基于本体的COVID-19临床决策支持系统的开发

Vinu Sherimon, P. Sherimon, Rahul V. Nair, Renchi Mathew, Sandeep M. Kumar, Khalid Shaikh, Hilal Khalid Al Ghafri, Huda Salim Al Shuaili
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摘要

导言:人类正在经历一个严重不稳定的时期和一场前所未有的全球卫生灾难。新冠肺炎疫情以前所未有的速度在全球蔓延。在这方面,我们在阿曼苏丹国进行了一个快速研究项目。我们开发了ecovid19应用程序,这是一个基于本体的临床决策支持系统(CDSS),具有远程会议功能,可为阿曼苏丹国阿曼皇家警察(ROP)的初级卫生中心/卫星诊所提供简单,快速的诊断和治疗。材料与方法:用本体表示领域知识和临床指南。本体是医学知识形式化编码最有力的方法之一。主要数据来自ROP医院的医疗团队,而次要数据来自发表在知名期刊上的文章。该应用程序为大众用户提供了文字界面和人工智能语音界面的新冠肺炎症状检查器,有英语和阿拉伯语两种语言。根据给定的信息,症状检查器向用户提供建议。如果感染风险高,将引导疑似病例到附近诊所就诊。根据病人现时在诊所的病情,理查会向诊检人员、医生、放射科医生和化验室技术员就程序和药物提供适当的建议。我们使用teatable Machine创建了一个用于分析x射线的TensorFlow模型。我们的CDSS也有一个基于webbrtc(网络实时通信系统)的远程会议选项,以便在患者出现困难或需要专家意见时与专家临床医生进行沟通。结果:ROP医院的专科医生对我们的CDSS进行了测试,并根据他们的建议和建议对用户界面进行了修改。为了评估临床效果,研究小组设置了多种类型的测试案例。精密度、灵敏度(召回率)、特异性和准确性足以预测各种类型的患者实例。结论:拟议的CDSS有潜力显著提高向阿曼公民提供的护理质量。它也可以适应其他可怕的流行病。
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eCOVID19 – Development of Ontology-based Clinical Decision Support System for COVID-19
Introduction: Humankind is passing through a period of significant instability and a worldwide health catastrophe that has never been seen before. COVID-19 spread over the world at an unprecedented rate. In this context, we undertook a rapid research project in the Sultanate of Oman. We developed ecovid19 application, an ontology-based clinical decision support system (CDSS) with teleconference capability for easy, fast diagnosis and treatment for primary health centers/Satellite Clinics of the Royal Oman Police (ROP) of Sultanate of Oman.Materials and Methods: The domain knowledge and clinical guidelines are represented using ontology. Ontology is one of the most powerful methods for formally encoding medical knowledge. The primary data was from the ROP hospital's medical team, while the secondary data came from articles published in reputable journals. The application includes a COVID-19 Symptom checker for the public users with a text interface and an AI-based voice interface and is available in English and Arabic. Based on the given information, the symptom checker provides recommendations to the user. The suspected cases will be directed to the nearby clinic if the risk of infection is high. Based on the patient's current medical condition in the clinic, the CDSS will make suitable suggestions to triage staff, doctors, radiologists, and lab technicians on procedures and medicines. We used Teachable Machine to create a TensorFlow model for the analysis of X-rays. Our CDSS also has a WebRTC (Web Real-Time Communication system) based teleconferencing option for communicating with expert clinicians if the patient develops difficulties or if expert opinion is requested.Results: The ROP hospital's specialized doctors tested our CDSS, and the user interfaces were changed based on their suggestions and recommendations. The team put numerous types of test cases to assess the clinical efficacy. Precision, sensitivity (recall), specificity, and accuracy were adequate in predicting the various categories of patient instances.Conclusion: The proposed CDSS has the potential to significantly improve the quality of care provided to Oman's citizens. It can also be tailored to fit other terrifying pandemics.
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