在面向罕见病的临床数据仓库中使用相似性度量来寻找患者：仓库博士与针堆中的针。

Finding patients using similarity measures in a rare diseases-oriented clinical data warehouse: Dr. Warehouse and the needle in the needle stack.

作者信息

Garcelon Nicolas, Neuraz Antoine, Benoit Vincent, Salomon Rémi, Kracker Sven, Suarez Felipe, Bahi-Buisson Nadia, Hadj-Rabia Smail, Fischer Alain, Munnich Arnold, Burgun Anita

机构信息

Institut Imagine, Paris Descartes Université Paris Descartes-Sorbonne Paris Cité, Paris, France; INSERM, Institut Imagine, UMR 1163, Université Paris Descartes, Sorbonne Paris Cité, Paris, France; INSERM, Centre de Recherche des Cordeliers, UMR 1138 Equipe 22, Université Paris Descartes, Sorbonne Paris Cité, Paris, France.

INSERM, Centre de Recherche des Cordeliers, UMR 1138 Equipe 22, Université Paris Descartes, Sorbonne Paris Cité, Paris, France; Département d'informatique médicale, Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris (AP-HP), Université Paris Descartes, Sorbonne Paris Cité, France.

出版信息

J Biomed Inform. 2017 Sep;73:51-61. doi: 10.1016/j.jbi.2017.07.016. Epub 2017 Jul 25.

DOI:10.1016/j.jbi.2017.07.016

PMID:28754522

Abstract

OBJECTIVE

In the context of rare diseases, it may be helpful to detect patients with similar medical histories, diagnoses and outcomes from a large number of cases with automated methods. To reduce the time to find new cases, we developed a method to find similar patients given an index case leveraging data from the electronic health records.

MATERIALS AND METHODS

We used the clinical data warehouse of a children academic hospital in Paris, France (Necker-Enfants Malades), containing about 400,000 patients. Our model was based on a vector space model (VSM) to compute the similarity distance between an index patient and all the patients of the data warehouse. The dimensions of the VSM were built upon Unified Medical Language System concepts extracted from clinical narratives stored in the clinical data warehouse. The VSM was enhanced using three parameters: a pertinence score (TF-IDF of the concepts), the polarity of the concept (negated/not negated) and the minimum number of concepts in common. We evaluated this model by displaying the most similar patients for five different rare diseases: Lowe Syndrome (LOWE), Dystrophic Epidermolysis Bullosa (DEB), Activated PI3K delta Syndrome (APDS), Rett Syndrome (RETT) and Dowling Meara (EBS-DM), from the clinical data warehouse representing 18, 103, 21, 84 and 7 patients respectively.

RESULTS

The percentages of index patients returning at least one true positive similar patient in the Top30 similar patients were 94% for LOWE, 97% for DEB, 86% for APDS, 71% for EBS-DM and 99% for RETT. The mean number of patients with the exact same genetic diseases among the 30 returned patients was 51%.

CONCLUSION

This tool offers new perspectives in a translational context to identify patients for genetic research. Moreover, when new molecular bases are discovered, our strategy will help to identify additional eligible patients for genetic screening.

摘要

目的

在罕见病背景下，使用自动化方法从大量病例中检测具有相似病史、诊断和结局的患者可能会有所帮助。为了缩短寻找新病例的时间，我们开发了一种方法，利用电子健康记录中的数据，在给定索引病例的情况下找到相似患者。

材料与方法

我们使用了法国巴黎一家儿童学术医院（内克尔儿童医院）的临床数据仓库，其中包含约40万名患者。我们的模型基于向量空间模型（VSM）来计算索引患者与数据仓库中所有患者之间的相似性距离。VSM的维度基于从临床数据仓库中存储的临床叙述中提取的统一医学语言系统概念构建。通过三个参数增强VSM：相关性得分（概念的词频-逆文档频率）、概念的极性（否定/未否定）和共同概念的最小数量。我们通过展示来自临床数据仓库中分别代表18、103、21、84和7名患者的五种不同罕见病（洛氏综合征（LOWE）、营养不良性大疱性表皮松解症（DEB）、活化磷脂酰肌醇3激酶δ综合征（APDS）、雷特综合征（RETT）和道林·米拉（EBS-DM））的最相似患者来评估该模型。