bluuebunny/crossref_metadata_2025_split huggingface dataset:
A deep learning model for syncope recognition in the emergency department
Authors: Giaj Levra A , Dipaola F , Gatti M , Mene' R , Furlan R 
Published: October 2024

Abstract

Introduction
Syncope is a common presenting symptom in the emergency departments (EDs) and poses challenges in its recognition and differentiation from alternative causes of transient loss of consciousness. Natural Language Processing (NLP) is an artificial intelligence technique that enables the analysis of natural language, such as clinical notes and medical history within electronic medical records (EMRs). The application of NLP into the medical field is surging but its use for the recognition of syncope is still underexplored.

Purpose
We aimed to develop a NLP-based deep learning model for syncope recognition in the ED.

Methods
Two models, based on the Italian and Multilingual Bidirectional Encoder Representations from Transformers (BERT) language model, were developed and evaluated using consecutive EMRs from Humanitas Research Hospital ED. The first "triage" model was only based on textual data contained in the "triage" section of the EMR. The second "anamnesis" model also considered textual data contained in the "medical history" section. Performance metrics including area under the curve (AUC), Matthew’s correlation coefficient (MCC) and Brier’s score (BS) were used to compare the discriminative capabilities of different BERT models.

Results
The models were developed and tested on 15,098 and 15,222 EMRs, respectively. The Multilanguage BERT discriminated patients with syncope with an AUC of 0.85 in the triage model and of 0.92 in the anamnesis model. The Italian BERT identified patients with syncope with a MCC of 0.50 and a BS of 0.02 in the triage model and of 0.64 and 0.01, respectively, in the anamnesis model.

Conclusions
We propose two NLP-based models that identify syncope patients with a high discriminative capability. The presented algorithm may provide a starting point for the management of syncope patients using machine learning techniques.ROC and calibration curves of modelsFramework for model implementation
Machine learning for prediction of all-cause mortality in acute coronary syndrome
Authors: Zaka A , Gupta A , Mustafiz C , Mutahar D , Parvez R , Stretton B , Kovoor J , Mridha N , Bacchi S 
Published: October 2024

Abstract

Background
Acute coronary syndrome (ACS) remains one of the leading causes of death globally. Accurate and reliable mortality risk prediction of ACS patients is essential for developing targeted treatment strategies and improve prognostication. Traditional models for risk stratification such as the Global Registry of Acute Coronary Events (GRACE) and the Thrombolysis In Myocardial Infarction (TIMI) risk scores offer moderate discriminative value, and do not incorporate contemporary predictors of ACS prognosis. Machine learning (ML) models have emerged as an alternate method that may offer improved risk assessment. This study aims to compare machine learning models with traditional risk scores for predicting all-cause mortality in patients with ACS.

Methods
PubMed, EMBASE, Web of Science and Cochrane databases were searched until 1st November 2023 for studies comparing ML models with traditional statistical methods for event prediction of ACS patients. The primary outcome was comparative discrimination measured by C-statistics with 95% confidence intervals between ML models and traditional methods in estimating the risk of all-cause mortality.

Results
Ten studies were included (239627 patients). The summary C-statistic of all ML models across all endpoints was 0.89 (95% CI, 0.86-0.92), compared to traditional methods 0.82 (95% CI, 0.79-0.85). The difference in C-statistic between all ML models and traditional methods was 0.07 (p&lt;0.05). Three models undertook external validation, and calibration was inconsistently reported.

Conclusion
ML models demonstrated superior discrimination of all-cause mortality for ACS patients compared to traditional risk scores. Despite outperforming well-established prognostic tools such as the GRACE and TIMI scores, current clinical applications of ML approaches remain uncertain, particularly in view of the methodological limitations of existing models and the need for greater validation.
Assessing the impact of implantable loop recorder placement on anxiety level
Authors: Bakhshaei S , Bagheri S , Mohan A , Reddy S , Vu D , Du N , Singh B , Mehta D , Reinherz I , Ho A 
Published: October 2024

Abstract

Background
Anxiety is a prolonged state of apprehension or worry that can be an independent predictor of cardiovascular events, associated with a 48% increased risk of cardiac death. About one-third of patients with arrhythmias present with high anxiety levels, and subsequently worse quality of life. In our experience of patient care, many patients feel anxious about the possibility of an arrhythmia that could lead to their demise, especially if their symptoms are undiagnosed.

