Informatics in Medicine Unlocked最新文献

{"title":"Fully automatic fossa ovalis segmentation from computed tomography images using deep neural network with atlas-based localization","authors":"Gakuto Aoyama ,&nbsp;Toru Tanaka ,&nbsp;Yukiteru Masuda ,&nbsp;Naoki Matsuki ,&nbsp;Ryo Ishikawa ,&nbsp;Masahiko Asami ,&nbsp;Kiyohide Satoh ,&nbsp;Takuya Sakaguchi","doi":"10.1016/j.imu.2025.101613","DOIUrl":"10.1016/j.imu.2025.101613","url":null,"abstract":"<div><h3>Background and objective</h3><div>Information on the location of the fossa ovalis (FO) is necessary for planning interventional procedures that require an inter-atrial septal (IAS) puncture. At present, this information is obtained manually from pre-procedural medical images, which is time consuming with limited reproducibility. In this paper, we propose a method to automatically segment the FO region from computed tomography (CT) images.</div></div><div><h3>Methods</h3><div>Our proposed method roughly crops CT images based on atlas information of the FO and heart chambers, and inputs the cropped CT images to a U-Net-based deep neural network (DNN) to segment the FO region. This method was evaluated by five-fold cross validation using 215 CT images with manually annotated FO regions, and its segmentation accuracy was compared to two previously reported methods based on thinness of the IAS wall and on simple DNN.</div></div><div><h3>Results</h3><div>The segmentation process was successful in all cases for the IAS-based method, but failed in 4 cases for the proposed method and in 30 cases for the DNN method due to irregular heart structure. For the segmentation accuracies of our proposed method, the IAS wall thinness-based method and DNN based-method, mean chamfer distances were 2.16 ± 1.43, 3.30 ± 1.37 and 2.66 ± 1.32 respectively, with the difference being statistically significant.</div></div><div><h3>Conclusions</h3><div>These results suggest that our proposed method can automatically segment the FO region more accurately with fewer failures.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101613"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerated and accurate cervical cancer diagnosis using a novel stacking ensemble method with explainable AI","authors":"Md Ismail Hossain Siddiqui ,&nbsp;Shakil Khan ,&nbsp;Zishad Hossain Limon ,&nbsp;Hamdadur Rahman ,&nbsp;Mahbub Alam Khan ,&nbsp;Abdullah Al Sakib ,&nbsp;S M Masfequier Rahman Swapno ,&nbsp;Rezaul Haque ,&nbsp;Ahmed Wasif Reza ,&nbsp;Abhishek Appaji","doi":"10.1016/j.imu.2025.101657","DOIUrl":"10.1016/j.imu.2025.101657","url":null,"abstract":"<div><div>Cervical cancer is a preventable yet life-threatening disease that claims hundreds of thousands of lives each year, particularly in low-resource settings where timely screening is scarce. Current Deep Learning (DL) approaches for automated cervical cytology classification encounter challenges such as class imbalance, computational inefficiency, and inadequate generalizability. This study proposes a novel CerviXEnsemble model that integrates multiple pre-trained DL architectures (Inception-ResNetV2, EfficientNet-B6, ResNet152, Inception-ResNetV2, EfficientNet-B6, DenseNet201, and NASNetMobile) as base learners, along with a dense-layer meta-learner that refines and consolidates predictions for improved robustness. Unlike traditional single-CNN models, our stacking ensemble approach utilizes diverse feature representations to enhance classification stability and generalization across multiple cytology datasets. To validate the model, we experimented with the Herlev and SIPaKMeD benchmark datasets in this study. Techniques like contrast enhancement and data augmentation were employed to optimize feature extraction. The model achieved state-of-the-art performance, attaining an accuracy of 99.38 % and an F1-score of 98.49 % on the Herlev dataset and an accuracy of 98.71 % and an F1-score of 97.53 % on SIPaKMeD. These performances are superior to previous studies in controlling class imbalance and providing stable predictions over different samples. Additionally, Explainable AI (XAI) techniques were incorporated to ensure transparent and interpretable predictions, aiding clinicians in their decision-making processes. An interpratable web application was developed for real-time Pap smear analysis to reduce the diagnostic workload for pathologists by identifying high-risk samples. This solution shows great promise for use in various healthcare settings, maintaining high diagnostic accuracy while requiring minimal computational resources, making it suitable for both urban hospitals and rural clinics.