Medical Visual Question Answering (VQA) is a combination of medical artificial intelligence and popular VQA challenges. Given a medical image and a clinically relevant question in natural language, the medical VQA system is expected to predict a plausible and convincing answer. Although the general-domain VQA has been extensively studied, the medical VQA still needs specific investigation and exploration due to its task features. In the first part of this survey, we collect and discuss the publicly available medical VQA datasets up-to-date about the data source, data quantity, and task feature. In the second part, we review the approaches used in medical VQA tasks. We summarize and discuss their techniques, innovations, and potential improvements. In the last part, we analyze some medical-specific challenges for the field and discuss future research directions. Our goal is to provide comprehensive and helpful information for researchers interested in the medical visual question answering field and encourage them to conduct further research in this field.

The world has recently been exposed to a fierce attack from many viral diseases, such as Covid-19, that exhausted medical systems around the world. Such attack had a negative impact not only on the health status of people or the high death rate, but also had a bad impact on the economic situation, which affected all countries of the world especially the poor and the developing ones. Monkeypox is one of the latest viral diseases that may cause a pandemic in the near future if not dealt and diagnosed with appropriately. This paper provides a new strategy for diagnosing monkeypox, which is called; Accurate Monkeypox Diagnosing Strategy (AMDS). The proposed AMDS consists of two phases, which are; (i) pre-processing and (ii) classification. During the pre-processing phase, the most effective feature are selected using Binary Tiki-Taka Algorithm (BTTA). On the other hand, in the classification phase, ensemble classification is used for diagnosing new cases, which combines evidence from three different new classifiers, namely; (a) Layered K-Nearest Neighbors (LKNN), (b) Statistical Naïve Bayes (SNB), and (c) Deep Learning Classifier (DLC). Moreover, the decisions of the proposed classifiers are merged in a new voting scheme called Fuzzified Voting Scheme (FVS). AMDS has been compared against recent diagnostic strategies. Experimental results have proven that AMDS outperforms other monkeypox diagnostic strategies as it introduces the most accurate diagnosis according to two different datasets.

Nowadays, it is really important and crucial to follow the new biomedical knowledge that is presented in scientific literature. To this end, Information Extraction pipelines can help to automatically extract meaningful relations from textual data that further require additional checks by domain experts. In the last two decades, a lot of work has been performed for extracting relations between phenotype and health concepts, however, the relations with food entities which are one of the most important environmental concepts have never been explored. In this study, we propose FooDis, a novel Information Extraction pipeline that employs state-of-the-art approaches in Natural Language Processing to mine abstracts of biomedical scientific papers and automatically suggests potential cause or treat relations between food and disease entities in different existing semantic resources. A comparison with already known relations indicates that the relations predicted by our pipeline match for 90% of the food-disease pairs that are common in our results and the NutriChem database, and 93% of the common pairs in the DietRx platform. The comparison also shows that the FooDis pipeline can suggest relations with high precision. The FooDis pipeline can be further used to dynamically discover new relations between food and diseases that should be checked by domain experts and further used to populate some of the existing resources used by NutriChem and DietRx.

Training deep neural network classifiers for electrocardiograms (ECGs) requires sufficient data. However, imbalanced datasets pose a major problem for the training process and hence data augmentation is commonly performed. Generative adversarial networks (GANs) can create synthetic ECG data to augment such imbalanced datasets. This review aims at identifying the present literature concerning synthetic ECG signal generation using GANs to provide a comprehensive overview of architectures, quality evaluation metrics, and classification performances. Thirty publications from the years 2019 to 2022 were selected from three separate databases. Nine publications used a quality evaluation metric neglecting classification, eleven performed a classification but omitted a quality evaluation metric, and ten publications performed both. Twenty different quality evaluation metrics were observed. Overall, the classification performance of databases augmented with synthetically created ECG signals increased by 7 % to 98 % in accuracy and 6 % to 97 % in sensitivity. In conclusion, synthetic ECG signal generation using GANs represents a promising tool for data augmentation of imbalanced datasets. Consistent quality evaluation of generated signals remains challenging. Hence, future work should focus on the establishment of a gold standard for quality evaluation metrics for GANs.

