Zhang Jing, Li Jianhua, Zhu Yi, Fu Yu, Chen Lixia
School of Cyber Science and Engineering, Southeast University, No. 2 SEU Road, Nanjing, 211189 China.
Engineering Research Center of Blockchain Application, Supervision and Management (Southeast University), Ministry of Education, No. 2 SEU Road, Nanjing, 211189 China.
Health Inf Sci Syst. 2023 Oct 17;11(1):49. doi: 10.1007/s13755-023-00251-w. eCollection 2023 Dec.
Thyroid diseases, especially thyroid tumors, have a huge population in China. The postoperative patients, under China's incomplete tertiary diagnosis and treatment system, will frequently go to tertiary hospitals for follow-up and medication adjustment, resulting in heavy burdens on both specialists and patients. To help postoperative patients recover better against the above adverse conditions, a novel mobile application ThyroidKeeper is proposed as a collaborative AI-based platform that benefits both patients and doctors. In addition to routine health records and management functions, ThyroidKeeper has achieved several innovative points. First, it can automatically adjust medication dosage for patients during their rehabilitation based on their medical history, laboratory indicators, physical health status, and current medication. Second, it can comprehensively predict the possible complications based on the patient's health status and the health status of similar groups utilizing graph neural networks. Finally, the employing of graph neural network models can improve the efficiency of online communication between doctors and patients, help doctors obtain medical information for patients more quickly and precisely, and make more accurate diagnoses. The preliminary evaluation in both laboratory and real-world environments shows the advantages of the proposed ThyroidKeeper system.
在中国，甲状腺疾病，尤其是甲状腺肿瘤患者数量众多。在我国三级诊疗体系尚不完善的情况下，术后患者经常前往三级医院进行随访和调整用药，这给专家和患者都带来了沉重负担。为了帮助术后患者在上述不利条件下更好地康复，我们提出了一种新型移动应用程序ThyroidKeeper，它是一个基于人工智能的协作平台，对患者和医生都有益处。除了常规的健康记录和管理功能外，ThyroidKeeper还实现了几个创新点。首先，它可以根据患者的病史、实验室指标、身体健康状况和当前用药情况，在患者康复期间自动调整用药剂量。其次，它可以利用图神经网络，根据患者的健康状况和相似群体的健康状况，全面预测可能出现的并发症。最后，图神经网络模型的应用可以提高医患在线沟通的效率，帮助医生更快、更准确地获取患者的医疗信息，从而做出更准确的诊断。在实验室和实际环境中的初步评估都显示了所提出的ThyroidKeeper系统的优势。