Purpose
Our project aims to explore the change in patients’ anxiety levels following implantable loop recorder (ILR) placement.

Methods
This is a survey-based observational study that was performed in an outpatient clinic and hospital using validated General Anxiety Disorder-7 (GAD-7) questionnaires. Individuals who met indications to undergo ILR implantation including palpitations, syncope, subclinical atrial fibrillation, and cryptogenic stroke underwent baseline assessment of anxiety prior to implantation. Individuals had repeat assessments at 6 weeks and 12 weeks post-implantation to evaluate the effect of ILR placement.

Results
Our study included 50 individuals. Out of the initial subjects, 10/25 females and 11/25 males were lost to follow-up. The average initial GAD-7 score was 3.3 (Range 0, 15), average 6-week score was 1.6 (Range 0, 14), and average 12-week score was 0.9 (Range 0, 10). Total decrease in GAD-7 score after 6 weeks of having ILR was 50%. Total decrease in GAD-7 score after 12 weeks of ILR was statistically significant at 73% (P=0.03). Females showed greater improvement of GAD-7 scores in comparison to males at 6 weeks (67% vs 36%) and 12 weeks (81% vs 66%).

Conclusion
Studies have suggested an overall decreased level of anxiety in patients receiving implantable devices that have the potential to treat arrhythmias such as implantable cardiac defibrillators and pacemakers. Our results suggest that the benefit may extend to implantable devices that seek to provide further diagnostic information and aid in the prognostication of disease burden. As seen in our study population, GAD-7 scores showed dramatic improvement with greater percentage in female than male population. After the placement of loop recorders, many patients experience a sense of security with having a device that monitors them. Our study is the first to address the association between ILR and anxiety. Future studies evaluating this association would help increase the power of the study.Figure 1:Bar graph of GAD-7 Scores
Time-series clustering of raw recordings of 24-hour ambulatory blood presssure for risk stratification in general population
Authors: Ntalianis E , Sabovcik F , Cauwenberghs N , Kuznetsova T 
Published: October 2024

Abstract

Objective
24-hour ambulatory blood pressure measurements (ABPM) is the best out-of-office BP measurement used to refine cardiovascular (CV) risk stratification. Currently, only summary ABPM indexes are used in the clinical practice while the temporal characteristics of the raw BP recordings have not been explored. Therefore, in this study we employed an unsupervised machine learning (ML) algorithm suitable for multivariate time-series analysis to identify distinct BP and heart rate (HR) profiles associated with the incidence of adverse CV events.

Design and Method
In 1391 community-dwelling individuals (mean age, 46.9 years; 50.97% women), we acquired 24-hour ABPM and clinical data and collected CV events (n=399) on average 22 years later. We employed multivariate dynamic time warping and k-medoids algorithm on the raw recordings of systolic and diastolic BP and HR. To assess the clinical significance of the derived clusters, we compared the clinical characteristics and the incidence of adverse events. We validated the trained model using 24-hour ABPM obtained from external population cohort (n=1137).

Results
The silhouette score and Dunn index showed that the optimal number of clusters was 4. Across all clusters we observed significant difference in age and sex distribution. Cluster 4 had the worst CV risk factors profiling, with higher office BP and anti-hypertensive medication intake compared to the other clusters. We observed differences in the incidence of adverse events between clusters (Figure), with cluster 1 representing the lowest risk group and cluster 4 the highest. After adjusting for traditional CV risk factors, including office systolic BP, cluster 1 had the lowest risk (HR:0.92; 95%CI:0.84-0.99; P=0.036) and cluster 4 the highest (HR:1.13; 95%CI:1.03-1.24; P=0.006) as compared to the average population risk. The analysis of the external validation cohort revealed similar ABPM patterns and clinical characteristics.

