Practical use case of natural language processing for observational clinical research data retrieval from electronic health records: AssistMED project
Cezary Maciejewski, Krzysztof Ozierański, Mikołaj Basza, Adam Barwiołek, Michalina Ciurla, Aleksandra Bożym, Maciej Janusz Krajsman, Piotr Lodziński, Grzegorz Opolski, Marcin Grabowski, Andrzej Cacko, Paweł Balsam · Polskie Archiwum Medycyny Wewnętrznej · 2024
INTRODUCTION: Electronic health records (EHRs) contain data valuable for clinical research. However, they are in textual format and require manual encoding to databases, which is a lengthy and costly process. Natural language processing (NLP) is a computational technique that allows for text analysis. OBJECTIVES: Our study aimed to demonstrate a practical use case of NLP for a large retrospective study cohort characterization and comparison with human retrieval. PATIENTS AND METHODS: Anonymized discharge documentation of 10 314 patients from a cardiology tertiary care department was analyzed for inclusion in the CRAFT registry (Multicenter Experience in Atrial Fibrillation Patients Treated with Oral Anticoagulants; NCT02987062). Extensive clinical characteristics regarding concomitant diseases, medications, daily drug dosages, and echocardiography were collected manually and through NLP. RESULTS: There were 3030 and 3029 patients identified by human and NLP‑based approaches, respectively, reflecting 99.93% accuracy of NLP in detecting AF. Comprehensive baseline patient characteristics by NLP was faster than human analysis (3 h and 15 min vs 71 h and 12 min). The calculated CHA2DS2VASc and HAS‑BLED scores based on both methods did not differ (human vs NLP; median [interquartile range], 3 [2-5] vs 3 [2-5]; P = 0.74 and 1 [1-2] vs 1 [1-2]; P = 0.63, respectively). For most data, an almost perfect agreement between NLP- and human-retrieved characteristics was found; daily dosage identification was the least accurate NLP feature. Similar conclusions on cohort characteristics would be made; however, daily dosage detection for some drug groups would require additional human validation in the NLP‑based cohort. CONCLUSIONS: NLP utilization in EHRs may accelerate data acquisition and provide accurate information for retrospective studies.