ReviewThe western journal of emergency medicine2019
Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview.
Review in The western journal of emergency medicine, 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.
- Hierarchical Poincaré analysis for anaesthesia monitoring.Journal of clinical monitoring and computing · 2020Trial
- Machine learning-based prediction of postoperative mortality risk after abdominal surgery.World journal of gastrointestinal surgery · 2025Article
- Current Applications, Challenges, and Future Directions of Artificial Intelligence in Emergency Medicine: A Narrative Review.Archives of academic emergency medicine · 2025Article
- Application of artificial intelligence in osteoporosis: a review.Frontiers in medicine · 2025Review
- Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool.Cureus · 2024Review
- Development and Validation of a Prognostic Nomogram for Patients with AFP and DCP Double-Negative Hepatocellular Carcinoma After Local Ablation.Journal of hepatocellular carcinoma · 2024Article
- Effect of Applying a Real-Time Medical Record Input Assistance System With Voice Artificial Intelligence on Triage Task Performance in the Emergency Department: Prospective Interventional Study.JMIR medical informatics · 2022Article
- Development and Validation of a Natural Language Processing Tool to Identify Injuries in Infants Associated With Abuse.Academic pediatrics · 2022Article
- Using machine learning to predict subsequent events after EMS non-conveyance decisions.BMC medical informatics and decision making · 2022Article
- Machine learning approach to predict acute kidney injury after liver surgery.World journal of clinical cases · 2021Article
- Automated and accurate assessment for postural abnormalities in patients with Parkinson's disease based on Kinect and machine learning.Journal of neuroengineering and rehabilitation · 2021Article
- Review
- Impact of Big Data Analytics on People's Health: Overview of Systematic Reviews and Recommendations for Future Studies.Journal of medical Internet research · 2021Article
- Ultrasound Images Guided under Deep Learning in the Anesthesia Effect of the Regional Nerve Block on Scapular Fracture Surgery.Journal of healthcare engineering · 2021Article
- High-precision multiclass cell classification by supervised machine learning on lectin microarray data.Regenerative therapy · 2020Article
- Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches.Journal of clinical medicine · 2020Article
- Predicting outcome for ambulance patients with dyspnea: a prospective cohort study.Journal of the American College of Emergency Physicians open · 2020Article
- Natural Language Processing - A Surveillance Stepping Stone to Identify Child Abuse.Academic pediatricsArticle
- Performance of emergency triage prediction of an open access natural language processing based chatbot application (ChatGPT): A preliminary, scenario-based cross-sectional study.Turkish journal of emergency medicineArticle
- Artificial intelligence and machine learning in emergency medicine: a narrative review.Acute medicine & surgeryReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Health informatics is a vital technology that holds great promise in the healthcare setting. We describe two prominent health informatics tools relevant to emergency care, as well as the historical background and the current state of informatics. We also identify recent research findings and practice changes. The recent advances in machine learning and natural language processing (NLP) are a prominent development in health informatics overall and relevant in emergency medicine (EM). A basic comprehension of machine-learning algorithms is the key to understand the recent usage of artificial intelligence in healthcare. We are using NLP more in clinical use for documentation. NLP has started to be used in research to identify clinically important diseases and conditions. Health informatics has the potential to benefit both healthcare providers and patients. We cover two powerful tools from health informatics for EM clinicians and researchers by describing the previous successes and challenges and conclude with their implications to emergency 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.