Computer methods and programs in biomedicine update最新文献_第7页

A comprehensive review of the use of Shapley value to assess node importance in the analysis of biological networks 在生物网络分析中使用Shapley值来评估节点重要性的全面回顾

Computer methods and programs in biomedicine update

Pub Date : 2025-01-01 DOI: 10.1016/j.cmpbup.2025.100185

Giang Pham, Paolo Milazzo

Background:

In 2017, Lundberg and Lee introduced SHAP, a breakthrough in Explainable AI, creatively applying the Shapley value to estimate the importance of input features in machine learning outputs. The Shapley value, from cooperative game theory, fairly distributes system gains among participants. Inspired by SHAP’s success, this survey explores the application of Shapley value-based methods in biological network analysis.

Method:

We conducted a comprehensive literature search on the application of the Shapley value in biological network analysis from 2004 to 2024. From this, we focused on studies that applied the Shapley value in innovative and non-trivial ways, distinct from its typical usage.

Result:

The review identified six original studies that provide novel applications of the Shapley value in analyzing biological networks. These methods have also inspired further development and applications. For each, we discuss the foundational contributions, subsequent advancements, and applications.

Discussion:

Although the reviewed methods share the common objective of using the Shapley value to estimate an element’s contribution within a system, each one takes a distinct approach to modeling the cooperative game. Some methods employ game settings that enable more efficient Shapley value calculations, albeit with a narrower scope, as they are tailored to specific problems. Other methods offer broader applicability but encounter the usual computational challenges associated with calculating the exact Shapley value due to its time complexity. Fortunately, these challenges can be mitigated through the use of approximation techniques. Despite the computational challenges, Shapley value-based methods demonstrate to be beneficial for the interpretation of biological networks.

背景：2017年，Lundberg和Lee引入了SHAP，这是可解释人工智能的突破，创造性地应用Shapley值来估计机器学习输出中输入特征的重要性。合作博弈论中的Shapley值将系统收益公平地分配给参与者。受Shapley的成功启发，本调查探讨了基于Shapley值的方法在生物网络分析中的应用。方法：对2004 ~ 2024年Shapley值在生物网络分析中的应用进行了全面的文献检索。由此，我们将重点放在以创新和非琐碎的方式应用Shapley值的研究上，这与它的典型用法不同。结果：该综述确定了六项原始研究，这些研究提供了Shapley值在分析生物网络中的新应用。这些方法也激发了进一步的开发和应用。对于每一个，我们讨论了基础的贡献，随后的进展和应用。讨论：尽管这些方法都有一个共同的目标，即使用Shapley值来评估系统中某个元素的贡献，但每种方法都采用了不同的方法来模拟合作博弈。有些方法采用了能够更有效地进行Shapley值计算的游戏设置，尽管范围较窄，因为它们是针对特定问题量身定制的。其他方法提供了更广泛的适用性，但由于其时间复杂性，在计算精确的Shapley值时遇到了通常的计算挑战。幸运的是，这些挑战可以通过使用近似技术得到缓解。尽管存在计算方面的挑战，基于Shapley值的方法证明对生物网络的解释是有益的。

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引用次数: 0

Efficient synthesis of 3D MR images for schizophrenia diagnosis classification with generative adversarial networks 基于生成对抗网络的精神分裂症诊断分类三维MR图像的高效合成

Computer methods and programs in biomedicine update

Pub Date : 2025-01-01 DOI: 10.1016/j.cmpbup.2025.100197

Sebastian King , Yasmin Hollenbenders , Alexandra Reichenbach

Schizophrenia and other psychiatric disorders can greatly benefit from objective decision support in diagnosis and therapy. Machine learning approaches based on neuroimaging, e.g. magnetic resonance imaging (MRI), have the potential to serve this purpose. However, the medical data sets these algorithms can be trained on are often rather small, leading to overfit, and the resulting models can therewith not be transferred into a clinical setting. The generation of synthetic images from real data is a promising approach to overcome this shortcoming. Due to the small data set size and the size and complexity of medical images, i.e. their three-dimensional nature, those algorithms are challenged on several levels. We develop four generative adversarial network (GAN) architectures that tackle these challenges and evaluate them systematically with a data set of 193 MR images of schizophrenia patients and healthy controls. The best architecture, a GAN with spectral normalization regulation and an additional encoder (α-SN-GAN), is then extended with an auxiliary classifier into an ensemble of networks capable of generating distinct image sets for the two diagnostic categories. The synthetic images increase the accuracy of a diagnostic classifier from a baseline accuracy of around 61 % to 79 %. This novel end-to-end pipeline for schizophrenia diagnosis demonstrates a data and memory efficient approach to support clinical decision-making that can also be transferred to support other psychiatric disorders.

