectal cancer after stoma surgery.BMC gastroenterology · 2026Article
- A Framework and Method for Measuring the Implementation of Data Science in Critical Care.Critical care explorations · 2026Article
- Guidelines for the construction and management of critical care medicine in China (2025 Edition).Journal of intensive medicine · 2026Article
- Development and validation of a predictive model for myocardial injury in children with pneumonia based on interpretable machine learning.Frontiers in pediatrics · 2026Article
- Why do intensive care units in countries without board-certified clinical informatics specialists risk falling behind in the Artificial Intelligence era?Critical care science · 2026Article
- RMS: a ML-based system for ICU respiratory monitoring and resource planning.NPJ digital medicine · 2025Article
- Aggregating Patient Safety and Status Information in the Electronic Health Record to Support Time-Sensitive Mobility Interventions in the Intensive Care Unit: Protocol for the Design and Testing of a Clinical Decision Support Tool.JMIR research protocols · 2025Article
- Development and validation of machine learning-based risk prediction models for ICU-acquired weakness: a prospective cohort study.European journal of medical research · 2025Article
- The PICU Data Collaborative: A Novel, Multi-Institutional, Pediatric Critical Care Dataset.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2025Article
- Multicenter Development and Prospective Validation of eCARTv5: A Gradient-Boosted Machine-Learning Early Warning Score.Critical care explorations · 2025Observational
- Trash in/trash out? Using routinely collected clinical data for data science in the ICU: Con.Intensive care medicine · 2025Article
- Contribution of Open Access Databases to Intensive Care Medicine Research: Scoping Review.Journal of medical Internet research · 2025Article
- Establishment of a nomogram model based on immune-related genes using machine learning for aortic dissection diagnosis and immunomodulation assessment.International journal of medical sciences · 2025Article
62 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The digitalization of the health-care system has resulted in a deluge of clinical big data and has prompted the rapid growth of data science in medicine. Data science, which is the field of study dedicated to the principled extraction of knowledge from complex data, is particularly relevant in the critical care setting. The availability of large amounts of data in the ICU, the need for better evidence-based care, and the complexity of critical illness makes the use of data science techniques and data-driven research particularly appealing to intensivists. Despite the increasing number of studies and publications in the field, thus far there have been few examples of data science projects that have resulted in successful implementations of data-driven systems in the ICU. However, given the expected growth in the field, intensivists should be familiar with the opportunities and challenges of big data and data science. The present article reviews the definitions, types of algorithms, applications, challenges, and future of big data and data science in critical care.
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.