Healthcare analytics (New York, N.Y.)最新文献_第10页

An electrocardiogram signal classification using a hybrid machine learning and deep learning approach 利用机器学习和深度学习混合方法进行心电图信号分类

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 Epub Date: 2024-10-09 DOI: 10.1016/j.health.2024.100366

Faramarz Zabihi , Fatemeh Safara , Behrouz Ahadzadeh

{"title":"An electrocardiogram signal classification using a hybrid machine learning and deep learning approach","authors":"Faramarz Zabihi , Fatemeh Safara , Behrouz Ahadzadeh","doi":"10.1016/j.health.2024.100366","DOIUrl":"10.1016/j.health.2024.100366","url":null,"abstract":"<div><div>An electrocardiogram (ECG) is a diagnostic tool that captures the electrical activity of the heart. Any irregularity in the heart's electrical system is referred to as an arrhythmia, which can be identified through the analysis of ECG signals. Timely diagnosis of cardiac arrhythmias is crucial in order to mitigate their potentially harmful consequences. However, manual analysis of ECG signals is time-consuming and prone to inaccuracies. Therefore, researchers have developed medical decision support systems that utilize machine learning techniques to automate the analysis of ECG signals. In this study, we propose a novel method for classifying ECG signals into four distinct types of heartbeats: normal, supraventricular, ventricular, and fusion. Our method consists of two subsystems that integrate both machine learning and deep learning approaches. The first subsystem uses a residual network block to extract features from the input ECG signal, followed by an LSTM network for learning and classification of these features. The second subsystem uses several feature extraction methods and a random forest to classify the ECG signals. Furthermore, it employs a Synthetic Minority Over-Sampling Technique to improve dataset balance and overall performance. The ultimate result is achieved by merging the results of both subsystems together. An assessment of our approach was carried out on the MIT-BIH dataset, which acts as a recognized ECG signal classification benchmark. Our technique attained an impressive accuracy rate of 99.26%, ranking it as one of the most superior methods in the current literature. Our findings demonstrate the effectiveness and efficiency of our approach in accurately classifying ECG signals for arrhythmia detection.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100366"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420609","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}

引用次数: 0

A comprehensive review of predictive analytics models for mental illness using machine learning algorithms 全面回顾使用机器学习算法的精神疾病预测分析模型

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 Epub Date: 2024-06-17 DOI: 10.1016/j.health.2024.100350

Md. Monirul Islam , Shahriar Hassan , Sharmin Akter , Ferdaus Anam Jibon , Md. Sahidullah

{"title":"A comprehensive review of predictive analytics models for mental illness using machine learning algorithms","authors":"Md. Monirul Islam , Shahriar Hassan , Sharmin Akter , Ferdaus Anam Jibon , Md. Sahidullah","doi":"10.1016/j.health.2024.100350","DOIUrl":"https://doi.org/10.1016/j.health.2024.100350","url":null,"abstract":"<div><p>Our emotional, psychological, and social well-being are all parts of our mental health, influencing our thoughts, emotions, and behaviors. Mental health also influences how we respond to stress, interact with others, and make good or bad decisions. There has been growing interest in the use of machine learning for the early detection of mental illness. This study reviews the machine learning models, algorithms, and applications for the early detection of mental disease, particularly emphasizing the data modalities. We further propose a comprehensive methodology for assessing mental health that synergistically combines social media monitoring, data analytics from wearable devices, verbal polls, and individualized support. We provide an overview of the field’s current state, highlight the potential benefits and challenges of using machine learning in mental health care, and a new taxonomy of mental disorders issues based on five domains of data types. We review existing research on using machine learning to detect and treat mental illness and discuss the implications for future research. Finally, the value of this work lies in its potential to provide a fast and accurate method for predicting the mental health status of a person, which may assist in the diagnosis and treatment of mental illness.</p></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100350"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772442524000522/pdfft?md5=bc1cb3cc91aa0634c506d50a66bd2d34&pid=1-s2.0-S2772442524000522-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435122","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}

引用次数: 0

A metafrontier and Malmquist productivity index approach for analyzing biased technological and efficiency change in Taiwanese traditional Chinese medicine 台湾中医药偏激技术与效率变迁之超前沿与Malmquist生产力指数分析

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 Epub Date: 2024-11-28 DOI: 10.1016/j.health.2024.100372