精神分裂症和其他精神疾病在诊断和治疗中可以从客观决策支持中获益。基于神经成像的机器学习方法，例如磁共振成像（MRI），有可能服务于这一目的。然而，这些算法可以训练的医疗数据集往往相当小，导致过拟合，因此产生的模型不能转移到临床环境中。从真实数据生成合成图像是克服这一缺点的一种很有前途的方法。由于数据集规模小，医学图像的大小和复杂性，即它们的三维性质，这些算法在几个层面上受到挑战。我们开发了四个生成对抗网络（GAN）架构来解决这些挑战，并使用193个精神分裂症患者和健康对照的MR图像数据集系统地评估它们。最佳结构是具有光谱归一化调节和附加编码器（α-SN-GAN）的GAN，然后通过辅助分类器扩展为能够为两种诊断类别生成不同图像集的网络集成。合成图像将诊断分类器的准确度从大约61%的基线准确度提高到79%。这种新型的端到端精神分裂症诊断管道展示了一种数据和记忆有效的方法来支持临床决策，也可以转移到支持其他精神疾病。

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引用次数: 0

Diagnosis of Alzheimer's disease using non-linear features of ERP signals through a hybrid attention-based CNN-LSTM model 通过基于注意力的CNN-LSTM混合模型利用ERP信号的非线性特征诊断阿尔茨海默病

Computer methods and programs in biomedicine update

Pub Date : 2025-01-01 DOI: 10.1016/j.cmpbup.2025.100192

Elias Mazrooei Rad , Sayyed Majid Mazinani , Seyyed Ali Zendehbad

Biological signals have a dynamic and non-linear nature, and hence nonlinear analysis is important for understanding the signals. In this study, a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model is proposed for the diagnosis of Alzheimer’s disease (AD) from the Event-Related Potential (ERP) signals obtained from the Electroencephalogram (EEG) data. The P300 component of the ERP signal, derived from acoustic stimulation, is a key indicator of AD, and its amplitude and latency are characterized. By using nonlinear features such as phase diagrams, correlation dimension, entropy, and Lyapunov exponents, the proposed model classifies AD stages. The hybrid CNN-LSTM architecture, enhanced by an attention mechanism, captures both spatial and temporal dependencies in the ERP signals, achieving high accuracy: For healthy people, 95 %, for mild AD patients, 92.5 %, and for severe AD patients, 97.5 %. The model achieves 75 % accuracy in recall mode for healthy individuals, 72.5 % for mild AD, and 87.5 % for severe AD. Results show that the proposed model outperforms traditional methods and provides a robust and accurate diagnostic framework for AD. The result of this approach is to show that the combination of non-linear EEG analysis with advanced deep learning methods could provide early and precise AD detection.

生物信号具有动态和非线性的性质，因此非线性分析对于理解信号非常重要。本研究提出了一种卷积神经网络（CNN）和长短期记忆（LSTM）混合模型，用于从脑电图（EEG）数据中获得的事件相关电位（ERP）信号诊断阿尔茨海默病（AD）。ERP信号的P300分量来源于声刺激，是AD的关键指标，其振幅和潜伏期具有特征。该模型利用相图、相关维数、熵和李亚普诺夫指数等非线性特征对AD阶段进行分类。CNN-LSTM混合架构，通过注意机制增强，捕获ERP信号的空间和时间依赖性，达到很高的准确性：健康人95%，轻度AD患者92.5%，重度AD患者97.5%。在回忆模式下，该模型对健康个体的准确率为75%，对轻度AD的准确率为72.5%，对重度AD的准确率为87.5%。结果表明，该模型优于传统方法，提供了一个鲁棒性和准确性较高的AD诊断框架。该方法的结果表明，将非线性脑电图分析与先进的深度学习方法相结合可以提供早期和精确的AD检测。

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引用次数: 0

Fostering digital health literacy to enhance trust and improve health outcomes 培养数字卫生素养，增强信任并改善卫生成果

