Improving a full-text search engine: the importance of negation detection and family history context to identify cases in a biomedical data warehouse.
OBJECTIVE
The repurposing of electronic health records (EHRs) can improve clinical and genetic research for rare diseases. However, significant information in rare disease EHRs is embedded in the narrative reports, which contain many negated clinical signs and family medical history. This paper presents a method to detect family history and negation in narrative reports and evaluates its impact on selecting populations from a clinical data warehouse (CDW).


METHODS
We developed a pipeline to process 1.6 million reports from multiple sources. This pipeline is part of the load process of the Necker Hospital CDW.


RESULTS
We identified patients with "Lupus and diarrhea," "Crohn's and diabetes," and "NPHP1" from the CDW. The overall precision, recall, specificity, and F-measure were 0.85, 0.98, 0.93, and 0.91, respectively.


CONCLUSIONS
The proposed method generates a highly accurate identification of cases from a CDW of rare disease EHRs.
Institut Imagine, Paris Descartes 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.
Nicolas
N
Garcelon
Institut Imagine, Paris Descartes 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.
Antoine
A
Neuraz
Institut Imagine, Paris Descartes Université Paris Descartes-Sorbonne Paris Cité, Paris, France.
Vincent
V
Benoit
Institut Imagine, Paris Descartes Université Paris Descartes-Sorbonne Paris Cité, Paris, France.|Service de Néphrologie Pédiatrique, Hôpital Necker-Enfants Malades, Assistance Publique -Hôpitaux de Paris (AP-HP), Université Paris Descartes, Sorbonne Paris Cité, France.
Rémi
R
Salomon
INSERM, Centre de Recherche des Cordeliers, UMR 1138 Equipe 22, Université Paris Descartes, Sorbonne Paris Cité, Paris, France.|Hôpital Européen Georges Pompidou, Assistance Publique -Hôpitaux de Paris (AP-HP), Université Paris Descartes, Sorbonne Paris Cité, France.
Anita
A
Burgun
D000073458:Data Warehousing; D057286:Electronic Health Records; D005192:Family Health; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008487:Medical History Taking; D009323:Natural Language Processing; D035583:Rare Diseases; D057386:Search Engine / Q000379:methods
data warehouse; electronic health records; natural language processing; rare diseases; search engine