In healthcare systems,the Internet of Things(IoT)innovation and development approached new ways to evaluate patient data.A cloud-based platform tends to process data generated by IoT medical devices instead of high st...In healthcare systems,the Internet of Things(IoT)innovation and development approached new ways to evaluate patient data.A cloud-based platform tends to process data generated by IoT medical devices instead of high storage,and computational hardware.In this paper,an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography(CT)images of patients with pneumonia,Covid-19,tuberculosis(TB),and cancer.Firstly,the CT images are captured and transmitted to the fog node through IoT devices.In the fog node,the image gets modified into a convenient and efficient format for further processing.advanced encryption Standard(AES)algorithm serves a substantial role in IoT and fog nodes for preventing data from being accessed by other operating systems.Finally,the preprocessed image can be classified automatically in the cloud by using various transfer and ensemble learning models.Herein different pre-trained deep learning architectures(Inception-ResNet-v2,VGG-19,ResNet-50)used transfer learning is adopted for feature extraction.The softmax of heterogeneous base classifiers assists to make individual predictions.As a meta-classifier,the ensemble approach is employed to obtain final optimal results.Disease predicted image is consigned to the recurrent neural network with long short-term memory(RNN-LSTM)for severity analysis,and the patient is directed to seek therapy based on the outcome.The proposed method achieved 98.6%accuracy,0.978 precision,0.982 recalls,and 0.974 F1-score on five class classifications.The experimental findings reveal that the proposed framework assists medical experts with lung disease screening and provides a valuable second perspective.展开更多
目的旨在通过信息系统开发过程中所必需的用户需求分析方法，确定和分析护士在使用纸质版重症监护谵妄筛查量表（ICDSC）评估过程中存在的问题，为制定智能版ICDSC交互式策略提供依据。方法采用文献法和任务走查法2种方法对纸质版ICDSC...目的旨在通过信息系统开发过程中所必需的用户需求分析方法，确定和分析护士在使用纸质版重症监护谵妄筛查量表（ICDSC）评估过程中存在的问题，为制定智能版ICDSC交互式策略提供依据。方法采用文献法和任务走查法2种方法对纸质版ICDSC进行用户需求分析。文献法以“ICDSC”“intensive care delirium screening checklist”和“重症监护谵妄筛查量表”为检索词，在PubMed、Springer、Elsevier、CNIAHL、CNKI和万方数据库中检索涉及ICDSC使用和评价的文献，检索时间为2000年1月-2017年12月，查找文献中对ICU护士使用纸质版ICDSC评估中存在问题的描述。任务走查法采用方便取样，选取ICU护士作为用户代表使用纸质版ICDSC对研究者提供的4份患者案例进行谵妄评估，每个案例都会完整完成ICDSC评估中的8项评估任务。研究者观察和记录护士在完成任务过程中出现的问题。护士样本量以信息饱和为原则（即不再出现新的信息为止）。结果本研究检索到的144篇文献中有7篇提及护士使用纸质版ICDSC评估谵妄过程中存在的问题，包括耗时、条目理解失误、护理记录的缺失／不准确。任务走查法共纳入15名ICU护士，结果显示存在的问题包括耗时、漏项、条目理解失误、计分错误、护理记录的缺失或不准确。结论纸质版ICDSC在使用过程中存在耗时、易漏项、条目理解失误、计分错误和评估信息来源不准确的问题，在进行智能版ICDSC的开发时，应针对这些具体问题设计交互式策略。展开更多
文摘In healthcare systems,the Internet of Things(IoT)innovation and development approached new ways to evaluate patient data.A cloud-based platform tends to process data generated by IoT medical devices instead of high storage,and computational hardware.In this paper,an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography(CT)images of patients with pneumonia,Covid-19,tuberculosis(TB),and cancer.Firstly,the CT images are captured and transmitted to the fog node through IoT devices.In the fog node,the image gets modified into a convenient and efficient format for further processing.advanced encryption Standard(AES)algorithm serves a substantial role in IoT and fog nodes for preventing data from being accessed by other operating systems.Finally,the preprocessed image can be classified automatically in the cloud by using various transfer and ensemble learning models.Herein different pre-trained deep learning architectures(Inception-ResNet-v2,VGG-19,ResNet-50)used transfer learning is adopted for feature extraction.The softmax of heterogeneous base classifiers assists to make individual predictions.As a meta-classifier,the ensemble approach is employed to obtain final optimal results.Disease predicted image is consigned to the recurrent neural network with long short-term memory(RNN-LSTM)for severity analysis,and the patient is directed to seek therapy based on the outcome.The proposed method achieved 98.6%accuracy,0.978 precision,0.982 recalls,and 0.974 F1-score on five class classifications.The experimental findings reveal that the proposed framework assists medical experts with lung disease screening and provides a valuable second perspective.
文摘目的旨在通过信息系统开发过程中所必需的用户需求分析方法，确定和分析护士在使用纸质版重症监护谵妄筛查量表（ICDSC）评估过程中存在的问题，为制定智能版ICDSC交互式策略提供依据。方法采用文献法和任务走查法2种方法对纸质版ICDSC进行用户需求分析。文献法以“ICDSC”“intensive care delirium screening checklist”和“重症监护谵妄筛查量表”为检索词，在PubMed、Springer、Elsevier、CNIAHL、CNKI和万方数据库中检索涉及ICDSC使用和评价的文献，检索时间为2000年1月-2017年12月，查找文献中对ICU护士使用纸质版ICDSC评估中存在问题的描述。任务走查法采用方便取样，选取ICU护士作为用户代表使用纸质版ICDSC对研究者提供的4份患者案例进行谵妄评估，每个案例都会完整完成ICDSC评估中的8项评估任务。研究者观察和记录护士在完成任务过程中出现的问题。护士样本量以信息饱和为原则（即不再出现新的信息为止）。结果本研究检索到的144篇文献中有7篇提及护士使用纸质版ICDSC评估谵妄过程中存在的问题，包括耗时、条目理解失误、护理记录的缺失／不准确。任务走查法共纳入15名ICU护士，结果显示存在的问题包括耗时、漏项、条目理解失误、计分错误、护理记录的缺失或不准确。结论纸质版ICDSC在使用过程中存在耗时、易漏项、条目理解失误、计分错误和评估信息来源不准确的问题，在进行智能版ICDSC的开发时，应针对这些具体问题设计交互式策略。
