DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records.
DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records.
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DOI：
10.1158/0008-5472.can-17-0615
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发表时间：
2017-11-01
期刊：
影响因子：
11.2
通讯作者：
Jacobson RS
中科院分区：
文献类型：
--
作者：
Savova GK;Tseytlin E;Finan S;Castine M;Miller T;Medvedeva O;Harris D;Hochheiser H;Lin C;Chavan G;Jacobson RS
需要精确的表型信息来了解遗传和表观遗传变化对肿瘤行为和反应性的影响。癌症表型的提取和表示目前主要是手动进行的，使得难以将表型数据与基因组数据相关联。此外，基因组数据的产生速度越来越快，加剧了这一问题。DeepPhe软件能够从癌症患者的电子病历中自动提取详细的表型信息。该系统在灵活的模块化架构中实现了先进的自然语言处理和知识工程方法，并使用匹兹堡大学医学中心（UPMC）乳腺癌患者的手动注释数据集进行了评估。由此产生的平台提供了计算表型的关键和缺失的计算方法。这些方法与高通量测序的先进分析相结合，将进一步加速向精确癌症治疗的过渡。
Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually making it difficult to correlate phenotypic data to genomic data. In addition, genomic data is being produced at an increasingly faster pace, exacerbating the problem. The DeepPhe software enables automated extraction of detailed phenotype information from Electronic Medical Records of cancer patients. The system implements advanced Natural Language Processing and knowledge engineering methods within a flexible modular architecture, and was evaluated using a manually-annotated dataset of the University of Pittsburgh Medical Center (UPMC) breast cancer patients. The resulting platform provides critical and missing computational methods for computational phenotyping. Working in tandem with advanced analysis of high-throughput sequencing, these approaches will further accelerate the transition to precision cancer treatment.