Kuan-Chen Chen , Hsiang-An Yu , Ming-Miin Yu

{"title":"A metafrontier and Malmquist productivity index approach for analyzing biased technological and efficiency change in Taiwanese traditional Chinese medicine","authors":"Kuan-Chen Chen , Hsiang-An Yu , Ming-Miin Yu","doi":"10.1016/j.health.2024.100372","DOIUrl":"10.1016/j.health.2024.100372","url":null,"abstract":"<div><div>This study assesses changes in resource productivity in traditional Chinese medicine (TCM) system across Taiwanese counties and cities from 2016 to 2019, stratifying the analysis by population densities. Employing a data envelopment analysis (DEA) metafrontier Malmquist productivity index model, this research relaxes Hicks' neutrality assumption of technical change, allowing for the measurement of biased technological change and technical gap ratio changes. The empirical findings reveal a decline in TCM system productivity, primarily attributed to reduced technological advancements. Notably, higher productivity changes were observed in counties and cities with lower population densities, contrasting with those having higher population densities, where productivity changes were limited. The results suggest that areas with lower population densities hold significant potential for technological enhancement, as evidenced by intergroup technology updates and technological leadership indices. Furthermore, the estimates of productivity change and technological bias underscore the inadequacy of assuming Hicks’ neutral technological change for analyzing TCM system productivity in Taiwan. These findings highlight the need for improved TCM system technology and innovation within the healthcare system to address the urban-rural gap effectively.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100372"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129684","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}

引用次数: 0

A deep neural network model with spectral correlation function for electrocardiogram classification and diagnosis of atrial fibrillation 用于心电图分类和心房颤动诊断的带频谱相关函数的深度神经网络模型

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 Epub Date: 2024-11-23 DOI: 10.1016/j.health.2024.100370

Sara Mihandoost

{"title":"A deep neural network model with spectral correlation function for electrocardiogram classification and diagnosis of atrial fibrillation","authors":"Sara Mihandoost","doi":"10.1016/j.health.2024.100370","DOIUrl":"10.1016/j.health.2024.100370","url":null,"abstract":"<div><div>Atrial Fibrillation (AF) is a common type of irregular heartbeat, and early detection can significantly improve treatment outcomes and prognoses. Single-lead Electrocardiogram (ECG) devices are under extensive scrutiny for monitoring patients' heart health worldwide. Standardized ECG signal monitoring has demonstrated a significant reduction in mortality rates associated with severe cardiovascular diseases. However, the automatic detection method for AF requires significant improvement. This study presents a novel approach that utilizes the cyclostationary analysis of ECG signals, uncovering a spectral hidden periodicity between the QRS-T (the main wave components representing electrical activity in the heart) complexes of the ECG signal through the Spectral Correlation Function (SCF). To validate the proposed method's performance, the single ECG's SCF coefficients are applied to the Convolutional Recurrent Neural Network (CRNN), which consists of convolutional and long short-term memory (LSTM) layers, on the 2017 PhysioNet challenge dataset. The obtained results demonstrate that the proposed approach efficiently represents ECG signals through SCF coefficients, leading to the accurate detection of AF with an average accuracy of 92.76% and an average F1-score of 89.1%.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100370"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699625","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}

引用次数: 0

An ensemble convolutional neural network model for brain stroke prediction using brain computed tomography images 利用脑计算机断层扫描图像预测脑中风的集合卷积神经网络模型

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 Epub Date: 2024-10-29 DOI: 10.1016/j.health.2024.100368

Most. Jannatul Ferdous, Rifat Shahriyar

{"title":"An ensemble convolutional neural network model for brain stroke prediction using brain computed tomography images","authors":"Most. Jannatul Ferdous, Rifat Shahriyar","doi":"10.1016/j.health.2024.100368","DOIUrl":"10.1016/j.health.2024.100368","url":null,"abstract":"<div><div>A stroke is a potentially fatal brain attack that causes an interruption in the blood supply to the brain. As a result, brain cells start to die due to a lack of oxygen and nutrients. After a stroke, every minute is critical. A million or more brain cells perish every minute during a stroke. The prompt identification of a stroke can prevent lasting brain damage or even save the patient’s life. Doctors advise computed tomography (CT) images of the brain for earlier stroke detection. If doctors delay CT diagnosis or may make erroneous diagnoses, this can be life-threatening. For that reason, an automatic diagnosis of stroke from a brain CT scan image will be beneficial for stroke patients. This study moderates three pre-trained convolutional neural network (CNN) models named Inceptionv3, MobileNetv2, and Xception by updating the top layer of those models using the transfer-learning technique based on CT images of the brain. A new ensemble convolutional neural network (ENSNET) model is proposed for automatic brain stroke prediction from brain CT scan images. ENSNET is the average of two improved CNN models named InceptionV3 and Xception. We have relied on the following metrics: accuracy, precision, recall, f1-score, confusion matrix, accuracy versus epoch, loss versus epoch, and the receiver operating characteristic (ROC) curve to assess performance matrices. The accuracy of the moderated Inceptionv3 is 97.48%, the moderated MobileNetv2 is 83.29%, and the moderated Xception is 96.11%. Nonetheless, the suggested ensemble model ENSNET performs better than the other models when it comes to the diagnosis of stroke from brain CT scans, providing 98.86% accuracy, 97.71% precision, 98.46% recall, 98.08% f1-score, and 98.74% area under the ROC curve(AUC). Therefore, the proposed model ENSNET can detect strokes from computed tomography images of the brain more successfully than other models.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100368"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578078","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}