Conclusion
Employing an unsupervised ML model on the raw ABPM recordings we identified clinically meaningful clusters associated with an increasing cumulative incidence of CV events. This ML analysis paves the way towards utilization of temporal BP and HR information and subsequently optimization of CV risk stratification.
Using deep learning to predict cardiovascular magnetic resonance findings from echocardiography videos
Authors: Sahashi Y , Ouyang D , Kwan A 
Published: October 2024

Abstract

Background
Echocardiography is the most common modality for assessing cardiac structure and function. While cardiac magnetic resonance (CMR) imaging is less accessible, CMR can provide unique tissue characterization including late gadolinium enhancement (LGE), T1 and T2 mapping, and extracellular volume (ECV) which are associated with tissue fibrosis, infiltration, and inflammation. While deep learning has been shown to uncover findings not recognized by clinicians, it is unknown whether CMR-based tissue characteristics can be derived from echocardiography videos using deep learning.

Purpose
To assess the performance of a deep learning model applied to echocardiography to detect CMR-based parameters.

Methods
In a retrospective single-center study, adult patients with CMRs and echocardiography studies within 30 days were included. A video-based convolutional neural network was trained on echocardiography videos to predict CMR-derived labels including WMA presence, LGE presence, and abnormal T1, T2 or ECV across echocardiography views. The model performance was evaluated in a held-out test dataset not used for training.

Results
The study population included 1,453 adult patients (mean age 56±18 years, 42% female) with 2,556 paired echocardiography studies occurring on average 2 days after CMR (interquartile range 2 days prior to 6 days after). The model had high predictive capability for presence of WMA (AUC 0.873 [95%CI 0.816-0.922]), however, the model was unable to reliably detect the presence of LGE (AUC 0.699 [0.613-0.780]), native T1 (AUC 0.614 [0.500-0.715]), T2 0.553 [0.420-0.692], or ECV 0.564 [0.455-0.691]).

Conclusions
Deep learning applied to echocardiography accurately identified CMR-based WMA, but was unable to predict tissue characteristics, suggesting that signal for these tissue characteristics may not be present within ultrasound videos.Central Figure -Study Pipeline-
Rates of therapeutic interventions in a large real-world cohort with insertable cardiac monitors
Authors: De Melis M , Margetta J , Rosemas S C , Zhou J , Saha S 
Published: October 2024

Abstract

Background
Insertable cardiac monitors (ICMs) provide up to several years of continuous monitoring and have demonstrated high diagnostic yields for arrhythmia detection. However, the rates of arrhythmia-related therapeutic interventions following ICM implant have not been comprehensively characterized across indicated patient populations.

Purpose
To quantify rates of arrhythmia therapies in a large, real-world U.S. cohort of ICM patients.

Methods
De-identified patients receiving a Reveal LINQ ICM between Oct 1, 2016 – Sept 30, 2020 with no prior cardiac implantable electronic devices (CIEDs) and ≥1 year of follow-up in the Optum Clinformatics® Data Mart claims database were identified (N=17,037). Patients were stratified by indication for ICM placement: N=431 (2.5%) of patients were indicated for AF ablation monitoring, 2,193 (12.9%) for AF management, N=4,852 (28.5%) Cryptogenic stroke, N=1,252 (7.3%) Palpitations, 1,346 (7.9%) Suspected AF, 6,716 (39.4%) Unexplained syncope, and 247 (1.4%) Ventricular tachycardia (VT). Each patient was followed in the claims database after ICM insertion until end of continuous claims enrollment, end of database, or death. Therapeutic interventions for arrhythmias were characterized during follow-up, including procedures (CIED implants, cardioversion, and ablation) and arrhythmia medication initiation (antiarrhythmics, rate-control medications, and oral anticoagulants).

Results
Mean(SD) follow-up in the claims database post ICM placement was 3.4(1.3) years; 52.4% of patients were female, and mean age was 70.8(11.9) years. Overall, 54.5% of patients received a therapeutic intervention during follow-up, with 25.0% of patients receiving a therapeutic procedure and 44.0% initiating an arrhythmia-related medication (table). The highest procedure rates included implantable pulse generator (IPG) implants in the Unexplained syncope population (21.4% of patients) and cardiac ablations in the populations receiving ICM for known AF (27.8% of AF ablation monitoring patients and 23.5% of AF management patients). Medication initiation rates were highest for oral anticoagulants in the Cryptogenic stroke population (31.0% of patients). The mean time from ICM insertion to therapeutic action was 13(13) months for procedures and 7(11) months for medication initiation.