Computer methods and programs in biomedicine update

Pub Date : 2024-02-01 DOI: 10.1016/j.cmpbup.2024.100140

Kristine Sørensen

引用次数: 0

Deep learning based detection of silicosis from computed tomography images 基于深度学习的计算机断层扫描图像矽肺病检测

Computer methods and programs in biomedicine update

Pub Date : 2024-01-01 DOI: 10.1016/j.cmpbup.2024.100166

Hamit Aksoy , Ümit Atila , Sertaç Arslan

Artificial intelligence has increasingly been used in interpreting medical images to support the timely treatment of diseases by providing early and accurate diagnosis. Pneumoconiosis is a tissue reaction that develops as a result of the accumulation of inorganic dust in the lungs. The most common types of pneumoconiosis include diseases such as coal worker's pneumoconiosis, silicosis, asbestosis, and siderosis. Silicosis, which has maintained its importance since the 1900s and has seen over 182,000 articles published in the last 10 years, is a global health problem. The automated detection and recognition of silicosis in lung computed tomography (CT) images can be considered the backbone of assisting the silicosis diagnosis process. Automated medical assistance systems developed using artificial intelligence can simplify the medical examination process and reduce the time required to start accurate treatment. Although the literature contains various studies that benefit silicosis diagnosis using chest X-ray images or pneumoconiosis diagnosis using CT images, there is not enough classification study that can particularly aid the diagnosis of silicosis in CT images.

The method of early detection of silicosis from chest radiographs and CT images has been a challenging task due to the high variability among pneumoconiosis readers. Based on the success of deep learning in the classification and segmentation of medical images, this study has shown that deep learning networks and transfer learning algorithms can detect silicosis with high accuracy by classifying CT images. The performance of the six algorithms examined in the study is compared, and the algorithm with the best performance is recommended. Performance criteria such as accuracy, precision, specificity, and F1-score of the algorithms used in the study were calculated. The accuracy rates of the models were obtained as 92.62 %, 93.03 %, 92.76 %, 95.38 %, 97.29 %, and 95.17 % for AlexNet, VGG16, ResNet50, InceptionV3, Xception, and DenseNet121, respectively. These results show that Xception outperformed the other algorithms and was the most successful algorithm in the automatic detection of silicosis with an accuracy rate of 97.29 %.

Additionally, a new dataset consisting of tomography images from silicosis patients is presented in this study. Experimental results have shown that transfer learning algorithms can significantly benefit the diagnosis of silicosis by successfully classifying CT images. The findings of the study highlight the clinical importance of artificial intelligence methods in medical image analysis and early disease diagnosis.

人工智能已越来越多地用于解读医学图像，通过提供早期准确诊断来支持疾病的及时治疗。尘肺病是由于无机粉尘在肺部积聚而产生的一种组织反应。最常见的尘肺类型包括煤工尘肺、矽肺、石棉沉滞症和矽肺等疾病。矽肺病是一个全球性的健康问题，自 20 世纪以来一直受到重视，在过去 10 年中发表了超过 182,000 篇文章。肺部计算机断层扫描（CT）图像中矽肺病的自动检测和识别可被视为辅助矽肺病诊断过程的支柱。利用人工智能开发的自动医疗辅助系统可以简化医疗检查过程，缩短开始准确治疗所需的时间。虽然文献中包含各种有益于使用胸部X光图像进行矽肺诊断或使用CT图像进行尘肺诊断的研究，但特别有助于CT图像中矽肺诊断的分类研究还不够多。由于尘肺病读者之间的差异很大，从胸部X光片和CT图像中早期检测矽肺的方法一直是一项具有挑战性的任务。基于深度学习在医学图像分类和分割方面的成功经验，本研究表明，深度学习网络和迁移学习算法可以通过对CT图像进行分类，高精度地检测出矽肺病。研究中对六种算法的性能进行了比较，并推荐了性能最佳的算法。研究中使用的算法的准确率、精确度、特异性和 F1 分数等性能标准都经过了计算。结果显示，AlexNet、VGG16、ResNet50、InceptionV3、Xception 和 DenseNet121 的准确率分别为 92.62%、93.03%、92.76%、95.38%、97.29% 和 95.17%。这些结果表明，Xception 的表现优于其他算法，是自动检测矽肺病最成功的算法，准确率高达 97.29%。实验结果表明，迁移学习算法能成功地对 CT 图像进行分类，对矽肺病的诊断大有裨益。研究结果凸显了人工智能方法在医学图像分析和早期疾病诊断中的临床重要性。