Brain segmentation is often the first and most critical step in quantitative analysis of the brain for many clinical applications, including fetal imaging. Different aspects challenge the segmentation of the fetal brain in magnetic resonance imaging (MRI), such as the non-standard position of the fetus owing to his/her movements during the examination, rapid brain development, and the limited availability of imaging data. In recent years, several segmentation methods have been proposed for automatically partitioning the fetal brain from MR images. These algorithms aim to define regions of interest with different shapes and intensities, encompassing the entire brain, or isolating specific structures. Deep learning techniques, particularly convolutional neural networks (CNNs), have become a state-of-the-art approach in the field because they can provide reliable segmentation results over heterogeneous datasets. Here, we review the deep learning algorithms developed in the field of fetal brain segmentation and categorize them according to their target structures. Finally, we discuss the perceived research gaps in the literature of the fetal domain, suggesting possible future research directions that could impact the management of fetal MR images.

Electronic systems are increasingly present in the healthcare system and are often related to improved medical care. However, the widespread use of these technologies ended up building a relationship of dependence that can disrupt the doctor-patient relationship. In this context, digital scribes are automated clinical documentation systems that capture the physician-patient conversation and then generate the documentation for the appointment, enabling the physician to engage with the patient entirely. We have performed a systematic literature review on intelligent solutions for automatic speech recognition (ASR) with automatic documentation during a medical interview. The scope included only original research on systems that could detect speech and transcribe it in a natural and structured fashion simultaneously with the doctor-patient interaction, excluding speech-to-text-only technologies. The search resulted in a total of 1995 titles, with eight articles remaining after filtering for the inclusion and exclusion criteria. The intelligent models mainly consisted of an ASR system with natural language processing capability, a medical lexicon, and structured text output. None of the articles had a commercially available product at the time of the publication and reported limited real-life experience. So far, none of the applications has been prospectively validated and tested in large-scale clinical studies. Nonetheless, these first reports suggest that automatic speech recognition may be a valuable tool in the future to facilitate medical registration in a faster and more reliable manner. Improving transparency, accuracy, and empathy could drastically change how patients and doctors experience a medical visit. Unfortunately, clinical data on the usability and benefits of such applications is almost non-existent. We believe that future work in this area is necessary and needed.

BACKGROUND: Artificial intelligence (AI) technology has clustered patients based on clinical features into sub-clusters to stratify high-risk and low-risk groups to predict outcomes in lung cancer after radiotherapy and has gained much more attention in recent years. Given that the conclusions vary considerably, this meta-analysis was conducted to investigate the combined predictive effect of AI models on lung cancer. METHODS: This study was performed according to PRISMA guidelines. PubMed, ISI Web of Science, and Embase databases were searched for relevant literature. Outcomes, including overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), and local control (LC), were predicted using AI models in patients with lung cancer after radiotherapy, and were used to calculate the pooled effect. Quality, heterogeneity, and publication bias of the included studies were also evaluated. RESULTS: Eighteen articles with 4719 patients were eligible for this meta-analysis. The combined hazard ratios (HRs) of the included studies for OS, LC, PFS, and DFS of lung cancer patients were 2.55 (95 % confidence interval (CI) = 1.73-3.76), 2.45 (95 % CI = 0.78-7.64), 3.84 (95 % CI = 2.20-6.68), and 2.66 (95 % CI = 0.96-7.34), respectively. The combined area under the receiver operating characteristics curve (AUC) of the included articles on OS and LC in patients with lung cancer was 0.75 (95 % CI = 0.67-0.84), and 0.80 (95%CI = 0.0.68-0.95), respectively. CONCLUSION: The clinical feasibility of predicting outcomes using AI models after radiotherapy in patients with lung cancer was demonstrated. Large-scale, prospective, multicenter studies should be conducted to more accurately predict the outcomes in patients with lung cancer.

Electronic health records (EHR) are sparse, noisy, and private, with variable vital measurements and stay lengths. Deep learning models are the current state of the art in many machine learning domain; however, the EHR data is not a suitable training input for most of them. In this paper, we introduce RIMD, a novel deep learning model that consists of a decay mechanism, modular recurrent networks, and a custom loss function that learns minor classes. The decay mechanism learns from patterns in sparse data. The modular network allows multiple recurrent networks to pick only relevant input based on the attention score at a given timestamp. Finally, the custom class balance loss function is responsible for learning minor classes based on samples provided in training. This novel model is used to evaluate predictions for early mortality identification, length of stay, and acute respiratory failure on MIMIC-III dataset. Experiment results indicate that the proposed models outperform similar models in F1-Score, AUROC, and PRAUC scores.