引用次数: 0

An investigation of Susceptible–Exposed–Infectious–Recovered (SEIR) tuberculosis model dynamics with pseudo-recovery and psychological effect 带假康复和心理效应的易感-暴露-感染-康复（SEIR）结核病模型动力学研究

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 Epub Date: 2024-09-17 DOI: 10.1016/j.health.2024.100361

Yudi Ari Adi , Suparman

{"title":"An investigation of Susceptible–Exposed–Infectious–Recovered (SEIR) tuberculosis model dynamics with pseudo-recovery and psychological effect","authors":"Yudi Ari Adi , Suparman","doi":"10.1016/j.health.2024.100361","DOIUrl":"10.1016/j.health.2024.100361","url":null,"abstract":"<div><div>Tuberculosis is one of the most pressing issues of the modern era, posing a severe health risk to humans in recent decades. This study proposes a Susceptible–Exposed–Infectious–Recovered (SEIR) tuberculosis epidemic transmission model with psychological effects and pseudo-recovery. We consider a compartmental mathematical model in which the entire population is divided into four compartments based on their natural features. The model is validated, and parameter values are estimated using Indonesian data from 2002 to 2022. To investigate their epidemiological significance, we proved the positivity and boundedness of solutions, as well as the local and global stability of equilibria. Sensitivity analysis is used to find the most influential parameters with the most significant influence on the basic reproduction number, <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>. The bifurcation procedure tools of the center manifold theory are used to conduct a bifurcation study. Mathematical conditions ensure the inferred event of forward bifurcation. We performed numerical simulations that support our theoretical findings.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100361"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323345","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}

引用次数: 0

Artificial intelligence and diagnostic healthcare using computer vision and medical imaging 使用计算机视觉和医学成像的人工智能和诊断医疗保健

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 Epub Date: 2024-06-25 DOI: 10.1016/j.health.2024.100352

Gaurav Dhiman, Wattana Viriyasitavat, Atulya K. Nagar, Oscar Castillo

引用次数: 0

A longitudinal mixed effects model for assessing mortality trends during vaccine rollout 用于评估疫苗推广期间死亡率趋势的纵向混合效应模型

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 Epub Date: 2024-06-13 DOI: 10.1016/j.health.2024.100347

Qin Shao , Mounika Polavarapu , Lafleur Small , Shipra Singh , Quoc Nguyen , Kevin Shao

{"title":"A longitudinal mixed effects model for assessing mortality trends during vaccine rollout","authors":"Qin Shao , Mounika Polavarapu , Lafleur Small , Shipra Singh , Quoc Nguyen , Kevin Shao","doi":"10.1016/j.health.2024.100347","DOIUrl":"10.1016/j.health.2024.100347","url":null,"abstract":"<div><p>The rapid spread of coronavirus disease 2019 (COVID-19) initially presented unprecedented challenges for clinicians, policymakers, and healthcare systems, as there was limited evidence on the efficacy of various control measures. This study endeavors to provide a detailed and comprehensive overview of the global progression of the COVID-19 mortality in the context of vaccine rollout, utilizing public surveillance data from 145 countries sourced from the World Health Organization and the World Bank. The primary focus is to analyze shifts in the trend of new COVID-19 mortality worldwide before and after the introduction of COVID-19 vaccines. To achieve this, we propose a longitudinal mixed effects model aimed at elucidating the relationship between mortality trend and vaccination rollout, alongside other pertinent covariates. Our modeling approach seeks to accommodate variations in the timing of COVID-19 vaccine rollout among countries, as well as the correlation of observations from within the same country. Our findings highlight the significant impact of new cases, cardiovascular death rate, senior population, stringency index, and reproduction rate on mortality. However, we find that the impact of vaccination is not statistically significant, as evidenced by a relatively large <span><math><mi>p</mi></math></span>-value. Furthermore, the study reveals substantial disparities in mortality rates among countries across four income groups.</p></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100347"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772442524000492/pdfft?md5=ae79d48a8a53e7a4841d3c82370b0bf0&pid=1-s2.0-S2772442524000492-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141401659","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}

