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A Medical Guidance Model Driven by Subjective and Objective Knowledge

机译：主观和客观知识驱动的医学指导模型

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摘要

Because of the impact of Corona Virus Disease 2019 (COVID-19), online medical services have developed rapidly and are widely accepted by people. People can find doctors for diagnosis and treatment by the name of the disease online. However, patients usually lack professional medical knowledge and have their own subjective preferences for health services, which makes it difficult for patients to accurately find a doctor that suits them. To this end, we proposed a medical guidance model driven by subjective and objective knowledge to provide decision support to patients. In the proposed model, the doctor's and disease's own information is regarded as objective knowledge, and the information of doctor feature extracted from patient reviews is regarded as subjective knowledge. They are fused into a knowledge graph. On this basis, a knowledge decision engine is designed to recommend the most suitable doctor based on the patient's objective conditions and subjective preferences. Finally, a prototype system is designed and developed to demonstrate the feasibility of the model as above. The system guides patients to improve their objective conditions and subjective preferences through inquiries, and returns recommended doctors to patients in an interpretable manner. The medical guidance model can effectively meet the personalized and professional needs of patients in online medical services, which has good practical value under the digital healthcare continues to become the trend of the future.
机译：由于电晕病毒疾病2019（Covid-19）的影响，在线医疗服务发展迅速，人们被人们广泛接受。人们可以在网上名称找到医生进行诊断和治疗。然而，患者通常缺乏专业的医学知识，并为卫生服务有自己的主观偏好，这使得患者难以准确地找到适合他们的医生。为此，我们提出了由主观和客观知识驱动的医学指导模型，为患者提供决策支持。在拟议的模型中，医生和疾病自己的信息被视为客观知识，从患者评论中提取的医生功能的信息被视为主观知识。它们融合到知识图中。在此基础上，知识决策引擎旨在根据患者的客观条件和主观偏好推荐最合适的医生。最后，设计并开发了一种原型系统，以证明模型的可行性如上所述。该系统指导患者通过查询改善客观条件和主观偏好，并以可诠释的方式将推荐医生返回给患者。医学指导模型可以有效满足在线医疗服务中患者的个性化和专业需求，这在数字医疗保健下具有良好的实用价值，继续成为未来的趋势。

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