BACKGROUND: Cervical cancer is one of the most common cancers in women with an incidence of around 6.5 % of all the cancer in women worldwide. Early detection and adequate treatment according to staging improve the patient's life expectancy. Outcome prediction models might aid treatment decisions, but a systematic review on prediction models for cervical cancer patients is not available. DESIGN: We performed a systematic review for prediction models in cervical cancer following PRISMA guidelines. Key features that were used for model training and validation, the endpoints were extracted from the article and data were analyzed. Selected articles were grouped based on prediction endpoints i.e. Group1: Overall survival, Group2: progression-free survival; Group3: recurrence or distant metastasis; Group4: treatment response; Group5: toxicity or quality of life. We developed a scoring system to evaluate the manuscript. As per our criteria, studies were divided into four groups based on scores obtained in our scoring system, the Most significant study (Score > 60 %); Significant study (60 % > Score > 50 %); Moderately Significant study (50 % > Score > 40 %); least significant study (score < 40 %). A meta-analysis was performed for all the groups separately. RESULTS: The first line of search selected 1358 articles and finally 39 articles were selected as eligible for inclusion in the review. As per our assessment criteria, 16, 13 and 10 studies were found to be the most significant, significant and moderately significant respectively. The intra-group pooled correlation coefficient for Group1, Group2, Group3, Group4, and Group5 were 0.76 [0.72, 0.79], 0.80 [0.73, 0.86], 0.87 [0.83, 0.90], 0.85 [0.77, 0.90], 0.88 [0.85, 0.90] respectively. All the models were found to be good (prediction accuracy [c-index/AUC/R2] >0.7) in endpoint prediction. CONCLUSIONS: Prediction models of cervical cancer toxicity, local or distant recurrence and survival prediction show promising results with reasonable prediction accuracy [c-index/AUC/R2 > 0.7]. These models should also be validated on external data and evaluated in prospective clinical studies.

Clinical Practice Guidelines (CPGs) include recommendations aimed at optimising patient care, informed by a review of the available clinical evidence. To achieve their potential benefits, CPG should be readily available at the point of care. This can be done by translating CPG recommendations into one of the languages for Computer-Interpretable Guidelines (CIGs). This is a difficult task for which the collaboration of clinical and technical staff is crucial. However, in general CIG languages are not accessible to non-technical staff. We propose to support the modelling of CPG processes (and hence the authoring of CIGs) based on a transformation, from a preliminary specification in a more accessible language into an implementation in a CIG language. In this paper, we approach this transformation following the Model-Driven Development (MDD) paradigm, in which models and transformations are key elements for software development. To demonstrate the approach, we implemented and tested an algorithm for the transformation from the BPMN language for business processes to the PROforma CIG language. This implementation uses transformations defined in the ATLAS Transformation Language. Additionally, we conducted a small experiment to assess the hypothesis that a language such as BPMN can facilitate the modelling of CPG processes by clinical and technical staff.

Medical dialog systems have the potential to assist e-medicine in improving access to healthcare services, improving patient treatment quality, and lowering medical expenses. In this research, we describe a knowledge-grounded conversation generation model that demonstrates how large-scale medical information in the form of knowledge graphs can aid in language comprehension and generation in medical dialog systems. Generic responses are often produced by existing generative dialog systems, resulting in monotonous and uninteresting conversations. To solve this problem, we combine various pre-trained language models with a medical knowledge base (UMLS) to generate clinically correct and human-like medical conversations using the recently released MedDialog-EN dataset. The medical-specific knowledge graph contains broadly 3 types of medical-related information, including disease, symptom and laboratory test. We perform reasoning over the retrieved knowledge graph by reading the triples in each graph using MedFact attention, which allows us to use semantic information from the graphs for better response generation. In order to preserve medical information, we employ a policy network, which effectively injects relevant entities associated with each dialog into the response. We also study how transfer learning can significantly improve the performance by utilizing a relatively small corpus, created by extending the recently released CovidDialog dataset, containing the dialogs for diseases that are symptoms of Covid-19. Empirical results on the MedDialog corpus and the extended CovidDialog dataset demonstrate that our proposed model significantly outperforms the state-of-the-art methods in terms of both automatic evaluation and human judgment.

