Search Results for author:
Found 4 papers, 0 papers with code
一种非结构化数据表征增强的术后风险预测模型(An Unstructured Data Representation Enhanced Model for Postoperative Risk Prediction)
“准确的术后风险预测对临床资源规划和应急方案准备以及降低患者的术后风险和死亡率具有积极作用。术后风险预测目前主要基于术前和术中的患者基本信息、实验室检查、生命体征等结构化数据, 而蕴含丰富语义信息的非结构化术前诊断的价值还有待验证。针对该问题, 本文提出一种非结构化数据表征增强的术后风险预测模型, 利用自注意力机制, 精巧的将结构化数据与术前诊断数据进行信息加权融合。基于临床数据, 将本文方法与术后风险预测常用的统计机器学习模型以及最新的深度神经网络进行对比, 本文方法不仅提升了术后风险预测的性能, 同时也为预测模型带来了良好的可解释性。”
On Learning Better Embeddings from Chinese Clinical Records: Study on Combining In-Domain and Out-Domain Data
In recent years, a surge of interest on how to learn good embeddings and evaluate embedding quality based on English medical text has become increasing evident, however a limited number of studies based on Chinese medical text, particularly Chinese clinical records, were performed.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