Conclusions
In a large sample of real-world ICM patients, more than half received a therapeutic intervention after ICM placement. Time to intervention was lengthy and showed substantial variability amongst patients, underscoring the importance of long-term continuous monitoring to uncover clinically-relevant arrhythmia findings.
Artificial intelligence derived electrocardiograms: a comparative study of network architectures predicting sex and left ventricular dysfunction
Authors: Cohen-Shelly M , Hochstein D , Barda N , Bleiweiss A , Aflalo E , Bar N , Zimlichman E , Klang E , Rahman N , Klempfner R , Maor E , Beinart R , Segev A , Raanani E , Sabbag A 
Published: October 2024

Abstract

Background
Within cardiovascular medicine, AI-driven electrocardiogram (ECG) analysis has emerged as a powerful tool, capable of predicting clinically significant abnormalities. The increasing growth of wearable technologies for ECG monitoring underscores the importance of advancing AI methodologies. While Convolutional Neural Networks (CNNs) have traditionally been the go-to choice for ECG analysis, the rise of Vision Transformers (ViTs), a novel computer vision paradigm, raises the question of their potential superiority.

Purpose
To compare the performance of CNNs and ViTs in ECG analysis, and to assess the efficiency of lean models using truncated ECG recordings.

Methods
We trained a model using 12-lead ECGs as input for the binary classification of two outputs: Sex (Male or Female) and Left ventricular dysfunction (LVD, defined as below 35%), determined by echocardiography within 2 weeks of the ECG. Models were developed using full standard ECGs, single lead and truncated recordings (1,2 and 4 10 seconds). We compared CNNs (PyTorch) and ViTs (HuggingFace) performance. We analyzed the explainability of our findings, representing the AI model's focus of interest on ECGs from normal cases versus those with reduced ejection fraction, thereby validating its diagnostic discernment (Fig 1)

Results
We identified 150,691 and 29,422 patients with valid ECG tests for the Sex and LVD prediction models, respectively. For the Sex outcome, the AUROCs were 0.911 (95% CI: 0.908-0.914) and 0.898 (95% CI: 0.894-0.901) for the CNN-based model and the ViT-based models, respectively, with the difference statistically significant (P-Value &lt; 0.001). For the LVD outcome, the AUROCs were comparable at 0.878 (95% CI: 0.864-0.892) and 0.866 (95% CI: 0.853-0.879) for the CNN-based model and the ViT-based models, respectively, (P-Value = 0.056). Furthermore, we found that 98.8% for the maximal predictive power of the models was achieved within a 2 second time frame of a 12 lead ECG. Similarly, a single lead based model achieved a relatively high AUC for the prediction of LVD.

Conclusions
AI-ECG models developed with ViT architecture did not surpass CNN-based models in sex classification and LVD identification. Our study highlights the significance of lean models allowing for rapid prediction within 2-second tracings or using a single lead, demonstrates the potential for streamlined AI-ECG applications in clinical practice.
Artificial intelligence in cardiology: a machine learning model for supraventricular tachycardia discrimination
Authors: De La Linde Valdes A , Rios-Munoz G , Lopez-Doriga Costales J , Carta-Bergaz A , Gonzalez-Torrecilla E , Atienza F , Arenal A , Calvo D , Gomez-Sanchez R , Bermejo Thomas J , Avila Alonso P 
Published: October 2024

Abstract

Background
Distinguishing atrioventricular nodal reentrant tachycardia (AVNRT) from orthodromic atrioventricular reentrant tachycardia (AVRT) presents a diagnostic challenge, with clinical and electrocardiogram (ECG) features playing a crucial role in differential diagnosis.

Purpose
The aim of this study was to devise and validate a predictive algorithm that utilises basic clinical and ECG parameters to accurately classify AVNRT versus AVRT. Furthermore, this algorithm was integrated into a user-friendly, web-based tool to support clinical decision-making.

Methods
We conducted a prospective analysis of 704 patients (median age: 53.6 years; 59.7% female) with electrophysiologically confirmed AVNRT (n=535) or AVRT (n=169). A comprehensive evaluation was performed using a structured questionnaire tailored to arrhythmia-specific symptoms. The study utilised advanced machine learning techniques for classification, employing feature selection methods to enhance predictive accuracy. The analysis included 19 demographic, clinical, and ECG variables, adopting an 80-20% split for training and internal validation. The algorithm's performance was evaluated against a model comprising all variables (Full Model), expert ECG interpretations (ExpertECG), objective ECG criteria (ECG model), and a baseline dummy classifier, with efficacy assessed by the Area Under the Receiver Operating Characteristic curve (AUC) and the Area Under the Precision-Recall Curve (AUPRC).