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引用次数: 0

An evaluation of the commonly used portable medical sensors performance in comparison to clinical test results for telehealth systems 将常用便携式医疗传感器的性能与远程医疗系统的临床测试结果进行对比评估

Computer methods and programs in biomedicine update

Pub Date : 2024-01-01 DOI: 10.1016/j.cmpbup.2024.100147

Rafiqul Islam Maruf , Saori Tou , Rieko Izukura , Yoko Sato , Mariko Nishikitani , Kimiyo Kikuchi , Fumihiko Yokota , Subaru Ikeda , Rakibul Islam , Ashir Ahmed , Masashi Miyazaki , Naoki Nakashima

Background and Objective

One of the primary challenges faced by telehealth systems is the accurate transmission of patient information to remote doctors. In this context, portable medical sensors deployed at the remote patients' end play a crucial role in measuring vital information. There are many sensors available in the market. However, the accuracy of the sensors has been always a concern. The objective of this study is to verify different sensors and create awareness for using accurate sensors to avoid misdiagnosis for the patients’ safety.

Methods

This study considered the test result of a Japanese clinical pathology laboratory as the reference gold standard. The clinical pathology laboratory uses 1) Hexokinase UV method for blood glucose, 2) Enzymatic Determination method for cholesterol, 3) Automatic Analyzer (EDTA-2 K) of Hemoglobin, and 4) Uricase POD method for uric acid. To assess the performance of a medical sensor, its test results were compared to the gold standard test results obtained from the laboratory using the same sample. A Normalized Root Mean Square Error (NRMSE) threshold of less than 0.2 was established as the criterion for determining whether the medical sensor's performance fell within an acceptable range.

Results

Among the eight most commonly used blood glucose devices in the Asian market, only one device was deemed acceptable with NRMSE less than 0.2. However, all four devices found in the Japanese market showed their acceptability. In the case of cholesterol, hemoglobin, and uric acid devices, only a limited number of items were available in Asian markets. Some of the hemoglobin and uric acid devices were found to be somewhat acceptable, while all the cholesterol sensors were found erroneous.

Conclusions

This study has clearly shown the issues with the portable medical sensors and recommends the device approval authority of each country to approve sales of the quality sensors only for patients’ safety.

背景与目标远程医疗系统面临的主要挑战之一是向远程医生准确传输病人信息。在这种情况下，部署在远程患者端的便携式医疗传感器在测量重要信息方面发挥着至关重要的作用。市场上有许多传感器。然而，传感器的准确性一直是个问题。本研究的目的是验证不同的传感器，提高人们使用准确传感器的意识，以避免误诊，保障患者的安全。该临床病理实验室使用 1) 紫外光六磷酸酶法检测血糖，2) 酶法测定胆固醇，3) 自动分析仪（EDTA-2 K）检测血红蛋白，4) 尿酸酶 POD 法检测尿酸。为评估医疗传感器的性能，将其测试结果与实验室使用相同样本获得的金标准测试结果进行比较。结果在亚洲市场最常用的八种血糖设备中，只有一种设备的归一化均方根误差（NRMSE）小于 0.2，被认为是可以接受的。然而，日本市场上的所有四种血糖仪都显示出其可接受性。至于胆固醇、血红蛋白和尿酸检测仪，亚洲市场上只有少量产品。这项研究清楚地表明了便携式医疗传感器存在的问题，建议各国的设备审批机构只批准销售高质量的传感器，以确保患者的安全。

