Next generation phenotyping using narrative reports in a rare disease clinical data warehouse.
Next generation phenotyping using narrative reports in a rare disease clinical data warehouse.
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DOI：
10.1186/s13023-018-0830-6
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发表时间：
2018-05-31
影响因子：
3.7
通讯作者：
Rance B
中科院分区：
文献类型：
--
作者：
Garcelon N;Neuraz A;Salomon R;Bahi-Buisson N;Amiel J;Picard C;Mahlaoui N;Benoit V;Burgun A;Rance B
电子健康记录中收集的数据的二次使用为增加我们对罕见疾病的了解开辟了前景。Necker-Enfants Malades儿童医院的临床数据仓库（名为Dr. Warehouse）包含了数千名患者在正常护理过程中收集的数据。Warehouse博士致力于临床叙述的探索。在这项研究中，我们提出了我们的方法来寻找与感兴趣的疾病相关的表型。我们利用频率和TF-IDF来探索临床表型与罕见病之间的关联。我们在六个用例中应用了我们的方法：与Rett，Lowe，银Russell，Bardet-Biedl综合征，DOCK 8缺陷和活化PI 3-激酶δ综合征（APDS）相关的表型。我们要求领域专家评估Warehouse博士确定的前50个（频率和TF-IDF）表型的相关性，并计算平均精度和平均平均精度。专家们得出结论，在Warehouse博士发现的前50种表型中，有16到39种表型可以被认为是相关的。TF-IDF为11至41）。频率的平均精度范围为0.55至0.91，TF-IDF的平均精度范围为0.52至0.95。平均精密度为0.79。我们的研究表明，存储在电子健康记录中的临床叙述中识别的表型可以为罕见病专家提供除文献外还可以使用的候选表型。临床数据仓库可用于执行下一代表型分析，特别是在罕见疾病的背景下。我们已经开发了一种方法来检测与一组患者使用从自由文本临床叙述中提取的医学概念相关的表型。本文的在线版本（10.1186/s13023-018-0830-6）包含补充材料，可供授权用户使用。
Secondary use of data collected in Electronic Health Records opens perspectives for increasing our knowledge of rare diseases. The clinical data warehouse (named Dr. Warehouse) at the Necker-Enfants Malades Children’s Hospital contains data collected during normal care for thousands of patients. Dr. Warehouse is oriented toward the exploration of clinical narratives. In this study, we present our method to find phenotypes associated with diseases of interest. We leveraged the frequency and TF-IDF to explore the association between clinical phenotypes and rare diseases. We applied our method in six use cases: phenotypes associated with the Rett, Lowe, Silver Russell, Bardet-Biedl syndromes, DOCK8 deficiency and Activated PI3-kinase Delta Syndrome (APDS). We asked domain experts to evaluate the relevance of the top-50 (for frequency and TF-IDF) phenotypes identified by Dr. Warehouse and computed the average precision and mean average precision. Experts concluded that between 16 and 39 phenotypes could be considered as relevant in the top-50 phenotypes ranked by descending frequency discovered by Dr. Warehouse (resp. between 11 and 41 for TF-IDF). Average precision ranges from 0.55 to 0.91 for frequency and 0.52 to 0.95 for TF-IDF. Mean average precision was 0.79. Our study suggests that phenotypes identified in clinical narratives stored in Electronic Health Record can provide rare disease specialists with candidate phenotypes that can be used in addition to the literature. Clinical Data Warehouses can be used to perform Next Generation Phenotyping, especially in the context of rare diseases. We have developed a method to detect phenotypes associated with a group of patients using medical concepts extracted from free-text clinical narratives. The online version of this article (10.1186/s13023-018-0830-6) contains supplementary material, which is available to authorized users.
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影响因子：
4.6
作者：
Cortelazzo A;De Felice C;Guerranti R;Signorini C;Leoncini S;Pecorelli A;Zollo G;Landi C;Valacchi G;Ciccoli L;Bini L;Hayek J
通讯作者：
Hayek J
DOI：
10.1136/jamia.2009.001560
发表时间：
2010-09-01
影响因子：
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影响因子：
4.5
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影响因子：
4.5
作者：
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