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"56 ","pages":"Article 101657"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DeepPatchNet: A deep learning model for enhanced screening and diagnosis of oral cancer","authors":"Idriss Tafala ,&nbsp;Fatima-Ezzahraa Ben-Bouazza ,&nbsp;Aymane Edder ,&nbsp;Oumaima Manchadi ,&nbsp;Bassma Jioudi","doi":"10.1016/j.imu.2025.101658","DOIUrl":"10.1016/j.imu.2025.101658","url":null,"abstract":"<div><div>Oral cancer remains a serious global health challenge, significantly affecting patient survival and quality of life. While convolutional neural networks (CNNs) have historically dominated image classification tasks, recent advances suggest that transformer-based models may offer superior performance—albeit with high data and computational demands. In this study, we present <strong>DeepPatchNet</strong>, a novel deep learning architecture that integrates DeepLabV3+ and ConvMixer to address these limitations. Designed for histopathological image classification, DeepPatchNet provides a lightweight, interpretable, and high-performing solution. We evaluated the model on the NDB-UFES dataset (3763 images) and an independent H&amp;E-stained OSCC dataset (1020 images), benchmarking its performance against state-of-the-art models including Vision Transformers (ViTs)<span><span>[1]</span></span>, <span><span>[2]</span></span>, InceptionResNetV2, VGG19, and ConvNeXt. DeepPatchNet achieved superior performance with 86.71% accuracy, 86.80% precision, 86.71% recall, and an F1 score of 86.75%, outperforming all comparison models. Furthermore, the integration of Gradient-weighted Class Activation Mapping (Grad-CAM) enhances interpretability by visually highlighting diagnostically relevant features, addressing a key barrier to clinical adoption. While our results are promising, further validation in real-world clinical settings is needed. DeepPatchNet shows strong potential as a reliable decision support tool for early oral cancer detection and diagnosis.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101658"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An explainable machine learning model for COVID-19 severity prognosis at hospital admission","authors":"Antonios T. Tsanakas ,&nbsp;Yvonne M. Mueller ,&nbsp;Harmen JG. van de Werken ,&nbsp;Ricardo Pujol Borrell ,&nbsp;Christos A. Ouzounis ,&nbsp;Peter D. Katsikis","doi":"10.1016/j.imu.2024.101602","DOIUrl":"10.1016/j.imu.2024.101602","url":null,"abstract":"<div><div>The coronavirus disease −2019 (COVID-19) pandemic has resulted in serious healthcare challenges. Due to its high transmissibility and hospitalization rates, COVID-19 has led to many deaths and imposed a considerable burden on healthcare systems worldwide. The development of prognostic approaches supporting clinical decisions for hospitalized patients can contribute to better management of the pandemic. We deploy several Artificial Intelligence (AI) techniques to derive COVID-19 severity classification prognosis models for unvaccinated patients hospitalized with mild symptoms using immunological biomarkers. The risk levels are precisely defined, targeting patients with uncertain prognostic trajectories. Forty molecular biomarkers were evaluated for their ability to predict the course of the illness. Seven biomarkers, including IL-6, IL-10, CCL2, LDH, IFNα, ferritin, and anti-SARS-CoV-2 N protein IgA antibody, emerge as the most significant early predictors for the prospective development of severe disease. After applying feature selection, we settled for two complete sets of five and three biomarkers to generate appropriate classification models. A Random Forest model with five biomarkers appears to be the most effective, with an accuracy of 0.92 for the external set. Yet, a Decision Tree model with just three biomarkers, and an accuracy of 0.84 for the external set, provides marginally lower yet robust performance and an explainable structure that broadly reflects our current understanding of disease severity. These findings suggest that the severity is influenced by a few key pathological processes. Therefore, a three-biomarker model that utilizes IL-6, IFNα, and anti-SARS-CoV-2 N protein IgA antibody levels may enhance clinical decision-making and patient triage at hospitalization, contributing to the successful management of the disease.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101602"},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting ChatGPT in published documents: Chatbot catchphrases and buzzwords","authors":"Edward J. Ciaccio","doi":"10.1016/j.imu.2024.101516","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101516","url":null,"abstract":"","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"16 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141055460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG-based functional connectivity analysis of brain abnormalities: A review study","authors":"Nastaran Khaleghi ,&nbsp;Shaghayegh Hashemi ,&nbsp;Mohammad Peivandi ,&nbsp;Sevda Zafarmandi Ardabili ,&nbsp;Mohammadreza Behjati ,&nbsp;Sobhan Sheykhivand ,&nbsp;Sebelan Danishvar","doi":"10.1016/j.imu.2024.101476","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101476","url":null,"abstract":"<div><p>Several imaging modalities and many signal recording techniques have been used to study the brain activities. Significant advancements in medical device technologies like electroencephalographs have provided conditions for recording neural information with high temporal resolution. These recordings can be used to calculate the connections between different brain areas. It has been proved that brain abnormalities affect the brain activity in different brain regions and the connectivity patterns between them would change as the result. This paper studies the electroencephalogram (EEG) functional connectivity methods and investigates the impacts of brain abnormalities on the brain functional connectivities. The effects of different brain abnormalities including stroke, depression, emotional disorders, epilepsy, attention deficit hyperactivity disorder (ADHD), autism, and Alzheimer's disease on functional connectivity of the EEG recordings have been explored in this study. The EEG-based metrics and network properties of different brain abnormalities have been discussed to have a comparison of the connectivities affected by each abnormality. Also, the effects of therapy and medical intake on the EEG functional connectivity network of each abnormality have been reviewed.