A K Rauch, C Offergeld, Manuel Christoph Ketterer
[How to impart scientific knowledge and writing skills? Digital education during the COVID-19 pandemic using the example of otorhinolaryngology]
HNO
study aims use novel technology based natural language processing nlp extract clinical information electronic health records ehrs characterise clinical profile patients diagnosed spondyloarthritis spa largescale hospital observational retrospective analysis conducted ehr data patients spa including psoriatic arthritis psa hospital universitario la paz 2020 2022 data collected using savana manager nlpbased system enabling extraction information unstructured freetext ehrs variables analysed included demographic data spa subtypes comorbidities treatments performance technology detecting spa clinical entities evaluated precision recall f1 score metrics hospital population 639 474 patients 4337 07 patients diagnosis spa subtypes ehr population predominantly comprised men 553 mean age 509 years peripheral spa including psa reported 316 axial spa 209 axial peripheral spa 37 437 patients spa subtype reported common comorbidities included hypertension 250 dyslipidaemia 222 diabetes mellitus 155 use conventional diseasemodifying antirheumatic drugs csdmards biological dmards bdmards documented methotrexate 253 patients used csdmards adalimumab 106 patients used bdmard nlp technology demonstrated high precision recall assessed f1 score values 080 indicating reliable data extraction application nlp technology facilitated characterisation spa patient profile including demographics clinical features comorbidities treatments study supports utility nlp enhancing understanding spa suggests potential improving patient management extracting meaningful information unstructured ehr data