ArticleInformatics (MDPI)2021
Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review.
Article in Informatics (MDPI), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis 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
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Trends and methods in intensive care unit (ICU) research using machine learning: latent dirichlet allocation (LDA)-based thematic literature review.BMC medical informatics and decision making · 2025Pooled it
- Comparison of large language models and conventional machine learning in postoperative outcome prediction: a retrospective, multi-national development and validation study.Korean journal of anesthesiology · 2026Article
- Artificial Intelligence in Intensive Care: An Overview of Systematic Reviews with Clinical Maturity and Readiness Mapping.Journal of clinical medicine · 2025Review
- Large Language Models in Critical Care Medicine: Scoping Review.JMIR medical informatics · 2025Article
- Validation of a cancer population derived AKI machine learning algorithm in a general critical care scenario.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study.Journal of medical Internet research · 2025Article
- The evolving role of nursing informatics in the era of artificial intelligence.International nursing review · 2025Review
- Predicting Agitation-Sedation Levels in Intensive Care Unit Patients: Development of an Ensemble Model.JMIR medical informatics · 2025Article
- Contribution of Open Access Databases to Intensive Care Medicine Research: Scoping Review.Journal of medical Internet research · 2025Article
- Predicting mortality in critically ill patients with hypertension using machine learning and deep learning models.Frontiers in cardiovascular medicine · 2025Article
- Early prediction of mortality upon intensive care unit admission.BMC medical informatics and decision making · 2024Article
- Development and validation of a deep learning algorithm for the prediction of serum creatinine in critically ill patients.JAMIA open · 2024Article
- Short-term vital parameter forecasting in the intensive care unit: A benchmark study leveraging data from patients after cardiothoracic surgery.PLOS digital health · 2024Article
- Using Domain Adaptation and Inductive Transfer Learning to Improve Patient Outcome Prediction in the Intensive Care Unit: Retrospective Observational Study.Journal of medical Internet research · 2024Observational
- The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review.Journal of medical Internet research · 2024Article
- Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.Circulation · 2024Review
- Early prediction of mortality at sepsis diagnosis time in critically ill patients by using interpretable machine learning.Journal of clinical monitoring and computing · 2024Article
- Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape.Critical care clinics · 2023Review
- Medical transformer for multimodal survival prediction in intensive care: integration of imaging and non-imaging data.Scientific reports · 2023Article
- MIMIC-IV on FHIR: converting a decade of in-patient data into an exchangeable, interoperable format.Journal of the American Medical Informatics Association : JAMIA · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Modern Intensive Care Units (ICUs) provide continuous monitoring of critically ill patients susceptible to many complications affecting morbidity and mortality. ICU settings require a high staff-to-patient ratio and generates a sheer volume of data. For clinicians, the real-time interpretation of data and decision-making is a challenging task. Machine Learning (ML) techniques in ICUs are making headway in the early detection of high-risk events due to increased processing power and freely available datasets such as the Medical Information Mart for Intensive Care (MIMIC). We conducted a systematic literature review to evaluate the effectiveness of applying ML in the ICU settings using the MIMIC dataset. A total of 322 articles were reviewed and a quantitative descriptive analysis was performed on 61 qualified articles that applied ML techniques in ICU settings using MIMIC data. We assembled the qualified articles to provide insights into the areas of application, clinical variables used, and treatment outcomes that can pave the way for further adoption of this promising technology and possible use in routine clinical decision-making. The lessons learned from our review can provide guidance to researchers on application of ML techniques to increase their rate of adoption in healthcare.
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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.