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"47 ","pages":"Article 101476"},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000327/pdfft?md5=f4cca409c15776d628c46f1cedf6de45&pid=1-s2.0-S2352914824000327-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140351321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Usability evaluation of electronic prescribing systems from physician' perspective: A case study from southern Iran","authors":"Mohammad Hosein Hayavi-Haghighi ,&nbsp;Somayeh Davoodi ,&nbsp;Saeed Hossini Teshnizi ,&nbsp;Razieh Jookar","doi":"10.1016/j.imu.2024.101460","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101460","url":null,"abstract":"<div><h3>Introduction</h3><p>The evaluation of e-prescribing systems' usability is crucial as they are integral to the quality of healthcare services. This study evaluates the usability of three e-prescribing systems and examines the impact of individual factors on system usability.</p></div><div><h3>Method</h3><p>The objective of this descriptive study was to assess the usability of e-prescribing systems (EP, Dinad, and Shafa) as perceived by 105 physicians from three clinics at Hormozgan University of Medical Sciences in Bandar Abbas, Iran. The data was collected using the 2020 edition of the Isometric Questionnaire 9241/110, which comprises of seven axes and 66 questions. The participants were asked to rate their opinions on a 5-point Likert scale, with options ranging from completely disagree [1] to completely agree [5].</p></div><div><h3>Results</h3><p>EP, Dinad, and Shafa received average scores of 3.45, 3.32, and 3.24, respectively. Self-descriptiveness and User Error Tolerance axes were rated the highest ratings, with average scores of 3.60 and 3.48. Conversely, conformity and suitability axes received the lowest ratings, with average scores of 3.19 and 3.22, respectively. Upon evaluating the usability axes, the EP significantly improved controllability and user engagement compared to other systems. The usability of Dinad and Shafa in the Gynecology clinic was significantly higher than the two other clinics. Also, older physicians with more work experience rated the Shafa significantly higher than two other systems.</p></div><div><h3>Conclusion</h3><p>The evaluated systems had average usability. although there was no statistically significant difference in the usability of these systems, the evaluation of dimensions revealed unique strengths in each system.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"45 ","pages":"Article 101460"},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000169/pdfft?md5=dcfd36af8f38f00e28885b20de141cda&pid=1-s2.0-S2352914824000169-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139719489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precision diagnostics in cardiac tumours: Integrating echocardiography and pathology with advanced machine learning on limited data","authors":"Seyed-Ali Sadegh-Zadeh ,&nbsp;Naser Khezerlouy-aghdam ,&nbsp;Hanieh Sakha ,&nbsp;Mehrnoush Toufan ,&nbsp;Mahsa Behravan ,&nbsp;Amir Vahedi ,&nbsp;Mehran Rahimi ,&nbsp;Haniyeh Hosseini ,&nbsp;Sanaz Khanjani ,&nbsp;Bita Bayat ,&nbsp;Syed Ahsan Ali ,&nbsp;Reza Hajizadeh ,&nbsp;Ali Eshraghi ,&nbsp;Saeed Shiry Ghidary ,&nbsp;Mozafar Saadat","doi":"10.1016/j.imu.2024.101544","DOIUrl":"10.1016/j.imu.2024.101544","url":null,"abstract":"<div><p>This study pioneers the integration of echocardiography and pathology data with advanced machine learning (ML) techniques to significantly enhance the diagnostic accuracy of cardiac tumours, a critical yet challenging aspect of cardiology. Despite advancements in diagnostic methods, cardiac tumours' nuanced complexity and rarity necessitate more precise, non-invasive, and efficient diagnostic solutions. Our research aims to bridge this gap by developing and validating ML models—Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM)—optimized for limited datasets prevalent in specialized medical fields. Utilizing a dataset comprising clinical features from 399 patients at the Heart Hospital, our study meticulously evaluated the performance of these models against traditional diagnostic metrics. The RF model emerged superior, achieving a groundbreaking accuracy of 96.25 % and a perfect ROC AUC score of 0.99, significantly outperforming existing diagnostic approaches. Key predictors identified include age, <em>echo malignancy</em>, and <em>echo position</em>, underscoring the value of integrating diverse data types. Clinical validation conducted at the Heart Hospital further confirmed the models' applicability and reliability, with the RF model demonstrating a diagnostic accuracy of 94 % in a real-world setting. These findings advocate for the potential of ML in revolutionizing cardiac tumour diagnostics, offering pathways to more accurate, non-invasive, and patient-centric diagnostic processes. This research not only highlights the capabilities of ML to enhance diagnostic precision in the realm of cardiac tumours but also sets a foundation for future explorations into its broader applicability across various domains of medical diagnostics, emphasizing the need for expanded datasets and external validation.