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. Objective The goal of this study was to develop a VPS for clinical diagnostic reasoning that integrates interaction in natural language and an ITS. We also aimed to provide preliminary results of a short-term learning test administered on undergraduate students after use of the simulator. Methods We trained a Siamese long short-term memory network for anamnesis and NLP algorithms combined with Systematized Nomenclature of Medicine (SNOMED) ontology for diagnostic hypothesis generation. The ITS was structured on the concepts of knowledge, assessment, and learner models. To assess short-term learning changes, 15 undergraduate medical students underwent two identical tests, composed of multiple-choice questions, before and after performing a simulation by the virtual simulator. The test was made up of 22 questions; 11 of these were core questions that were specifically designed to evaluate clinical knowledge related to the simulated case. Results We developed a VPS called Hepius that allows students to gather clinical information from the patient’s medical history, physical exam, and investigations and allows them to formulate a differential diagnosis by using natural language. Hepius is also an ITS that provides real-time step-by-step feedback to the student and suggests specific topics the student has to review to fill in potential knowledge gaps. Results from the short-term learning test showed an increase in both mean test score (P<.001) and mean score for core questions (P<.001) when comparing presimulation and postsimulation performance. Conclusions By combining ITS and NLP technologies, Hepius may provide medical undergraduate students with a learning tool for training them in diagnostic reasoning. This may be particularly useful in a setting where students have restricted access to clinical wards, as is happening during the COVID-19 pandemic in many countries worldwide.

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. Records were combined in a single dataset and classified. The performance of combined multiple NLP feature selectors and classifiers was tested. Primary Outcomes: NLP algorithms’ accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F3 score. Results: 15,098 and 15,222 records from 2013 and 2015 datasets were analyzed. Syncope was present in 571 records. Normalized Gini Index feature selector combined with Support Vector Machines classifier obtained the best F3 value (84.0%), with 92.2% sensitivity and 47.4% positive predictive value. A 96% analysis time reduction was computed, compared with EMRs manual review. Conclusions: This artificial intelligence algorithm enabled the automatic identification of a large population of syncope patients using EMRs.

Right Ventricular Dysplasia constitutes a genetic cardiomyopathy characterized by fibrous-adipose substitution of the right and rarely of the left ventricular myocardium. This disorder is associated with ventricular arrhythmias ranging from frequent ventricular ectopic beats, nonsustained and sustained ventricular tachycardia of left bundle branch morphology and sudden death. Therefore, the syndrome has been labelled Arrhythmogenic RVD Cardiomyopathy. Diagnostic criteria, preliminary genetic data, and clinical manifestations are summarized and critical addressed, using data from the literature and from our own experience. The most important aspects of the ECG in this syndrome are reviewed and stressed with particular attention to initial versus advanced clinical subsets. The typical anatomical abnormalities and biopsy or pathology material are presented.

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. It is well known that frequent ventricular arrhythmias (VA) and a high incidence of sudden death (SD) can be documented in CHF patients; nevertheless a direct relationship between VA, TM, and AM has not been clearly demonstrated; neither beneficial effects, on the same endpoints, of the treatment and suppression of ambient VA in CHF. Conversely, sometimes clear negative effects on both TM and AM have been observed. According to individual studies and two recent complete and large metanalysis, ACE-I were unable to reduce AM, but they reduced TM. Furthermore, they can affect and modify many, if not all, of the triggering factors of VA and SD in this context. Differently from ACE-I, betablockers (BB) have been clearly associated with a reduction in TM and AM, in the same context. Thus, at present time, ACE-I, with or without BB, should be considered the standard therapy in all patients with CHF, if not contraindicated. Angiotensin II antagonists (AII-a) probably represent a comparably effective treatment, in all CHF patients and mainly in those patients, suffering from side effects or showing intolerance to ACE-I, but we are still lacking definitive data from RCTs. In many RCTs, conducted with traditional antiarrhythmic drug therapy (ADT), these drugs have been widely used, contributing probably, in a consistent way, to some of the positive results of these studies. All primary and some secondary implantable defibrillators (ICD) RCTs, in the prevention of SD, have included these drugs as the standard treatment of the underlying cardiac disease, with or without CHF. The same therapeutical strategy is regularly applied in all biventricular pacing (BP) RCTs, with or without the ICD. These trials are supposed to assess the reduction in TM and AM, preventing deterioration or progression of CHF and improving the quality of the patients' s life.Finally, according to these clinical evidences, in the last part of the review, we stress the need for a more widespread implementation of ACE-I and AII-a in treating CHF patients.

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. However, the lacks of external validation and reliable diagnostic standards currently limit their implementation in clinical practice. Syncope represents a challenging problem for the emergency physician both because its diagnosis is not supported by specific tests and the available prognostic tools proved to be inefficient. ML algorithms have the potential to overcome these limitations and, in the future, they could support the clinician in managing syncope patients more efficiently. However, at present only few studies have addressed this issue, albeit with encouraging results.