G Jagadamba, R Shashidhar, Ravi Vinayakumar, Mallu Sahana, Alahmadi Tahani Jaser
Department of Information Science and Engineering, Siddaganaga Institute of Technology, Tumakuru, Karnataka- 57210, India.
Department of Electronics and Communication Engineering, JSS Science and Technology University, Mysuru, Karnataka 570006, India.
Open Respir Med J. 2024 Jun 6;18:e18743064296470. doi: 10.2174/0118743064296470240520075316. eCollection 2024.
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.
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.
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.
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.
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.
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.
电子健康记录（EHRs）是实时的数字患者记录，可全面概述个人的完整健康数据。电子健康记录有助于做出更好的医疗决策，提供基于证据的患者治疗，并跟踪患者的临床进展。电子健康记录为分析和对比检查结果及其他数据提供了一系列新机会，创建了适当的信息管理机制以提高效率、加快问题解决和识别速度。
本研究的目的是实施一个可互操作的电子健康记录系统，通过用于早期识别肺癌的决策支持系统来提高医疗质量。
所提议系统的主要目标是开发一个安卓应用程序，用于维护电子健康记录系统和使用深度学习进行疾病早期检测的决策支持系统。第二个目标是研究肺部疾病的早期阶段，以便使用决策支持系统进行预测/检测。
为提取患者的电子健康记录数据，开发了一个安卓应用程序。该安卓应用程序有助于收集每个患者的数据。收集到的数据用于创建一个用于早期预测肺癌的决策支持系统。为训练、测试和验证肺癌预测，从现成的数据集中收集了一些样本，并收集了一些患者的数据。从患者那里收集的有效数据涵盖年龄在40至70岁之间的男性和女性患者。在实验过程中，共考虑了316张图像。测试时将数据集按80:20进行划分。为进行评估，对肺癌检测中的3种不同疾病，即大细胞癌、腺癌和鳞状细胞癌进行了人工分类。
第一个模型针对电子健康记录与数据收集和更新的互操作性约束进行了测试。对于疾病检测系统，通过考虑80:20的训练和测试比例，对大细胞癌、腺癌和鳞状细胞癌类型进行了肺癌预测。在所考虑的336张图像中，大细胞癌的预测比腺癌和鳞状细胞癌少。分析还表明，大细胞癌主要发生在因吸烟的男性中，而在女性中则表现为乳腺癌。
由于医疗行业的挑战日益增加，一个安全、可互操作的电子健康记录可以帮助患者和医生通过安卓应用程序高效地访问患者数据。因此，尝试并成功使用了一个基于深度学习模型的决策支持系统进行疾病检测。对肺癌的早期疾病检测进行了评估，该模型的准确率达到了93%。在未来的工作中，可以进行电子健康记录数据的整合以早期检测各种疾病。
