Generating Accurate Electronic Health Assessment from Medical Graph.
Generating Accurate Electronic Health Assessment from Medical Graph.
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DOI：
10.18653/v1/2020.findings-emnlp.336
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发表时间：
2020-11
期刊：
影响因子：
--
通讯作者：
Yu H
中科院分区：
文献类型：
--
作者：
Yang Z;Yu H
人工智能的基本目标之一是建立基于计算机的专家系统。在病人就诊期间推断临床诊断以产生临床评估是建立医疗诊断系统的关键一步。以前的工作主要是基于医学领域的特定知识，或者是患者先前的诊断和临床接触。本文提出了一种新的临床评估自动生成模型(MCAG)。MCAG建立在创新的图形神经网络上，其中丰富的临床知识被整合到端到端的语料库学习系统中。我们对医生制定的金标准的评估结果表明，与竞争性基线模型相比，MCAG显著提高了BLEU和胭脂评分。此外，医生的评估表明，MCAG可以产生高质量的评估。
One of the fundamental goals of artificial intelligence is to build computer-based expert systems. Inferring clinical diagnoses to generate a clinical assessment during a patient encounter is a crucial step towards building a medical diagnostic system. Previous works were mainly based on either medical domain-specific knowledge, or patients’ prior diagnoses and clinical encounters. In this paper, we propose a novel model for automated clinical assessment generation (MCAG). MCAG is built on an innovative graph neural network, where rich clinical knowledge is incorporated into an end-to-end corpus-learning system. Our evaluation results against physician generated gold standard show that MCAG significantly improves the BLEU and rouge score compared with competitive baseline models. Further, physicians’ evaluation showed that MCAG could generate high-quality assessments.