引用次数: 0

A hierarchical Bayesian approach for identifying socioeconomic factors influencing self-rated health in Japan 用分层贝叶斯方法确定影响日本自我健康评价的社会经济因素

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 Epub Date: 2024-10-25 DOI: 10.1016/j.health.2024.100367

Makoto Nakakita , Teruo Nakatsuma

{"title":"A hierarchical Bayesian approach for identifying socioeconomic factors influencing self-rated health in Japan","authors":"Makoto Nakakita , Teruo Nakatsuma","doi":"10.1016/j.health.2024.100367","DOIUrl":"10.1016/j.health.2024.100367","url":null,"abstract":"<div><div>This study identifies socioeconomic factors that potentially influence self-rated health (SRH), an important indicator of health status, in the Japanese population. We used a panel data logit model to simultaneously estimate the effects of personal attributes, living environment, and social conditions. To achieve a stable estimation of the panel data logit model, we applied hierarchical Bayesian modeling and the Markov Chain Monte Carlo (MCMC) method to obtain its estimation. Furthermore, we used the ancillary-sufficiency interweaving strategy (ASIS) algorithm to improve the efficiency of the MCMC method for the panel data logit model. The results indicate that SRH within the Japanese population is affected by demographic and socioeconomic factors (e.g., age, marital status, educational background, and employment status) and daily habits such as frequency of drinking alcohol. We also obtained results that differed from previous studies in the research literature. Differences in the national character among countries may be reflected in these results. Since SRH is a subjective measure of health status and often differs from actual health status, it is crucial to remove the influences of the national character on SRH in evaluating the actual health status of individuals within a population. The study findings provide important insights into addressing these factors to understand SRH in the Japanese context better.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100367"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142537361","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}

引用次数: 0

A comparative analysis of machine learning algorithms with tree-structured parzen estimator for liver disease prediction 机器学习算法与树状结构帕尔森估计器在肝病预测方面的比较分析

Healthcare analytics (New York, N.Y.) Pub Date : 2024-12-01 Epub Date: 2024-08-16 DOI: 10.1016/j.health.2024.100358

Rakibul Islam, Azrin Sultana, MD. Nuruzzaman Tuhin

{"title":"A comparative analysis of machine learning algorithms with tree-structured parzen estimator for liver disease prediction","authors":"Rakibul Islam, Azrin Sultana, MD. Nuruzzaman Tuhin","doi":"10.1016/j.health.2024.100358","DOIUrl":"10.1016/j.health.2024.100358","url":null,"abstract":"<div><p>The liver is one of the most essential organs in the body, which helps with metabolism and keeping the body healthy. Successful treatments and better patient outcomes depend on early and correct Liver Disease (LD) diagnosis and identification. This study proposes a system for predicting the LD by combining the techniques of Machine Learning (ML) algorithms that include the Decision Tree, Random Forest, Extra Tree Classifier (ETC), LightGBM, and Adaboost, with the Tree-Structured Parzen Estimator (TPE) method for hyperparameter tuning. No previous literature research has utilized ML algorithms with TPE to predict LD. For this research, the Indian Liver Patients’ Dataset with 583 instances and 11 attributes was used. In the pre-processing of the data, techniques such as upsampling have been utilized to address the class imbalance problem. Normalization has been employed to scale the dataset, and feature selection has been applied to choose important features. The proposed model has been analyzed and compared using a 10-fold cross-validation process, with various evaluation metrics including accuracy, precision, recall, and F1-score. The model proposed in this study achieved the best level of accuracy while employing the ETC with the TPE approach, with a recorded accuracy of 95.8%.</p></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"6 ","pages":"Article 100358"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772442524000601/pdfft?md5=3aa72f3755c5377eba838fab77bd6aa3&pid=1-s2.0-S2772442524000601-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006485","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}

引用次数: 0