Mining incomplete clinical data for the early assessment of Kawasaki disease based on feature clustering and convolutional neural networks

作者：

Highlights：

• We investigated data-driven approaches for the early assessment of Kawasaki disease.

• We developed a method to tackle the incompleteness problem of clinical data associated with group-based missing patterns.

• We demonstrated the superior performance of the proposed method under incomplete data settings using a real-world dataset.

摘要

•We investigated data-driven approaches for the early assessment of Kawasaki disease.•We developed a method to tackle the incompleteness problem of clinical data associated with group-based missing patterns.•We demonstrated the superior performance of the proposed method under incomplete data settings using a real-world dataset.

论文关键词：Electronic health records,Clinical data mining,Medical decision making,Convolutional neural networks

论文评审过程：Received 22 August 2019, Revised 26 February 2020, Accepted 3 April 2020, Available online 3 May 2020, Version of Record 13 May 2020.

论文官网地址：https://doi.org/10.1016/j.artmed.2020.101859