ArticleProceedings of the IEEE. Institute of Electrical and Electronics Engineers2016
Machine Learning and Decision Support in Critical Care.
Article in Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 141 papers, 8 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
141 citing papers in PubMed, 8 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- Pooled it
- Designing Clinical Decision Support Systems (CDSS)-A User-Centered Lens of the Design Characteristics, Challenges, and Implications: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review.PloS one · 2024Pooled it
- Machine learning-based prediction models for pressure injury: A systematic review and meta-analysis.International wound journal · 2023Pooled it
- Randomized Clinical Trials of Machine Learning Interventions in Health Care: A Systematic Review.JAMA network open · 2022Pooled it
- Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit.Intensive care medicine · 2021Pooled it
- Use of machine learning to analyse routinely collected intensive care unit data: a systematic review.Critical care (London, England) · 2019Pooled it
- Novel displays of patient information in critical care settings: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Machine Learning Analysis to Identify Digital Behavioral Phenotypes for Engagement and Health Outcome Efficacy of an mHealth Intervention for Obesity: Randomized Controlled Trial.Journal of medical Internet research · 2021Trial
- Development of an Explainable Machine Learning Model to Predict Mortality Risk in Sepsis Patients: Insights From a Real-World Clinical Data.Shock (Augusta, Ga.) · 2026Article
- Development and Validation of a Multi-Modal Ensemble Model for Predicting Progression in Idiopathic Scoliosis.Global spine journal · 2026Article
- An empirical study of machine learning robustness and scalability for imbalanced tabular clinical data in emergency and critical care.Scientific reports · 2026Article
- Toward generalizable and interpretable machine learning models in healthcare: Insights from ICU outcome predictions.Health care management science · 2026Article
- The Geometry of Circulatory Shock: A Conceptual Multi-Scale Lagrangian Framework for Physiology-Informed Hemodynamic Phenotyping.Journal of clinical medicine · 2026Review
- Article
- Revolutionizing Heart Failure Management With Artificial Intelligence: A Narrative Review of Diagnostic, Prognostic, and Therapeutic Innovations.Health science reports · 2026Article
- Contrastive Transformer-Driven Discovery of Temporal Hemodynamic Subphenotypes in Cardiac Surgery Patients.medRxiv : the preprint server for health sciences · 2026Article
- Machine Learning Models for Sepsis: From Early Detection to Short- and Long-Term Prognosis.International journal of molecular sciences · 2026Article
- Predicting Cardiovascular Collapse in Critically Ill Patients During Intubation Induction: A Prospective Observational Study.Medicina (Kaunas, Lithuania) · 2026Observational
- Hybrid phenotype-guided modeling across algorithm-feature regimes with application to ICU mortality prediction for Acinetobacter baumannii.Scientific reports · 2026Article
81 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
6 authors at 2 institutions in 2 countries.
Funding
Abstract
Clinical data management systems typically provide caregiver teams with useful information, derived from large, sometimes highly heterogeneous, data sources that are often changing dynamically. Over the last decade there has been a significant surge in interest in using these data sources, from simply re-using the standard clinical databases for event prediction or decision support, to including dynamic and patient-specific information into clinical monitoring and prediction problems. However, in most cases, commercial clinical databases have been designed to document clinical activity for reporting, liability and billing reasons, rather than for developing new algorithms. With increasing excitement surrounding "secondary use of medical records" and "Big Data" analytics, it is important to understand the limitations of current databases and what needs to change in order to enter an era of "precision medicine." This review article covers many of the issues involved in the collection and preprocessing of critical care data. The three challenges in critical care are considered: compartmentalization, corruption, and complexity. A range of applications addressing these issues are covered, including the modernization of static acuity scoring; on-line patient tracking; personalized prediction and risk assessment; artifact detection; state estimation; and incorporation of multimodal data sources such as genomic and free text data.
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.