ReviewBiomedical engineering and computational biology2019
Critical Care, Critical Data.
Review in Biomedical engineering and computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Evaluating the generalisability of LiSep LSTM for early prediction of septic shock across US and european cohorts.Scientific reports · 2026Article
- Echo Chambers: Bias and Representation in Cardiac Imaging Datasets for Artificial Intelligence.Current cardiology reports · 2026Review
- A Framework and Method for Measuring the Implementation of Data Science in Critical Care.Critical care explorations · 2026Article
- Building the future of ICU care: Is our digital foundation strong enough? A multicentre survey of Australian and New Zealand intensive care units.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2025Article
- A Systematic Comparison of Age, Comorbidity and Frailty of Two Defined ICU Populations in the German Helios Hospital Group from 2016-2021.Journal of clinical medicine · 2025Article
- From intensive care monitors to cloud environments: a structured data pipeline for advanced clinical decision support.EBioMedicine · 2025Article
- A model to simulate human cardio-respiratory responses to ketamine analgesia.Frontiers in physiology · 2025Article
- Standardized Comparison of Voice-Based Information and Documentation Systems to Established Systems in Intensive Care: Crossover Study.JMIR medical informatics · 2023Article
- A dosing strategy model of deep deterministic policy gradient algorithm for sepsis patients.BMC medical informatics and decision making · 2023Article
- Prediction of Acid-Base and Potassium Imbalances in Intensive Care Patients Using Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2023Article
- Individual illness dynamics: An analysis of children with sepsis admitted to the pediatric intensive care unit.PLOS digital health · 2022Article
- Ground truth labels challenge the validity of sepsis consensus definitions in critical illness.Journal of translational medicine · 2022Article
- Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example.Critical care medicine · 2021Article
- Artificial intelligence-based prediction of transfusion in the intensive care unit in patients with gastrointestinal bleeding.BMJ health & care informatics · 2021Article
- Trends, Technologies, and Key Challenges in Smart and Connected Healthcare.IEEE access : practical innovations, open solutions · 2021Article
- Assessing delivery of mechanical ventilation: risks and benefits of large databases.Intensive care medicine · 2020Article
- Digital microbiology.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2020Review
- Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review.Pulmonary therapy · 2020Review
- Data sharing in the era of COVID-19.The Lancet. Digital health · 2020Article
- The advent of medical artificial intelligence: lessons from the Japanese approach.Journal of intensive care · 2020Review
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
No grant is acknowledged in the PubMed record.
Abstract
As
Indexed as
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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.