Healthcare analytics (New York, N.Y.)最新文献

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An integrated stacked convolutional neural network and the levy flight-based grasshopper optimization algorithm for predicting heart disease

Healthcare analytics (New York, N.Y.)

Pub Date : 2024-12-07 DOI: 10.1016/j.health.2024.100374

Syed Muhammad Salman Bukhari , Muhammad Hamza Zafar , Syed Kumayl Raza Moosavi , Majad Mansoor , Filippo Sanfilippo

Cardiovascular disease is the leading cause of death worldwide, including critical conditions such as blood vessel blockage, heart failure, and stroke. Accurate and early prediction of heart disease remains a significant challenge due to the complexity of symptoms and the variability of contributing factors. This study proposes a novel hybrid model integrating a Stacked Convolutional Neural Network (SCNN) with the Levy Flight-based Grasshopper Optimization Algorithm (LFGOA) to address this challenge. The SCNN provides robust feature extraction, while LFGOA enhances the model by optimizing hyperparameters, improving classification accuracy, and reducing overfitting. The proposed approach is evaluated using four publicly available heart disease datasets, each representing diverse clinical and demographic features. Compared to traditional classifiers, including Regression Trees, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, and standard Neural Networks, the SCNN-LFGOA consistently outperforms these methods. The results highlight that the SCNN-LFGOA achieves an average accuracy of 99%, with significant improvements in specificity, sensitivity, and F1-Score, showcasing its adaptability and robustness across datasets. This study highlights the SCNN-LFGOA's potential as a transformative tool for early and accurate heart disease prediction, contributing to improved patient outcomes and more efficient healthcare resource utilization. By combining deep learning with an advanced optimization technique, this research introduces a scalable and effective solution to a critical healthcare problem.

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引用次数: 0

Optimized early fusion of handcrafted and deep learning descriptors for voice pathology detection and classification 优化了语音病理检测和分类的手工和深度学习描述符的早期融合

Healthcare analytics (New York, N.Y.)

Pub Date : 2024-12-01 DOI: 10.1016/j.health.2024.100369

Roohum Jegan, R. Jayagowri

This study presents an automated noninvasive voice disorder detection and classification approach using an optimized fusion of modified glottal source estimation and deep transfer learning neural network descriptors. A new set of modified descriptors based on a glottal source estimator and pre-trained Inception-ResNet-v2 convolutional neural network-based features are proposed for the speech disorder detection and classification task. The modified feature set is obtained using mel-cepstral coefficients, harmonic model, phase discrimination means, distortion deviation descriptors, conventional wavelet, and glottal source estimation features. Early descriptor-level fusion is employed in this study for performance enhancement-however, the fusion results in higher feature vector dimensionality. A nature-inspired slime mould algorithm is utilized to remove redundant and select the best discriminating features. Finally, the classification is performed using the K-nearest neighbor (KNN) classifier. The proposed algorithm was evaluated using extensive experiments with different feature combinations, with and without feature selection, and with two popular datasets: the Arabic Voice Pathology Database (AVPD) and the Saarbrucken Voice Database (SVD). We show that the proposed optimized fusion method attained an enhanced voice pathology detection accuracy of 98.46%, encompassing a wide spectrum of voice disorders on the SVD database. Furthermore, compared to traditional handcrafted and deep neural network-based techniques, the proposed method demonstrates competitive performance with fewer features.

本研究提出了一种基于改进声门源估计和深度迁移学习神经网络描述符的优化融合的自动无创语音障碍检测和分类方法。提出了一套基于声门源估计器和预训练的Inception-ResNet-v2卷积神经网络特征的改进描述符，用于语音障碍检测和分类任务。改进后的特征集使用了梅尔倒谱系数、谐波模型、鉴相方法、失真偏差描述子、常规小波和声门源估计特征。本研究采用早期描述符级融合来提高性能，然而，融合导致更高的特征向量维数。利用自然启发的黏菌算法去除冗余并选择最佳判别特征。最后，使用k -最近邻（KNN）分类器执行分类。采用不同的特征组合、有和没有特征选择以及两个流行的数据集：阿拉伯语语音病理数据库（AVPD）和Saarbrucken语音数据库（SVD）对所提出的算法进行了广泛的实验评估。我们的研究表明，所提出的优化融合方法获得了98.46%的语音病理检测准确率，涵盖了SVD数据库中广泛的语音疾病。此外，与传统的手工制作和基于深度神经网络的技术相比，该方法具有较少的特征，具有竞争力。

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引用次数: 0

e-Health and artificial intelligence: Emerging trends, models, and applications

Healthcare analytics (New York, N.Y.)

Pub Date : 2024-12-01 DOI: 10.1016/j.health.2024.100354

Yu-Chen Hu, Pelin Angin, Haiming Liu, Debnath Bhattacharyya

引用次数: 0

An open-source application for obtaining retrospective and prospective insights into overall hospital quality star ratings

Healthcare analytics (New York, N.Y.)

Pub Date : 2024-12-01 DOI: 10.1016/j.health.2024.100371

Kenneth J. Locey, Brian D. Stein, Ryan Schipfer, Brittnie Dotson, Leslie Klemp

Overall Hospital Quality Star Ratings (overall star ratings) are designed to assist healthcare consumers by summarizing dozens of hospital quality measures. These ratings are also used by hospitals to direct quality improvements and are often used in healthcare research. However, no analytical tools have been developed to provide insights into the data, measures, and scores of the overall star rating system. To this end, we developed a novel open-source application to provide retrospective insights, prospective estimates, and research-ready data. Users can 1) examine changes in hospital performance from 2021 onward, 2) recalculate overall star ratings based on hypothetical improvements, 3) download data for all hospitals included in the overall star rating system since 2021, and 4) obtain prospective estimates based on the overall star rating methodology and its data source (Care Compare). We demonstrate 99.6% accuracy when estimating overall star ratings six months prior to public release. Estimates of whether hospitals will retain their star rating are up to 90% accurate a year before public release. We discuss the use of our application in healthcare research and the potential for similar tools to be developed for other hospital rating and ranking systems.

