Clinical Phenotypic Spectrum of 4095 Individuals with Down Syndrome from Text Mining of Electronic Health Records.
Clinical Phenotypic Spectrum of 4095 Individuals with Down Syndrome from Text Mining of Electronic Health Records.
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来自电子健康记录的文本挖掘的4095个患者的临床表型光谱。
DOI：
10.3390/genes12081159
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发表时间：
2021-07-28
期刊：
影响因子：
3.5
通讯作者：
Wang K
中科院分区：
文献类型：
--
作者：
Havrilla JM;Zhao M;Liu C;Weng C;Helbig I;Bhoj E;Wang K
人类遗传性疾病，如唐氏综合征，具有各种各样的临床表型表现，并且表征每个细微差别的表型和亚型可能是困难的。在这项研究中，我们检查了费城儿童医院4095名唐氏综合征患者的电子健康记录，以创建一种数字化表征表型谱的方法。我们使用自然语言处理（NLP）方法MetaMap从质量过滤的患者笔记中提取人类表型本体（HPO）术语。我们对与唐氏综合征患者相关的最常见HPO术语进行了编目，并将这些术语与基线人群中的术语进行了比较。我们根据不同年龄的临床访视频率对前100个HPO术语进行了表征，并突出显示了具有时间依赖性分布的选定术语。我们还发现了与唐氏综合征没有显著相关的表型术语，如“眼球突出”、“下斜睑裂”和“小耳畸形”。总之，我们的研究表明，孟德尔疾病个体的临床表型谱可以通过基于NLP的人口规模电子健康记录（EHR）数字表型来表征。
Human genetic disorders, such as Down syndrome, have a wide variety of clinical phenotypic presentations, and characterizing each nuanced phenotype and subtype can be difficult. In this study, we examined the electronic health records of 4095 individuals with Down syndrome at the Children’s Hospital of Philadelphia to create a method to characterize the phenotypic spectrum digitally. We extracted Human Phenotype Ontology (HPO) terms from quality-filtered patient notes using a natural language processing (NLP) approach MetaMap. We catalogued the most common HPO terms related to Down syndrome patients and compared the terms with those from a baseline population. We characterized the top 100 HPO terms by their frequencies at different ages of clinical visits and highlighted selected terms that have time-dependent distributions. We also discovered phenotypic terms that have not been significantly associated with Down syndrome, such as “Proptosis”, “Downslanted palpebral fissures”, and “Microtia”. In summary, our study demonstrated that the clinical phenotypic spectrum of individual with Mendelian diseases can be characterized through NLP-based digital phenotyping on population-scale electronic health records (EHRs).
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影响因子：
12.3
作者：
Havrilla JM;Liu C;Dong X;Weng C;Wang K
通讯作者：
Wang K
影响因子：
2
作者：
Morris, Joan K.;Alberman, Eva;Jacobs, Patricia
通讯作者：
Jacobs, Patricia
影响因子：
14.9
作者：
Köhler S;Gargano M;Matentzoglu N;Carmody LC;Lewis-Smith D;Vasilevsky NA;Danis D;Balagura G;Baynam G;Brower AM;Callahan TJ;Chute CG;Est JL;Galer PD;Ganesan S;Griese M;Haimel M;Pazmandi J;Hanauer M;Harris NL;Hartnett MJ;Hastreiter M;Hauck F;He Y;Jeske T;Kearney H;Kindle G;Klein C;Knoflach K;Krause R;Lagorce D;McMurry JA;Miller JA;Munoz-Torres MC;Peters RL;Rapp CK;Rath AM;Rind SA;Rosenberg AZ;Segal MM;Seidel MG;Smedley D;Talmy T;Thomas Y;Wiafe SA;Xian J;Yüksel Z;Helbig I;Mungall CJ;Haendel MA;Robinson PN
通讯作者：
Robinson PN
DOI：
10.1038/s41436-020-0923-1
发表时间：
2020-12
期刊：
Genetics in medicine : official journal of the American College of Medical Genetics
影响因子：
--
作者：
Ganesan S;Galer PD;Helbig KL;McKeown SE;O'Brien M;Gonzalez AK;Felmeister AS;Khankhanian P;Ellis CA;Helbig I
通讯作者：
Helbig I
影响因子：
14.9
作者：
Liu, Cong;Kury, Fabricio Sampaio Peres;Weng, Chunhua
通讯作者：
Weng, Chunhua