Validation of syncope short-term outcomes prediction by machine learning models in an Italian emergency department cohort.

Machine learning (ML) algorithms have the potential to enhance the prediction of adverse outcomes in patients with syncope. Recently, gradient boosting (GB) and logistic regression (LR) models have been applied to predict these outcomes following a syncope episode, using the Canadian Syncope Risk Score (CSRS) predictors. This study aims to externally validate these models and compare their performance with novel models.

A large language model-based clinical decision support system for syncope recognition in the emergency department: A framework for clinical workflow integration.

Differentiation of syncope from transient loss of consciousness can be challenging in the emergency department (ED). Natural Language Processing (NLP) enables the analysis of free text in the electronic medical records (EMR). The present paper aimed to develop a large language models (LLM) for syncope recognition in the ED and proposed a framework for model integration within the clinical workflow.

A Hybrid Model for 30-Day Syncope Prognosis Prediction in the Emergency Department.

Syncope is a challenging problem in the emergency department (ED) as the available risk prediction tools have suboptimal predictive performances. Predictive models based on machine learning (ML) are promising tools whose application in the context of syncope remains underexplored. The aim of the present study was to develop and compare the performance of ML-based models in predicting the risk of clinically significant outcomes in patients presenting to the ED for syncope.

Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study.

Predicting clinical deterioration in COVID-19 patients remains a challenging task in the Emergency Department (ED). To address this aim, we developed an artificial neural network using textual (e.g.

On the explainability of hospitalization prediction on a large COVID-19 patient dataset.

We develop various AI models to predict hospitalization on a large (over 110k) cohort of COVID-19 positive-tested US patients, sourced from March 2020 to February 2021. Models range from Random Forest to Neural Network (NN) and Time Convolutional NN, where combination of the data modalities (tabular and time dependent) are performed at different stages (early vs. model fusion).

Learning Analytics Applied to Clinical Diagnostic Reasoning Using a Natural Language Processing-Based Virtual Patient Simulator: Case Study.

Background: Virtual patient simulators (VPSs) log all users' actions, thereby enabling the creation of a multidimensional representation of students' medical knowledge. This representation can be used to create metrics providing teachers with valuable learning information.

Objective: The aim of this study is to describe the metrics we developed to analyze the clinical diagnostic reasoning of medical students, provide examples of their application, and preliminarily validate these metrics on a class of undergraduate medical students.

Machine Learning and Syncope Management in the ED: The Future Is Coming.

In recent years, machine learning (ML) has been promisingly applied in many fields of clinical medicine, both for diagnosis and prognosis prediction. Aims of this narrative review were to summarize the basic concepts of ML applied to clinical medicine and explore its main applications in the emergency department (ED) setting, with a particular focus on syncope management. Through an extensive literature search in PubMed and Embase, we found increasing evidence suggesting that the use of ML algorithms can improve ED triage, diagnosis, and risk stratification of many diseases.

A Natural Language Processing-Based Virtual Patient Simulator and Intelligent Tutoring System for the Clinical Diagnostic Process: Simulator Development and Case Study.

Background: Shortage of human resources, increasing educational costs, and the need to keep social distances in response to the COVID-19 worldwide outbreak have prompted the necessity of clinical training methods designed for distance learning. Virtual patient simulators (VPSs) may partially meet these needs. Natural language processing (NLP) and intelligent tutoring systems (ITSs) may further enhance the educational impact of these simulators.

Artificial Intelligence Algorithms and Natural Language Processing for the Recognition of Syncope Patients on Emergency Department Medical Records.

Background: Enrollment of large cohorts of syncope patients from administrative data is crucial for proper risk stratification but is limited by the enormous amount of time required for manual revision of medical records.

Aim: To develop a Natural Language Processing (NLP) algorithm to automatically identify syncope from Emergency Department (ED) electronic medical records (EMRs).

Methods: De-identified EMRs of all consecutive patients evaluated at Humanitas Research Hospital ED from 1 December 2013 to 31 March 2014 and from 1 December 2015 to 31 March 2016 were manually annotated to identify syncope.

Do ACE inhibitors or angiotensin II antagonists reduce total mortality and arrhythmic mortality? A critical review of controlled clinical trials.

ACE-inhibitors (ACE-I) represent effective drugs more and more widely used in acute myocardial infarction (AMI) patients, in post AMI patients and mainly, today, in CHF patients.A complete review of the scientific literature and of all the randomized controlled clinical trials (RCTs), where ACE-I have been tested directly or in association with other drugs, have been performed. ACE-I effects on total mortality (TM) and arrhythmic mortality (AM) and other composite clinical endpoints have been evaluated.