{"title":"An evaluation of the commonly used portable medical sensors performance in comparison to clinical test results for telehealth systems","authors":"Rafiqul Islam Maruf , Saori Tou , Rieko Izukura , Yoko Sato , Mariko Nishikitani , Kimiyo Kikuchi , Fumihiko Yokota , Subaru Ikeda , Rakibul Islam , Ashir Ahmed , Masashi Miyazaki , Naoki Nakashima","doi":"10.1016/j.cmpbup.2024.100147","DOIUrl":"10.1016/j.cmpbup.2024.100147","url":null,"abstract":"<div><h3>Background and Objective</h3><p>One of the primary challenges faced by telehealth systems is the accurate transmission of patient information to remote doctors. In this context, portable medical sensors deployed at the remote patients' end play a crucial role in measuring vital information. There are many sensors available in the market. However, the accuracy of the sensors has been always a concern. The objective of this study is to verify different sensors and create awareness for using accurate sensors to avoid misdiagnosis for the patients’ safety.</p></div><div><h3>Methods</h3><p>This study considered the test result of a Japanese clinical pathology laboratory as the reference gold standard. The clinical pathology laboratory uses 1) Hexokinase UV method for blood glucose, 2) Enzymatic Determination method for cholesterol, 3) Automatic Analyzer (EDTA-2 K) of Hemoglobin, and 4) Uricase POD method for uric acid. To assess the performance of a medical sensor, its test results were compared to the gold standard test results obtained from the laboratory using the same sample. A Normalized Root Mean Square Error (NRMSE) threshold of less than 0.2 was established as the criterion for determining whether the medical sensor's performance fell within an acceptable range.</p></div><div><h3>Results</h3><p>Among the eight most commonly used blood glucose devices in the Asian market, only one device was deemed acceptable with NRMSE less than 0.2. However, all four devices found in the Japanese market showed their acceptability. In the case of cholesterol, hemoglobin, and uric acid devices, only a limited number of items were available in Asian markets. Some of the hemoglobin and uric acid devices were found to be somewhat acceptable, while all the cholesterol sensors were found erroneous.</p></div><div><h3>Conclusions</h3><p>This study has clearly shown the issues with the portable medical sensors and recommends the device approval authority of each country to approve sales of the quality sensors only for patients’ safety.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100147"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000144/pdfft?md5=6cb858a0f60b29a1fc21629928f765b9&pid=1-s2.0-S2666990024000144-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140091696","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

S-1 plus oxaloplatin (S-1OX) versus capecitabine plus oxaloplatin (CAPOX) for advanced gastric cancer: A systematic review and meta-analysis S-1加奥沙利铂（S-1OX）与卡培他滨加奥沙利铂（CAPOX）治疗晚期胃癌：系统综述与荟萃分析

Computer methods and programs in biomedicine update

Pub Date : 2024-01-01 DOI: 10.1016/j.cmpbup.2024.100151

S.M.Zeeshan Qadar , Zhiyong Dong , Sheikh Mohammed Shariful Islam , Jianxue Wang , Xiling Xu , Fakhsheena Anjum , Sana Shamim , Bafreen Sherif , Sundas Ali

Purpose

Several therapeutic combinations are available for the treatment of advanced gastric cancer (AGC). It is unclear which combinations are most beneficial to the patients. The purpose of this study was to compare the efficacy and safety of Tegafur/ gimeracil/ oteracil (S-1) plus oxaliplatin (S-1OX) with capecitabine plus oxaliplatin (CAPOX) in patients with AGC.

Materials and Methods

Relevant randomized controlled trials were searched in MEDLINE, EMBASE, The Cochrane Library (CENTRAL), two major Chinese biomedical databases (CBM, CNKI), and registry centers until July 22, 2019, with no language restrictions. Data were extracted for overall response rate (ORR), time to progression (TTP), overall survival time (OST), and toxicity. The systematic review was performed according to the recommendations of the Cochrane collaboration. RevMan 5.3.1 was used for statistical analysis.

Results

A total of 6 randomized controlled trials involving 911 patients were included. The quality of the trials was less than 3 points. All the trials demonstrated a significantly improved toxicity (hand-foot syndrome and neuropathy) in the S-1OX trials (p < 0.05). There was no statistically significant difference (p > 0.05) between S-1OX versus CAPOX in terms of ORR, OST, TTP. Any of the subgroup analyses did not exhibit heterogeneity, so the fixed-effects model be used to execute the subgroup meta-analysis.

Conclusions

Both S-1OX and CAPOX showed similar efficacy for treatment of AGC. However, S1-OX appeared to present less toxicity in terms of hand-foot syndrome and neuropathy as compared to CAPOX.

