Learning a Health Knowledge Graph from Electronic Medical Records.
Learning a Health Knowledge Graph from Electronic Medical Records.
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DOI：
10.1038/s41598-017-05778-z
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发表时间：
2017-07-20
影响因子：
4.6
通讯作者：
Sontag D
中科院分区：
文献类型：
--
作者：
Rotmensch M;Halpern Y;Tlimat A;Horng S;Sontag D
近年来，对医学和自我诊断症状检查器中的临床决策支持系统的需求大幅增加。现有平台依赖于通过劳动密集型过程手动编译或使用简单的成对统计自动导出的知识库。这项研究探索了一个自动化的过程，以学习高质量的知识库，直接从电子病历中链接疾病和症状。从273，174个去识别的患者记录中提取医学概念，并使用三种概率模型的最大似然估计来自动构建知识图：逻辑回归，朴素贝叶斯分类器和使用噪声OR门的贝叶斯网络。从学习的参数中得出疾病-症状关系图，并在获得许可的情况下，针对Google手动构建的知识图和专家医生的意见对构建的知识图进行评估和验证。我们的研究表明，直接和自动化建设高质量的健康知识图的医疗记录使用基本概念提取是可行的。噪声OR模型产生高质量的知识图，在临床评价中达到0.85的精确度和0.6的召回率。在评估框架中，噪声OR显著优于所有测试模型（p < 0.01）。
Demand for clinical decision support systems in medicine and self-diagnostic symptom checkers has substantially increased in recent years. Existing platforms rely on knowledge bases manually compiled through a labor-intensive process or automatically derived using simple pairwise statistics. This study explored an automated process to learn high quality knowledge bases linking diseases and symptoms directly from electronic medical records. Medical concepts were extracted from 273,174 de-identified patient records and maximum likelihood estimation of three probabilistic models was used to automatically construct knowledge graphs: logistic regression, naive Bayes classifier and a Bayesian network using noisy OR gates. A graph of disease-symptom relationships was elicited from the learned parameters and the constructed knowledge graphs were evaluated and validated, with permission, against Google’s manually-constructed knowledge graph and against expert physician opinions. Our study shows that direct and automated construction of high quality health knowledge graphs from medical records using rudimentary concept extraction is feasible. The noisy OR model produces a high quality knowledge graph reaching precision of 0.85 for a recall of 0.6 in the clinical evaluation. Noisy OR significantly outperforms all tested models across evaluation frameworks (p < 0.01).
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影响因子：
120.7
作者：
BARNETT, GO;CIMINO, JJ;HOFFER, EP
通讯作者：
HOFFER, EP
影响因子：
3.5
作者：
Neamatullah, Ishna;Douglass, Margaret M.;Clifford, Gari D.
通讯作者：
Clifford, Gari D.
DOI：
10.1136/jamia.1994.95236141
发表时间：
1994-01-01
影响因子：
6.4
作者：
MILLER, RA
通讯作者：
MILLER, RA
DOI：
10.1016/s0888-613x(01)00039-1
发表时间：
2001-08-01
影响因子：
3.9
作者：
Onisko, A;Druzdzel, MJ;Wasyluk, H
通讯作者：
Wasyluk, H
影响因子：
2
作者：
Hider, Philip N.;Griffin, Gemma;Coughlan, Edward
通讯作者：
Coughlan, Edward