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BACKGROUND: Natural language processing (NLP) is increasingly being used to extract structured information from unstructured text to assist clinical decision-making and aid healthcare research. The availability of expert-annotated documents for the development and validation of NLP applications is limited. We created synthetic clinical documents to address this, and to validate the Extraction of Epilepsy Clinical Text version 2 (ExECTv2) NLP pipeline. METHODS: We created 200 synthetic clinic letters based on hospital outpatient consultations with epilepsy specialists. The letters were double annotated by trained clinicians and researchers according to agreed guidelines. We used the annotation tool, Markup, with an epilepsy concept list based on the Unified Medical Language System ontology. All annotations were reviewed, and a gold standard set of annotations was agreed and used to validate the performance of ExECTv2. RESULTS: The overall inter-annotator agreement (IAA) between the two sets of annotations produced a per item F1 score of 0.73. Validating ExECTv2 using the gold standard gave an overall F1 score of 0.87 per item, and 0.90 per letter. CONCLUSION: The synthetic letters, annotations, and annotation guidelines have been made freely available. To our knowledge, this is the first publicly available set of annotated epilepsy clinic letters and guidelines that can be used for NLP researchers with minimum epilepsy knowledge. The IAA results show that clinical text annotation tasks are difficult and require a gold standard to be arranged by researcher consensus. The results for ExECTv2, our automated epilepsy NLP pipeline, extracted detailed epilepsy information from unstructured epilepsy letters with more accuracy than human annotators, further confirming the utility of NLP for clinical and research applications.
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Epilepsia , Procesamiento de Lenguaje Natural , Humanos , Curaduría de Datos/métodosRESUMEN
PURPOSE: Anonymised, routinely-collected healthcare data is increasingly being used for epilepsy research. We validated algorithms using general practitioner (GP) primary healthcare records to identify people with epilepsy from anonymised healthcare data within the Secure Anonymised Information Linkage (SAIL) databank in Wales, UK. METHOD: A reference population of 150 people with definite epilepsy and 150 people without epilepsy was ascertained from hospital records and linked to records contained within SAIL (containing GP records for 2.4 million people). We used three different algorithms, using combinations of GP epilepsy diagnosis and anti-epileptic drug (AED) prescription codes, to identify the reference population. RESULTS: Combining diagnosis and AED prescription codes had a sensitivity of 84% (95% ci 77-90) and specificity of 98% (95-100) in identifying people with epilepsy; diagnosis codes alone had a sensitivity of 86% (80-91) and a specificity of 97% (92-99); and AED prescription codes alone achieved a sensitivity of 92% (70-83) and a specificity of 73% (65-80). Using AED codes only was more accurate in children achieving a sensitivity of 88% (75-95) and specificity of 98% (88-100). CONCLUSION: GP epilepsy diagnosis and AED prescription codes can be confidently used to identify people with epilepsy using anonymised healthcare records in Wales, UK.
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Recolección de Datos/métodos , Epilepsia/diagnóstico , Epilepsia/epidemiología , Adulto , Algoritmos , Anticonvulsivantes/uso terapéutico , Niño , Registros Electrónicos de Salud/estadística & datos numéricos , Epilepsia/tratamiento farmacológico , Femenino , Humanos , Masculino , Reproducibilidad de los Resultados , Gales/epidemiologíaRESUMEN
AIM: To investigate the epidemiology, clinical profile and risk factors of pseudotumor cerebri syndrome (PTCS) in children aged 1-16â years. METHODS: A national prospective population-based cohort study over 25â months. Newly diagnosed PTCS cases notified via British Paediatric Surveillance Unit were ascertained using classical diagnostic criteria and categorised according to 2013 revised diagnostic criteria. We derived national age, sex and weight-specific annual incidence rates and assessed effects of sex and weight categories. RESULTS: We identified 185 PTCS cases of which 166 also fulfilled revised diagnostic criteria. The national annual incidence (95% CI) of PTCS in children aged 1-16â years was 0.71 (0.57 to 0.87) per 100â 000 population increasing with age and weight to 4.18 and 10.7 per 100â 000 in obese boys and girls aged 12-15â years, respectively. Incidence rates under 7â years were similar in both sexes. From 7â years onwards, the incidence in girls was double that in boys, but only in overweight (including obese) children. In children aged 12-15â years, an estimated 82% of the incidence of PTCS was attributable to obesity. Two subgroups of PTCS were apparent: 168 (91%) cases aged from 7â years frequently presented on medication and with headache and were predominantly female and obese. The remaining 17 (9%) cases under 7â years often lacked these risk factors and commonly presented with paralytic squint. CONCLUSIONS: This uniquely large population-based study of childhood PTCS will inform the design of future intervention studies. It suggests that weight reduction is central to the prevention of PTCS.