目的目前有多种治疗组合可用于晚期胃癌（AGC）的治疗。目前尚不清楚哪种联合疗法对患者最有益。本研究旨在比较替加氟（Tegafur）/吉莫拉嘧啶（Gimeracil）/奥特拉西（Oteracil）（S-1）加奥沙利铂（S-1OX）与卡培他滨加奥沙利铂（CAPOX）对AGC患者的疗效和安全性。材料与方法截至2019年7月22日，在MEDLINE、EMBASE、The Cochrane Library（CENTRAL）、两大中文生物医学数据库（CBM、CNKI）和注册中心检索了相关随机对照试验，无语言限制。提取的数据包括总反应率（ORR）、进展时间（TTP）、总生存时间（OST）和毒性。系统综述根据 Cochrane 协作组织的建议进行。结果共纳入了 6 项随机对照试验，涉及 911 名患者。试验质量低于 3 分。所有试验均显示，S-1OX 试验的毒性（手足综合征和神经病变）明显改善（p < 0.05）。在 ORR、OST 和 TTP 方面，S-1OX 与 CAPOX 之间的差异无统计学意义（p > 0.05）。结论S-1OX和CAPOX治疗AGC的疗效相似。然而，与 CAPOX 相比，S1-OX 在手足综合征和神经病变方面的毒性似乎更小。

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引用次数: 0

Delayed matrix pencil method for local shear wave viscoelastographic estimation 用于局部剪切波粘弹性估算的延迟矩阵铅笔法

Computer methods and programs in biomedicine update

Pub Date : 2024-01-01 DOI: 10.1016/j.cmpbup.2024.100156

X. Li, S. Turco, R.M. Aarts, H. Wijkstra, M. Mischi

Shear wave (SW) elastography is an ultrasound imaging modality that provides quantitative viscoelastic measurements of tissue. The phase difference method allows for local estimation of viscoelasticity by computing the dispersion curve using phases from two laterally-spaced pixels. However, this method is sensitive to measurement noise in the estimated SW particle velocities. Hence, we propose the delayed matrix pencil method to investigate this problem, and validated its feasibility both in-silico and in-vitro. The performance was compared with the original phase difference method and other two alternative techniques based on lowpass filtering and discrete wavelet transform denoising. The estimated viscoelastic values are summarized in box plots and followed by statistical analysis. Results from both studies show the proposed method to be more robust to noise with the smallest interquartile range in both elasticity and viscosity.

剪切波（SW）弹性成像是一种超声成像模式，可对组织的粘弹性进行定量测量。相位差法利用两个横向间隔像素的相位来计算频散曲线，从而对粘弹性进行局部估计。然而，这种方法对估计的 SW 粒子速度中的测量噪声很敏感。因此，我们提出了延迟矩阵铅笔法来研究这个问题，并在实验室和体外验证了其可行性。我们将其性能与原始相位差法和其他两种基于低通滤波和离散小波变换去噪的替代技术进行了比较。估计的粘弹性值以盒图的形式汇总，然后进行统计分析。这两项研究的结果表明，建议的方法对噪声的鲁棒性更强，弹性和粘度的四分位数间距最小。

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引用次数: 0

Studying usability of public health surveillance maps through framework based heuristic evaluation 通过基于框架的启发式评估研究公共卫生监测地图的可用性

Computer methods and programs in biomedicine update

Pub Date : 2024-01-01 DOI: 10.1016/j.cmpbup.2024.100143

Hurmat Ali Shah, Mowafa Househ, Jens Schneider, Dena A. Al-Thani, Marco Agus

Public health surveillance systems play a crucial role in detecting and responding to disease outbreaks. Visualizations of surveillance data are important for decision-making, but little attention has been paid to the usability and interaction of such systems. In this paper, we developed a set of 10 heuristics to assess the visualization and usability of public health surveillance systems. The heuristics cover aspects of perception, cognition, and interaction. The perception deals with how the system looks in the first glance and whether it has pleasant effect on the user or otherwise. Cognition deals with the question of whether enough information is provided to use the system, while usability and interaction deal with whether the system is user-friendly in terms of the tools provided for interaction and use. We recruited a panel of experts to evaluate a set of systems using our heuristics. Results showed that there was variation in the scores of the experts' assessments, indicating the importance of multiple expert evaluations. Our heuristics provide a practical and comprehensive tool for assessing the visualization and usability of public health surveillance systems, which can lead to improved decision-making and ultimately better public health outcomes. The results suggest that the heuristic based evaluation through a panel of experts can provide meaningful results and insights into the usability aspects of public health systems. The results suggest that for some systems there can be agreement in terms of evaluation while for some other systems the experts’ opinions can vary based on the weightage and importance each expert gives to a particular aspect.