Results
The optimised model, utilising the XGBoost algorithm with six critical variables (initial age at first episode, biological sex, presence of neck palpitations, pseudoR wave in lead V1/pseudoS wave in inferior leads, visibility of retrograde P-waves, and tachycardia cycle length), exhibited superior performance, achieving an AUC of 0.916 and an AUPRC of 0.823. This model significantly outperformed other comparative models in terms of predictive accuracy (Figure 1). Implementation of the model into a web-based interface was accomplished using Shiny, enhancing its accessibility for clinical application (Figure 2).

Conclusions
This study illustrates the efficacy of employing machine learning algorithms to utilise bedside clinical and ECG variables for accurately differentiating between AVNRT and AVRT. The creation of a web-based calculator marks a significant advancement, empowering clinicians, especially non-specialists, with a potent tool for providing personalised diagnostic insights. Future research should focus on external validation to ascertain the model's generalisability and its applicability in broader clinical settings.Figure 1Figure 2
A smartphone based AI model to detect left ventricular systolic dysfunction on 12-lead ECG
Authors: Demolder A , Herman R , Vavrik B , Martonak M , Boza V , Herman M , Palus T , Kresnakova V , Bahyl J , Iring A , Hatala R , Bartunek J , Vanderheyden M , Heggermont W , Penicka M 
Published: October 2024

Abstract

Background
Despite echocardiography being essential for cardiac assessment, a point-of-care tool for rapid left ventricular ejection fraction (LVEF) evaluation is lacking in current clinical practice, highlighting the need for a widescale, easily deployable method to assess LV function.

Purpose
To develop and validate two artificial intelligence (AI) models that accurately detect reduced LVEF on a single 12-lead ECG using a smartphone, facilitating early identification of patients at risk for heart failure who may benefit from more detailed echocardiographic evaluation.

Methods
A large collection of ECGs and transthoracic echocardiograms (TTEs) was sourced from our hospital data vault from 2011 to 2021. ECGs were paired to TTEs within a 24h time window and the resulting pairs were randomly assigned to the model development dataset (50%) and validation dataset (50%). Two distinct AI-ECG models were developed: one to detect LVEF≤40% and another for LVEF&lt;50%, derived from the ESC definition of heart failure. Both AI-ECG models were paired with smartphone based ECG digitization technology. Key performance metrics include area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score.

Results
A total of 1,205,370 ECGs (198,274 patients) and 291,433 TTEs (105,849 patients) were collected and paired, resulting in 109,809 ECG-TTE pairs (56,236 patients). The validation dataset consisted of 25,510 distinct TTE-ECG pairs (25,510 patients). Prevalence of LVEF≤40% and LVEF&lt;50% was 5.4% and 7.9% respectively. The LVEF≤40% model demonstrated an AUC of 0.963 (95% CI: 0.959-0.966), sensitivity 0.924 (95% CI: 0.91-0.937), specificity 0.887 (95% CI: 0.883-0.891), and F1-score of 0.474 (95% CI: 0.457-0.490). PPV and NPV were 0.318 (95% CI: 0.304-0.333) and 0.995 (95% CI: 0.994-0.996) respectively. Performance of the LVEF &lt;50% model shows an AUC of 0.952 (95% CI: 0.947-0.956), with a slightly lower sensitivity of 0.899 (95% CI: 0.886-0.912), specificity of 0.875 (95% CI: 0.871-0.879), PPV of 0.382 (95% CI: 0.368-0.395), NPV of 0.99 (95% CI: 0.989-0.992), and F1-score of 0.536 (0.521-0.55).

Conclusion
The AI models exhibit the ability to identify reduced LVEF from standard 12-lead ECGs using a smartphone. Results suggest potential clinical utility as a preliminary screening tool to select patients for confirmatory echocardiography. This AI application could potentially offer a timely and cost-effective method for identifying patients at risk for future development of heart failure.