AIMC Topic: Emergency Medical Services
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Enhancing the clinical relevance of haemorrhage prediction models in trauma.
Military Medical Research
Sep 20, 2023
Science fiction or clinical reality: a review of the applications of artificial intelligence along the continuum of trauma care.
World journal of emergency surgery : WJES
Mar 6, 2023
Artificial intelligence (AI) and machine learning describe a broad range of algorithm types that can be trained based on datasets to make predictions. The increasing sophistication of AI has created new opportunities to apply these algorithms within ...
Artificial intelligence and machine learning for hemorrhagic trauma care.
Military Medical Research
Feb 16, 2023
Artificial intelligence (AI), a branch of machine learning (ML) has been increasingly employed in the research of trauma in various aspects. Hemorrhage is the most common cause of trauma-related death. To better elucidate the current role of AI and c...
Deep Learning-Based Emergency Care Process Reengineering of Interventional Data for Patients with Emergency Time-Series Events of Myocardial Infarction.
Journal of healthcare engineering
Feb 23, 2022
This paper proposes a representation learning framework HE-LSTM model for heterogeneous temporal events, which can automatically adapt to the multiscale sampling frequency of multisource heterogeneous data. The proposed model also demonstrates its su...
A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study.
Scientific reports
Oct 15, 2021
High precision is optimal in prehospital diagnostic algorithms for strokes and large vessel occlusions. We hypothesized that prehospital diagnostic algorithms for strokes and their subcategories using machine learning could have high predictive value...
Prediction of Neurological Outcomes in Out-of-hospital Cardiac Arrest Survivors Immediately after Return of Spontaneous Circulation: Ensemble Technique with Four Machine Learning Models.
Journal of Korean medical science
Jul 19, 2021
BACKGROUND: We performed this study to establish a prediction model for 1-year neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients who achieved return of spontaneous circulation (ROSC) immediately after ROSC using machine learning...
Improving Prehospital Stroke Diagnosis Using Natural Language Processing of Paramedic Reports.
Stroke
Jun 24, 2021
BACKGROUND AND PURPOSE: Accurate prehospital diagnosis of stroke by emergency medical services (EMS) can increase treatments rates, mitigate disability, and reduce stroke deaths. We aimed to develop a model that utilizes natural language processing o...
Machine learning algorithms trained with pre-hospital acquired history-taking data can accurately differentiate diagnoses in patients with hip complaints.
Acta orthopaedica
Feb 12, 2021
Background and purpose - Machine learning (ML) techniques are a form of artificial intelligence able to analyze big data. Analyzing the outcome of (digital) questionnaires, ML might recognize different patterns in answers that might relate to differe...
Estimating Nonfatal Gunshot Injury Locations With Natural Language Processing and Machine Learning Models.
JAMA network open
Oct 1, 2020
IMPORTANCE: Nonfatal gunshot injuries are the most common firearm injury, but where they frequently occur remains unclear owing to data limitations. Natural language processing can be applied to medical text narratives of gunshot injury records to cl...
Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea.
Circulation. Arrhythmia and electrophysiology
Aug 4, 2020
BACKGROUND: Identification of systolic heart failure among patients presenting to the emergency department (ED) with acute dyspnea is challenging. The reasons for dyspnea are often multifactorial. A focused physical evaluation and diagnostic testing ...
Heart Failure, Systolic
Systole
Cardiology Service, Hospital
Ventricular Function, Left
Female
Signal Processing, Computer-Assisted
Diagnosis, Computer-Assisted
Humans
Stroke Volume
Reproducibility of Results
Dyspnea
Artificial Intelligence
Electrocardiography
Middle Aged
Ventricular Dysfunction, Left
Aged
Retrospective Studies
Predictive Value of Tests
Male
Emergency Medical Services
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