While deep learning has displayed excellent performance in a broad spectrum of application areas, neural networks still struggle to recognize what they have not seen, i.e., out-of-distribution (OOD) inputs. In the medical field, building robust models that are able to detect OOD images is highly critical, as these rare images could show diseases or anomalies that should be detected. In this study, we use wireless capsule endoscopy (WCE) images to present a novel patch-based self-supervised approach comprising three stages. First, we train a triplet network to learn vector representations of WCE image patches. Second, we cluster the patch embeddings to group patches in terms of visual similarity. Third, we use the cluster assignments as pseudolabels to train a patch classifier and use the Out-of-Distribution Detector for Neural Networks (ODIN) for OOD detection. The system has been tested on the Kvasir-capsule, a publicly released WCE dataset. Empirical results show an OOD detection improvement compared to baseline methods. Our method can detect unseen pathologies and anomalies such as lymphangiectasia, foreign bodies and blood with AUROC>0.6. This work presents an effective solution for OOD detection models without needing labeled images.

Deep learning has become a thriving force in the computer aided diagnosis of liver cancer, as it solves extremely complicated challenges with high accuracy over time and facilitates medical experts in their diagnostic and treatment procedures. This paper presents a comprehensive systematic review on deep learning techniques applied for various applications pertaining to liver images, challenges faced by the clinicians in liver tumour diagnosis and how deep learning bridges the gap between clinical practice and technological solutions with an in-depth summary of 113 articles. Since, deep learning is an emerging revolutionary technology, recent state-of-the-art research implemented on liver images are reviewed with more focus on classification, segmentation and clinical applications in the management of liver diseases. Additionally, similar review articles in literature are reviewed and compared. The review is concluded by presenting the contemporary trends and unaddressed research issues in the field of liver tumour diagnosis, offering directions for future research in this field.

OBJECTIVE: The proper handling of missing values is critical to delivering reliable estimates and decisions, especially in high-stakes fields such as clinical research. In response to the increasing diversity and complexity of data, many researchers have developed deep learning (DL)-based imputation techniques. We conducted a systematic review to evaluate the use of these techniques, with a particular focus on the types of data, intending to assist healthcare researchers from various disciplines in dealing with missing data. MATERIALS AND METHODS: We searched five databases (MEDLINE, Web of Science, Embase, CINAHL, and Scopus) for articles published prior to February 8, 2023 that described the use of DL-based models for imputation. We examined selected articles from four perspectives: data types, model backbones (i.e., main architectures), imputation strategies, and comparisons with non-DL-based methods. Based on data types, we created an evidence map to illustrate the adoption of DL models. RESULTS: Out of 1822 articles, a total of 111 were included, of which tabular static data (29%, 32/111) and temporal data (40%, 44/111) were the most frequently investigated. Our findings revealed a discernible pattern in the choice of model backbones and data types, for example, the dominance of autoencoder and recurrent neural networks for tabular temporal data. The discrepancy in imputation strategy usage among data types was also observed. The "integrated" imputation strategy, which solves the imputation task simultaneously with downstream tasks, was most popular for tabular temporal data (52%, 23/44) and multi-modal data (56%, 5/9). Moreover, DL-based imputation methods yielded a higher level of imputation accuracy than non-DL methods in most studies. CONCLUSION: The DL-based imputation models are a family of techniques, with diverse network structures. Their designation in healthcare is usually tailored to data types with different characteristics. Although DL-based imputation models may not be superior to conventional approaches across all datasets, it is highly possible for them to achieve satisfactory results for a particular data type or dataset. There are, however, still issues with regard to portability, interpretability, and fairness associated with current DL-based imputation models.

Every day, 800 women and 6700 newborns die from complications related to pregnancy or childbirth. A well-trained midwife can prevent most of these maternal and newborn deaths. Data science models together with logs generated by users of online learning applications for midwives can help improve their learning competencies. In this work, we evaluate various forecasting methods to determine the future interest of users for the different types of content available in the Safe Delivery App, a digital training tool for skilled birth attendants, broken down by profession and region. This first attempt at health content demand forecasting for midwifery learning shows that DeepAR can accurately anticipate content demand in operational settings, and could therefore be used to offer users personalized content and to provide an adaptive learning journey.