公共卫生监测系统在检测和应对疾病爆发方面发挥着至关重要的作用。监测数据的可视化对决策非常重要，但人们很少关注此类系统的可用性和交互性。在本文中，我们开发了一套 10 个启发式方法来评估公共卫生监控系统的可视化和可用性。启发式方法涵盖了感知、认知和交互等方面。感知涉及系统的第一印象如何，以及是否会给用户带来愉悦感。认知涉及是否提供了足够的信息来使用系统的问题，而可用性和交互则涉及系统提供的交互和使用工具是否方便用户。我们招募了一个专家小组，使用我们的启发式方法对一组系统进行评估。结果表明，专家们的评估得分存在差异，这说明了多个专家评估的重要性。我们的启发式方法为评估公共卫生监测系统的可视化和可用性提供了一个实用而全面的工具，可帮助改进决策，最终改善公共卫生成果。结果表明，通过专家小组进行启发式评估可以提供有意义的结果，并深入了解公共卫生系统的可用性。结果表明，对于某些系统，专家们的评价意见是一致的，而对于其他一些系统，专家们的意见则会因每位专家对特定方面的重视程度而有所不同。

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引用次数: 0

Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions 人工智能用于临床预测：探索关键领域和基本功能

Computer methods and programs in biomedicine update

Pub Date : 2024-01-01 DOI: 10.1016/j.cmpbup.2024.100148

Mohamed Khalifa , Mona Albadawy

Background

Clinical prediction is integral to modern healthcare, leveraging current and historical medical data to forecast health outcomes. The integration of Artificial Intelligence (AI) in this field significantly enhances diagnostic accuracy, treatment planning, disease prevention, and personalised care leading to better patient outcomes and healthcare efficiency.

Methods

This systematic review implemented a structured four-step methodology, including an extensive literature search in academic databases (PubMed, Embase, Google Scholar), applying specific inclusion and exclusion criteria, data extraction focusing on AI techniques and their applications in clinical prediction, and a thorough analysis of the collected information to understand AI's roles in enhancing clinical prediction.

Results

Through the analysis of 74 experimental studies, eight key domains, where AI significantly enhances clinical prediction, were identified: (1) Diagnosis and early detection of disease; (2) Prognosis of disease course and outcomes; (3) Risk assessment of future disease; (4) Treatment response for personalised medicine; (5) Disease progression; (6) Readmission risks; (7) Complication risks; and (8) Mortality prediction. Oncology and radiology come on top of the specialties benefiting from AI in clinical prediction.

Discussion

The review highlights AI's transformative impact across various clinical prediction domains, including its role in revolutionising diagnostics, improving prognosis accuracy, aiding in personalised medicine, and enhancing patient safety. AI-driven tools contribute significantly to the efficiency and effectiveness of healthcare delivery.

Conclusion and recommendations

AI's integration in clinical prediction marks a substantial advancement in healthcare. Recommendations include enhancing data quality and accessibility, promoting interdisciplinary collaboration, focusing on ethical AI practices, investing in AI education, expanding clinical trials, developing regulatory oversight, involving patients in the AI integration process, and continuous monitoring and improvement of AI systems.

背景临床预测是现代医疗保健不可或缺的一部分，它利用当前和历史医疗数据来预测健康结果。人工智能（AI）与这一领域的结合大大提高了诊断准确性、治疗计划、疾病预防和个性化护理，从而改善了患者的治疗效果，提高了医疗效率。方法本系统性综述采用了结构化的四步方法，包括在学术数据库（PubMed、Embase、Google Scholar）中进行广泛的文献检索，应用特定的纳入和排除标准，以人工智能技术及其在临床预测中的应用为重点进行数据提取，并对所收集的信息进行全面分析，以了解人工智能在增强临床预测中的作用。结果通过对 74 项实验研究的分析，确定了人工智能可显著增强临床预测的八个关键领域：(1) 疾病的诊断和早期检测；(2) 病程和结果的预后；(3) 未来疾病的风险评估；(4) 个性化医疗的治疗反应；(5) 疾病进展；(6) 再入院风险；(7) 并发症风险；以及 (8) 死亡率预测。肿瘤学和放射学在临床预测中受益于人工智能的专科中名列前茅。人工智能驱动的工具大大提高了医疗服务的效率和有效性。建议包括提高数据质量和可访问性、促进跨学科合作、关注人工智能伦理实践、投资人工智能教育、扩大临床试验、发展监管监督、让患者参与人工智能整合过程，以及持续监控和改进人工智能系统。

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