■电子健康记录(EHR)是实时的,数字病人记录,提供一个人的完整的健康数据的全面概述。电子健康记录(EHR)提供更好的医疗决策和基于证据的患者治疗,并跟踪患者的临床发展。EHR为分析和对比考试结果和其他数据提供了一系列新的机会,建立适当的信息管理机制以提高效率,快速决议,和身份证明。
■这项研究的目的是实施一个可互操作的EHR系统,通过决策支持系统来提高护理质量,以识别早期肺癌。
■所提出的系统的主要目标是开发一个Android应用程序,用于使用深度学习来维护EHR系统和决策支持系统,以便早期发现疾病。第二个目标是研究肺部疾病的早期阶段,以使用决策支持系统进行预测/检测。
■为了提取患者的EHR数据,开发了一个android应用程序。Android应用程序帮助积累每个患者的数据。累积的数据用于创建用于早期预测肺癌的决策支持系统。为了训练,test,并验证肺癌的预测,我们从现成的数据集中收集了一些样本和一些来自患者的数据.来自患者的有效数据收集包括40至70岁的年龄范围以及男性和女性患者。在实验过程中,总共316张图像被考虑。通过将数据集考虑到80:20分区来进行测试。为了评估的目的,对3种不同的疾病进行了手动分类,比如大细胞癌,腺癌,和鳞状细胞癌疾病在肺癌检测中的作用。
■针对EHR与数据收集和升级的互操作性约束,测试了第一个模型。说到疾病检测系统,肺癌被预测为大细胞癌,腺癌,和鳞状细胞癌类型,考虑80:20的训练和测试比例。在考虑的336张图像中,与腺癌和鳞状细胞癌相比,大细胞癌的预测较少。分析还显示,由于吸烟,男性主要发生大细胞癌,女性发现为乳腺癌。
■随着医疗保健行业的挑战日益增加,一个安全的,可互操作的EHR可以帮助患者和医生使用Android应用程序高效地访问患者数据。因此,尝试使用深度学习模型的决策支持系统,并成功用于疾病检测.对肺癌的早期疾病检测进行了评估,模型的准确率达到了93%。在今后的工作中,可以进行EHR数据的整合以早期检测各种疾病。
UNASSIGNED: Electronic health records (EHRs) are live, digital patient records that provide a thorough overview of a person\'s complete health data. Electronic health records (EHRs) provide better healthcare decisions and evidence-based patient treatment and track patients\' clinical development. The EHR offers a new range of opportunities for analyzing and contrasting exam findings and other data, creating a proper information management mechanism to boost effectiveness, quick resolutions, and identifications.
UNASSIGNED: The aim of this studywas to implement an interoperable EHR system to improve the quality of care through the decision support system for the identification of lung cancer in its early stages.
UNASSIGNED: The main objective of the proposed system was to develop an Android application for maintaining an EHR system and decision support system using deep learning for the early detection of diseases. The second objective was to study the early stages of lung disease to predict/detect it using a decision support system.
UNASSIGNED: To extract the EHR data of patients, an android application was developed. The android application helped in accumulating the data of each patient. The accumulated data were used to create a decision support system for the early prediction of lung cancer. To train, test, and validate the prediction of lung cancer, a few samples from the ready dataset and a few data from patients were collected. The valid data collection from patients included an age range of 40 to 70, and both male and female patients. In the process of experimentation, a total of 316 images were considered. The testing was done by considering the data set into 80:20 partitions. For the evaluation purpose, a manual classification was done for 3 different diseases, such as large cell carcinoma, adenocarcinoma, and squamous cell carcinoma diseases in lung cancer detection.
UNASSIGNED: The first model was tested for interoperability constraints of EHR with data collection and updations. When it comes to the disease detection system, lung cancer was predicted for large cell carcinoma, adenocarcinoma, and squamous cell carcinoma type by considering 80:20 training and testing ratios. Among the considered 336 images, the prediction of large cell carcinoma was less compared to adenocarcinoma and squamous cell carcinoma. The analysis also showed that large cell carcinoma occurred majorly in males due to smoking and was found as breast cancer in females.
UNASSIGNED: As the challenges are increasing daily in healthcare industries, a secure, interoperable EHR could help patients and doctors access patient data efficiently and effectively using an Android application. Therefore, a decision support system using a deep learning model was attempted and successfully used for disease detection. Early disease detection for lung cancer was evaluated, and the model achieved an accuracy of 93%. In future work, the integration of EHR data can be performed to detect various diseases early.