A Medical Guidance Model Driven by Subjective and Objective Knowledge

He Yu, Liang Xiao
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引用次数: 1

Abstract

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.
主客观知识驱动的医学指导模式
由于2019冠状病毒病(COVID-19)的影响,在线医疗服务得到了迅速发展,并被人们广泛接受。人们可以在网上通过疾病的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,对医疗服务有自己的主观偏好,这使得患者很难准确地找到适合自己的医生。为此,我们提出了主客观知识驱动的医学指导模型,为患者提供决策支持。在该模型中,医生和疾病本身的信息被视为客观知识,从患者评论中提取的医生特征信息被视为主观知识。它们被融合成一个知识图谱。在此基础上,设计知识决策引擎,根据患者的客观情况和主观偏好,推荐最适合的医生。最后,设计并开发了一个原型系统来验证上述模型的可行性。系统通过查询引导患者改善客观情况和主观偏好,并以可解释的方式将推荐的医生返回给患者。该医疗指导模式能有效满足患者在在线医疗服务中的个性化、专业化需求,在数字医疗持续成为未来趋势的情况下,具有良好的实用价值。
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