BACKGROUND AND AIM: Cognitive Computing systems are the intelligent systems that thinks, understands and augments the capabilities of human brain by blending the technologies of Artificial Intelligence, Machine Learning and Natural Language Processing. In recent days, maintenance or enhancement of health by preclusion, prognosis, and analysis of diseases has become a challenging task. The increasing diseases and its causes becomes a big question before humanity. Limited risk analysis, meticulous training process, and automated critical decision-making are some of the issues of cognitive computing. To overcome this issue, cognitive computing in healthcare works like a medical prodigy which anticipates the disease or illness of the human being and helps the doctors with technological facts to take the timely action. The main aim of this survey article is to explore the present and futuristic technological trends of cognitive computing in healthcare. In this work, different cognitive computing applications are reviewed, and the best application is recommended to the clinicians. Based on this recommendation, the clinicians are able to monitor and analyze the physical health of patients. METHODS: This article presents the systematic literature on the different aspects of cognitive computing in healthcare. Nearly seven online databases such as SCOPUS, IEEE Xplore, Google Scholar, DBLP, Web of Science, Springer and PubMed were screened and the published articles related to cognitive computing in healthcare is collected from 2014 to 2021. In total, 75 articles were selected, examined and their pros and cons are analyzed. The analysis is done with respect to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS: The basic findings of this review article and their significance for theory and practice are mindmaps portraying the cognitive computing platforms, cognitive applications in healthcare, and use cases of cognitive computing in healthcare. A detailed discussion section highlighting the present issues, future research directions and recent applications of cognitive computing in healthcare. Accuracy analysis of different cognitive systems conclude that the Medical Sieve achieves 0.95 and Watson For Oncology (WFO) achieves 0.93 and hence proves to be the prominent computing systems for healthcare. CONCLUSIONS: Cognitive computing, an evolving technology in healthcare augments the clinical thought process and enable the doctors to make the right diagnosis and preserve the patient's health in good condition. These systems provides timely care, optimal and cost-effective treatment. This article provides an extensive survey of the importance of cognitive computing in the health sector by highlighting the platforms, techniques, tools, algorithms, applications, and use cases. This survey also explores about the works in the literature on present issues and proposes the future research directions of applying cognitive systems in healthcare.

Diabetic Retinopathy (DR) is the most popular debilitating impairment of diabetes and it progresses symptom-free until a sudden loss of vision occurs. Understanding the progression of DR is a pressing issue in clinical research and practice. In this systematic review of articles on Machine Learning (ML) based risk prediction models for DR progression, ever since the use of Artificial Intelligence (AI) for DR detection, there have been more cross-sectional studies with different algorithms of use of AI, there haven't been many longitudinal studies for the AI based risk prediction models. This paper proposes a novel review to fill in the gaps identified in current reviews and facilitate other researchers with current research solutions for developing AI-based risk prediction models for DR progression and closely related problems; synthesize the current results from these studies and identify research challenges, limitations and gaps to inform the selection of machine learning techniques and predictors to build novel prediction models. Additionally, this paper suggested six (6) deep AI-related technical and critical discussion of the adopted strategies and approaches. The Systematic Literature Review (SLR) methodology was employed to gather relevant studies. We searched IEEE Xplore, PubMed, Springer Link, Google Scholar, and Science Direct electronic databases for papers published from January 2017 to 30th April 2023. Thirteen (13) studies were chosen on the basis of their relevance to the review questions and satisfying the selection criteria. However, findings from the literature review exposed some critical research gaps that need to be addressed in future research to improve on the performance of risk prediction models for DR progression.

