Abstract
For a long time, cervical spondylotic myelopathy has a high incidence in middle-aged and elderly people. In reality, the diagnosis of cervical spondylotic myelopathy by spine surgeons is a comprehensive process of aggregating information from multiple clinical data sources, which requires a comprehensive consideration based on the multi-source data. This process requires extensive experience and years of study by spine surgeons. The artificial intelligence method proposed in this work can greatly speed up this learning process. The proposed method comprehensively analyzes multi-source data in the patients’ electronic medical records, and provides diagnostic predictions to assist spine surgeons in efficient diagnosis. More importantly, the impact of different data sources on the diagnostic results is analyzed in depth.
Supported in part by the National Natural Science Foundation of China under Grant 61802130 and Grant 81802217, in part by the Guangdong Natural Science Foundation under Grant 2021A1515012651, Grant 2019A1515012152 and Grant 2019A1515010754, and in part by the Guangzhou Science and Technology Program Key Projects under Grant 2021053000053. (Corresponding author: Junying Chen.)
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Zheng, S. et al. (2022). Diagnostic Prediction for Cervical Spondylotic Myelopathy Based on Multi-source Data in Electronic Medical Records. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_41
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