Text mining of electronic health records can validate a register‐based diagnosis of epilepsy and subgroup into focal and generalized epilepsy.
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| Title: | Text mining of electronic health records can validate a register‐based diagnosis of epilepsy and subgroup into focal and generalized epilepsy. |
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| Authors: | Vulpius, Siri A. (AUTHOR), Werge, Sebastian (AUTHOR), Jørgensen, Isabella Friis (AUTHOR), Siggaard, Troels (AUTHOR), Hernansanz Biel, Jorge (AUTHOR), Knudsen, Gitte M. (AUTHOR), Brunak, Søren (AUTHOR), Pinborg, Lars H. (AUTHOR) |
| Source: | Epilepsia (Series 4). Oct2023, Vol. 64 Issue 10, p2750-2760. 11p. |
| Subjects: | Partial epilepsy, Electronic health records, Epilepsy, Text mining, Diagnosis of epilepsy, Nosology |
| Abstract: | Objective: Combining population‐based health registries and electronic health records offers the opportunity to create large, phenotypically detailed patient cohorts of high quality. In this study, we used text mining of clinical notes to confirm International Classification of Diseases, 10th Revision (ICD‐10)‐registered epilepsy diagnoses and classify patients according to focal and generalized epilepsy types. Methods: Using the Danish National Patient Registry, we identified patients who between 2006 and 2016 received an ICD‐10 diagnosis of epilepsy. To validate the epilepsy diagnosis and stratify patients into focal and generalized epilepsy types, we constructed dictionaries for text mining‐based extraction of clinical notes. Two physicians manually reviewed the clinical notes for a total of 527 patients and assigned epilepsy diagnoses, which were compared with the text‐mined diagnoses. Results: We identified 23 632 patients with an ICD‐10 diagnosis of epilepsy, of whom 50% were registered with an unspecified epilepsy diagnosis. In total, 11 211 patients were considered likely to have epilepsy by text mining, with an F1 measure ranging from 82% to 90%. Manual review of the electronic health records for 310 patients revealed a false discovery rate of 29%. This rate was decreased to 4% by the text mining algorithm. The weighted average F1 measure for text mining‐assigned epilepsy types was 79% (82% for focal and 76% for generalized epilepsy). Text mining successfully assigned a focal or generalized epilepsy type to 92% of the text mining‐eligible patients registered with unspecified epilepsy. Significance: Text mining of electronic health records can be used to establish a patient cohort with much higher likelihood of having a diagnosis of epilepsy and a focal or generalized epilepsy type compared to the cohort created from ICD‐10 epilepsy codes alone. We believe the concept will be essential for future genome‐wide and phenome‐wide association studies and subsequently the development of precision medicine for epilepsy patients. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | Objective: Combining population‐based health registries and electronic health records offers the opportunity to create large, phenotypically detailed patient cohorts of high quality. In this study, we used text mining of clinical notes to confirm International Classification of Diseases, 10th Revision (ICD‐10)‐registered epilepsy diagnoses and classify patients according to focal and generalized epilepsy types. Methods: Using the Danish National Patient Registry, we identified patients who between 2006 and 2016 received an ICD‐10 diagnosis of epilepsy. To validate the epilepsy diagnosis and stratify patients into focal and generalized epilepsy types, we constructed dictionaries for text mining‐based extraction of clinical notes. Two physicians manually reviewed the clinical notes for a total of 527 patients and assigned epilepsy diagnoses, which were compared with the text‐mined diagnoses. Results: We identified 23 632 patients with an ICD‐10 diagnosis of epilepsy, of whom 50% were registered with an unspecified epilepsy diagnosis. In total, 11 211 patients were considered likely to have epilepsy by text mining, with an F1 measure ranging from 82% to 90%. Manual review of the electronic health records for 310 patients revealed a false discovery rate of 29%. This rate was decreased to 4% by the text mining algorithm. The weighted average F1 measure for text mining‐assigned epilepsy types was 79% (82% for focal and 76% for generalized epilepsy). Text mining successfully assigned a focal or generalized epilepsy type to 92% of the text mining‐eligible patients registered with unspecified epilepsy. Significance: Text mining of electronic health records can be used to establish a patient cohort with much higher likelihood of having a diagnosis of epilepsy and a focal or generalized epilepsy type compared to the cohort created from ICD‐10 epilepsy codes alone. We believe the concept will be essential for future genome‐wide and phenome‐wide association studies and subsequently the development of precision medicine for epilepsy patients. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00139580 |
| DOI: | 10.1111/epi.17734 |