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"49 ","pages":"Article 101544"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S235291482400100X/pdfft?md5=554f36977387f1b7fb815683f0ab49fb&pid=1-s2.0-S235291482400100X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141622598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-optimal and optimal fractional control analysis of measles using real data","authors":"Fredrick Asenso Wireko ,&nbsp;Joshua Kiddy K. Asamoah ,&nbsp;Isaac Kwasi Adu ,&nbsp;Sebastian Ndogum","doi":"10.1016/j.imu.2024.101548","DOIUrl":"10.1016/j.imu.2024.101548","url":null,"abstract":"<div><p>This study employs fractional, non-optimal, and optimal control techniques to analyze measles transmission dynamics using real-world data. Thus, we develop a fractional-order compartmental model capturing measles transmission dynamics. We then formulate an optimal control problem to minimize the disease burden while considering constraints such as vaccination resources and intervention costs. The proposed model’s positivity, boundedness, measles reproduction number, and stability are obtained. The sensitivity analysis using the partial rank correlation coefficient is shown for the fractional orders of 0.99 and 0.90. It is noticed that the rate of recruitment into the susceptible population (<span><math><mi>π</mi></math></span>), the rate at which individuals in the latent class become asymptomatic (<span><math><msub><mrow><mi>α</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>), and the transmission rate (<span><math><mi>β</mi></math></span>) contribute positively to the spread of the disease, while the rate at which individuals in the asymptomatic class become symptomatic (<span><math><msub><mrow><mi>α</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>), the vaccination rate for the first measles dose (<span><math><msub><mrow><mi>γ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>), and the rate at which individuals in the latent class recover from measles (<span><math><msub><mrow><mi>δ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>) contribute significantly to the reduction of measles in the community. Utilizing numerical simulations and sensitivity analyses, we identify optimal control strategies that balance the trade-offs between intervention efficacy, resource allocation, and societal costs. Our findings provide insights into the effectiveness of fractional optimal control strategies in mitigating measles outbreaks and contribute to developing more robust and adaptive disease control policies in real-world scenarios.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"49 ","pages":"Article 101548"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824001047/pdfft?md5=81196b0fa29ac2f2b94006a2271418dd&pid=1-s2.0-S2352914824001047-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141622599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of the effects of MERCK, MODERNA, PFIZER/BioNTech, and JANSSEN COVID-19 vaccines on vaccinated people: A metadata analysis","authors":"Nadia Al-Rousan ,&nbsp;Hazem Al-Najjar","doi":"10.1016/j.imu.2024.101564","DOIUrl":"10.1016/j.imu.2024.101564","url":null,"abstract":"<div><h3>Purpose</h3><p>This research investigates the impact of four specific vaccines on the health of people who have been vaccinated. The vaccines under scrutiny are MERCK, MODERNA, PFIZER BioNTech, and JANSSEN.</p></div><div><h3>Methods</h3><p>The analysis considers a range of variables, including symptoms, mortality status, gender, age, number of vaccine doses, hospitalization status, and the number of days following vaccination. The methodology involves cross-tabulation analysis to establish connections between vaccinated individuals and the variables under examination. The dataset was compiled from the Centers for Disease Control and Prevention, encompassing roughly 65,000 cases and documenting over 40 distinct symptoms.</p></div><div><h3>Results</h3><p>The overall mortality rate among the vaccinated population is noteworthy. Notably, 40 different mild to severe symptoms were reported among vaccinated individuals. The research highlights the 10 most common symptoms experienced after vaccination. Females under 60 years of age constitute the majority of the dataset.</p></div><div><h3>Conclusions</h3><p>The vaccination-related mortality rate stands at approximately 3 % of those who received the vaccine, with the majority of cases occurring among individuals under the age of 60, who were not hospitalized and had received their initial vaccine dose.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"49 ","pages":"Article 101564"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824001205/pdfft?md5=eaac077f638df2c82200db4de8aef546&pid=1-s2.0-S2352914824001205-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}