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引用次数: 0

A metafrontier and Malmquist productivity index approach for analyzing biased technological and efficiency change in Taiwanese traditional Chinese medicine

Healthcare analytics (New York, N.Y.)

Pub Date : 2024-12-01 DOI: 10.1016/j.health.2024.100372

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

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.

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引用次数: 0

Artificial intelligence and diagnostic healthcare using computer vision and medical imaging

Healthcare analytics (New York, N.Y.)

Pub Date : 2024-12-01 DOI: 10.1016/j.health.2024.100352

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

引用次数: 0

Machine learning for smart health and distributed biomedical services

Healthcare analytics (New York, N.Y.)

Pub Date : 2024-12-01 DOI: 10.1016/j.health.2024.100363

Chinmay Chakraborty, Saïd Mahmoudi, Guangjie Han, Rubén González Crespo

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 : 2024-11-23 DOI: 10.1016/j.health.2024.100370

Sara Mihandoost

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%.

心房颤动（房颤）是一种常见的心律不齐类型，早期检测可显著改善治疗效果和预后。单导联心电图（ECG）设备在监测全球患者心脏健康方面受到广泛关注。标准化的心电信号监测已证明可显著降低与严重心血管疾病相关的死亡率。然而，心房颤动的自动检测方法还需要大力改进。本研究提出了一种新方法，利用心电信号的周期性分析，通过频谱相关函数（SCF）揭示心电信号 QRS-T（代表心脏电活动的主要波形成分）复合体之间的频谱隐藏周期性。为验证所提方法的性能，在 2017 PhysioNet 挑战赛数据集上，将单个心电图的 SCF 系数应用于卷积递归神经网络（CRNN），该网络由卷积层和长短期记忆层组成。结果表明，所提出的方法能通过 SCF 系数有效地表示心电信号，从而准确检测出房颤，平均准确率为 92.76%，平均 F1 分数为 89.1%。

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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 : 2024-10-29 DOI: 10.1016/j.health.2024.100368

Most. Jannatul Ferdous, Rifat Shahriyar

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.

中风是一种可能致命的脑部疾病，会导致大脑供血中断。因此，脑细胞会因缺氧和缺乏营养而开始死亡。中风后，每一分钟都至关重要。在中风期间，每分钟都有一百万或更多的脑细胞死亡。及时发现中风可以避免对大脑造成持久伤害，甚至挽救患者的生命。医生建议通过脑部计算机断层扫描（CT）图像来尽早发现中风。如果医生延误 CT 诊断或做出错误诊断，可能会危及生命。因此，通过脑部 CT 扫描图像自动诊断中风将对中风患者有益。本研究基于脑部 CT 图像，利用迁移学习技术更新了三个预先训练好的卷积神经网络（CNN）模型，分别命名为 Inceptionv3、MobileNetv2 和 Xception。本文提出了一种新的集合卷积神经网络（ENSNET）模型，用于从脑部 CT 扫描图像自动预测脑中风。ENSNET 是名为 InceptionV3 和 Xception 的两个改进 CNN 模型的平均值。我们采用以下指标来评估性能矩阵：准确度、精确度、召回率、f1-分数、混淆矩阵、准确度与历时的关系、损失与历时的关系以及接收者操作特征曲线（ROC）。经调节的 Inceptionv3 的准确率为 97.48%，经调节的 MobileNetv2 的准确率为 83.29%，经调节的 Xception 的准确率为 96.11%。尽管如此，建议的集合模型 ENSNET 在通过脑 CT 扫描诊断中风方面的表现优于其他模型，准确率为 98.86%，精确率为 97.71%，召回率为 98.46%，f1 分数为 98.08%，ROC 曲线下面积（AUC）为 98.74%。因此，与其他模型相比，所提出的 ENSNET 模型能更成功地从脑部计算机断层扫描图像中检测出脑卒中。

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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 : 2024-10-25 DOI: 10.1016/j.health.2024.100367

Makoto Nakakita , Teruo Nakatsuma

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.

本研究确定了可能影响日本人口自评健康（SRH）这一健康状况重要指标的社会经济因素。我们使用面板数据 logit 模型来同时估计个人属性、生活环境和社会条件的影响。为了实现面板数据 logit 模型的稳定估计，我们采用了分层贝叶斯建模和马尔可夫链蒙特卡罗（MCMC）方法来进行估计。此外，我们还使用了辅助-效率交织策略（ASIS）算法来提高面板数据 logit 模型的 MCMC 方法的效率。结果表明，日本人口的性健康和生殖健康受到人口和社会经济因素（如年龄、婚姻状况、教育背景和就业状况）以及日常习惯（如饮酒频率）的影响。我们还得出了与以往研究文献不同的结果。这些结果可能反映了各国在国民性方面的差异。由于性健康和生殖健康是对健康状况的主观衡量，往往与实际健康状况存在差异，因此在评估人口中个人的实际健康状况时，剔除民族特色对性健康和生殖健康的影响至关重要。研究结果为解决这些因素提供了重要启示，以便更好地了解日本的性健康和生殖健康状况。

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