MHM:基于多模式临床数据的分层多标签诊断预测

Zhi Qiao, Zhen Zhang, Xian Wu, Shen Ge, Wei Fan
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引用次数: 11

摘要

诊断预测的目的是预测患者下次就诊时可能出现的疾病,是临床决策支持系统(CDSS)的关键。现有的方法主要是将诊断预测作为一个多标签分类问题,并以离散医疗码为主要特征。而临床数据中医学代码和时间序列数据之间的结构信息往往被忽略。本文提出了基于多模态临床数据的分层多标签模型(MHM),将离散的医学编码、结构信息和时间序列数据整合到同一框架中进行诊断预测任务。在两个真实世界数据集上的实验结果表明,所提出的MHM优于最先进的方法。
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MHM: Multi-modal Clinical Data based Hierarchical Multi-label Diagnosis Prediction
Diagnosis prediction aims to forecast diseases that a patient might have in his next hospital visit, which is critical in Clinical Decision Supporting System (CDSS). Existing approaches mainly formulate diagnosis prediction as a multi-label classification problem and use discrete medical codes as major features. While the structural information among medical codes and time series data in clinical data are generally neglected. In this paper, we propose Multi-modal Clinical Data based Hierarchical Multi-label model (MHM) to integrate discrete medical codes, structural information and time series data into the same framework for diagnosis prediction task. Experimental results on two real world datasets demonstrate the superiority of proposed MHM over state-of-the-art approaches.
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