Computers in biology and medicine · 2025
Natural language processing for identifying major bleeding risk in hospitalised medical patients.
СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод в очереди
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Alnor Anne Bryde, Lynggaard Rasmus Bank, Laursen Martin Sundahl, Vinholt Pernille Just
Аннотация
BACKGROUND: Major bleeding is a severe complication in critically ill medical patients, resulting in significant morbidity, mortality, and healthcare costs. This study aims to assess the incidence and risk factors for major bleeding in hospitalised medical patients using a Natural Language Processing (NLP) model.
METHODS: We conducted a retrospective, cross-sectional observational study using electronic health records of adult patients admitted through the Emergency Department at Odense University Hospital from January 2017 to December 2022. Major bleeding during admission was identified and validated using a natural language model, with events classified according to current guidelines. Risk factors, including demographics, comorbidities, and biochemical values at admission, were evaluated. Two risk assessment models (RAMs) were developed using Cox proportional hazards regression. Validation included, bootstrapping, K-fold cross validation, and cluster analyses.
RESULTS: Of the 46,439 eligible patients, 1246 (2.7 %) experienced major bleeding. Risk factors for major bleeding included older age, male sex, alcohol consumption, higher systolic blood pressure, lower haemoglobin, and higher creatinine. RAM 1, which included biochemical data and comorbidities, demonstrated robust predictive performance (Harrell's C-statistic = 0.726). RAM 2, a simplified model without comorbidities, maintained similar predictive accuracy (C-statistic = 0.721), indicating its potential utility in clinical settings with limited resources for detailed patient histories. Results were consistent throughout validation.
CONCLUSION: This study highlights the incidence and risk factors of major bleeding in medical patients, emphasizing the predictive value of routinely measured biochemical markers. Furthermore, it shows the applicability of NLP models in identifying bleeding episodes in EHR text.
METHODS: We conducted a retrospective, cross-sectional observational study using electronic health records of adult patients admitted through the Emergency Department at Odense University Hospital from January 2017 to December 2022. Major bleeding during admission was identified and validated using a natural language model, with events classified according to current guidelines. Risk factors, including demographics, comorbidities, and biochemical values at admission, were evaluated. Two risk assessment models (RAMs) were developed using Cox proportional hazards regression. Validation included, bootstrapping, K-fold cross validation, and cluster analyses.
RESULTS: Of the 46,439 eligible patients, 1246 (2.7 %) experienced major bleeding. Risk factors for major bleeding included older age, male sex, alcohol consumption, higher systolic blood pressure, lower haemoglobin, and higher creatinine. RAM 1, which included biochemical data and comorbidities, demonstrated robust predictive performance (Harrell's C-statistic = 0.726). RAM 2, a simplified model without comorbidities, maintained similar predictive accuracy (C-statistic = 0.721), indicating its potential utility in clinical settings with limited resources for detailed patient histories. Results were consistent throughout validation.
CONCLUSION: This study highlights the incidence and risk factors of major bleeding in medical patients, emphasizing the predictive value of routinely measured biochemical markers. Furthermore, it shows the applicability of NLP models in identifying bleeding episodes in EHR text.