Nicolas Garcelon1,2, Antoine Neuraz1,2, Vincent Benoit1, Rémi Salomon1,3, Anita Burgun2,4. 1. Institut Imagine, Paris Descartes Université Paris Descartes-Sorbonne Paris Cité, Paris, France. 2. INSERM, Centre de Recherche des Cordeliers, UMR 1138 Equipe 22, Université Paris Descartes, Sorbonne Paris Cité, Paris, France. 3. Service de Néphrologie Pédiatrique, Hôpital Necker-Enfants Malades, Assistance Publique -Hôpitaux de Paris (AP-HP), Université Paris Descartes, Sorbonne Paris Cité, France. 4. Hôpital Européen Georges Pompidou, Assistance Publique -Hôpitaux de Paris (AP-HP), Université Paris Descartes, Sorbonne Paris Cité, France.
Abstract
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). MATERIALS AND 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. CONCLUSION: The proposed method generates a highly accurate identification of cases from a CDW of rare disease EHRs.
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). MATERIALS AND 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. CONCLUSION: The proposed method generates a highly accurate identification of cases from a CDW of rare disease EHRs.
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