BACKGROUND: Hospital-acquired pressure injuries (HAPIs) constitute a significant challenge harming thousands of people worldwide yearly. While various tools and methods are used to identify pressure injuries, artificial intelligence (AI) and decision support systems (DSS) can help to reduce HAPIs risks by proactively identifying patients at risk and preventing them before harming patients. OBJECTIVE: This paper comprehensively reviews AI and DSS applications for HAPIs prediction using Electronic Health Records (EHR), including a systematic literature review and bibliometric analysis. METHODS: A systematic literature review was conducted through PRISMA and bibliometric analysis. In February 2023, the search was performed using four electronic databases: SCOPIS, PubMed, EBSCO, and PMCID. Articles on using AI and DSS in the management of PIs were included. RESULTS: The search approach yielded 319 articles, 39 of which have been included and classified into 27 AI-related and 12 DSS-related categories. The years of publication varied from 2006 to 2023, with 40% of the studies taking place in the US. Most studies focused on using AI algorithms or DSS for HAPIs prediction in inpatient units using various types of data such as electronic health records, PI assessment scales, and expert knowledge-based and environmental data to identify the risk factors associated with HAPIs development. CONCLUSIONS: There is insufficient evidence in the existing literature concerning the real impact of AI or DSS on making decisions for HAPIs treatment or prevention. Most studies reviewed are solely hypothetical and retrospective prediction models, with no actual application in healthcare settings. The accuracy rates, prediction results, and intervention procedures suggested based on the prediction, on the other hand, should inspire researchers to combine both approaches with larger-scale data to bring a new venue for HAPIs prevention and to investigate and adopt the suggested solutions to the existing gaps in AI and DSS prediction methods.

The SARS-CoV-2 pandemic highlighted the need for software tools that could facilitate patient triage regarding potential disease severity or even death. In this article, an ensemble of Machine Learning (ML) algorithms is evaluated in terms of predicting the severity of their condition using plasma proteomics and clinical data as input. An overview of AI-based technical developments to support COVID-19 patient management is presented outlining the landscape of relevant technical developments. Based on this review, the use of an ensemble of ML algorithms that analyze clinical and biological data (i.e., plasma proteomics) of COVID-19 patients is designed and deployed to evaluate the potential use of AI for early COVID-19 patient triage. The proposed pipeline is evaluated using three publicly available datasets for training and testing. Three ML "tasks" are defined, and several algorithms are tested through a hyperparameter tuning method to identify the highest-performance models. As overfitting is one of the typical pitfalls for such approaches (mainly due to the size of the training/validation datasets), a variety of evaluation metrics are used to mitigate this risk. In the evaluation procedure, recall scores ranged from 0.6 to 0.74 and F1-score from 0.62 to 0.75. The best performance is observed via Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM) algorithms. Additionally, input data (proteomics and clinical data) were ranked based on corresponding Shapley additive explanation (SHAP) values and evaluated for their prognosticated capacity and immuno-biological credence. This "interpretable" approach revealed that our ML models could discern critical COVID-19 cases predominantly based on patient's age and plasma proteins on B cell dysfunction, hyper-activation of inflammatory pathways like Toll-like receptors, and hypo-activation of developmental and immune pathways like SCF/c-Kit signaling. Finally, the herein computational workflow is corroborated in an independent dataset and MLP superiority along with the implication of the abovementioned predictive biological pathways are corroborated. Regarding limitations of the presented ML pipeline, the datasets used in this study contain less than 1000 observations and a significant number of input features hence constituting a high-dimensional low-sample (HDLS) dataset which could be sensitive to overfitting. An advantage of the proposed pipeline is that it combines biological data (plasma proteomics) with clinical-phenotypic data. Thus, in principle, the presented approach could enable patient triage in a timely fashion if used on already trained models. However, larger datasets and further systematic validation are needed to confirm the potential clinical value of this approach. The code is available on Github: https://github.com/inab-certh/Predicting-COVID-19-severity-through-interpretable-AI-analysis-of-plasma-proteomics.

This paper presents ArrhyMon, a self-attention-based LSTM-FCN model for arrhythmia classification from ECG signal inputs. ArrhyMon targets to detect and classify six different types of arrhythmia apart from normal ECG patterns. To the best of our knowledge, ArrhyMon is the first end-to-end classification model that successfully targets the classification of six detailed arrhythmia types and compared to previous work does not require additional preprocessing and/or feature extraction operations separate from the classification model. ArrhyMon's deep learning model is designed to capture and exploit both global and local features embedded in ECG sequences by integrating fully convolutional network (FCN) layers and a self-attention-based long and short-term memory (LSTM) architecture. Moreover, to enhance its practicality, ArrhyMon incorporates a deep ensemble-based uncertainty model that generates a confidence-level measure for each classification result. We evaluate ArrhyMon's effectiveness using three publicly available arrhythmia datasets (i.e., MIT-BIH, Physionet Cardiology Challenge 2017 and 2020/2021) to show that ArrhyMon achieves state-of-the-art classification performance (average accuracy 99.63%), and that confidence measures show close correlation with subjective diagnosis made from practitioners.