Machine Learning-Supported Efficient VTE Risk Assessment using Routinely Collected Electronic Health Record Data
Li, Z.; Yagis, E.; Riad, A.; Windrath-Carr, O.; Arribas, M.; Sodiq, T.; Goldsmith, K.; Glampson, B.; Flott, K.; Haji, G.; Khan, Z.; Baker, C.; Mayer, E. K.
Show abstract
Venous thromboembolism (VTE) is a leading cause of preventable inpatient mortality, while the real-world performance of mandated risk assessment and the potential for automating using electronic health record (EHR) data remain unclear. We analysed 577,904 admissions and 726,896 VTE assessment forms across five NHS hospitals between 2015 and 2025 to evaluate assessment completion, concordance with structured EHR data, clinical validity, and feasibility of EHR-based automation assisted by machine learning. Overall completion was high (96.7%), and timely completion improved from 47.4% in 2015 to 90.5% in 2024. Agreement between forms and EHR data was good for common risk factors, but low-prevalence variables were often under-documented in the forms. Despite these discrepancies, form-derived thrombosis risk was associated with increased VTE incidence (OR 3.31, 95% CI 2.81-3.90). Machine learning models using first-14-hour EHR data achieved discrimination comparable to clinician-recorded variables (AUROC 0.709 vs 0.704), supporting real-time EHR-integrated assessment pre-population and decision support.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Predicting critical state after COVID-19 diagnosis: Model development using a large US electronic health record dataset 91%
- Non-invasive Diagnosis of Deep Vein Thrombosis from Ultrasound with Machine Learning 91%
- Zero-shot Interpretable Phenotyping of Postpartum Hemorrhage Using Large Language Models 91%
Similar papers in this journal
- Early initiation of prophylactic anticoagulation for prevention of COVID-19 mortality: a nationwide cohort study of hospitalized patients in the United States 92%
- Features of 16,749 hospitalised UK patients with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol 91%
- Clinical Decision Support in Cardiovascular Medicine: Effectiveness, Implementation Barriers, and Regulation 91%
Similar papers in this journal
- Use of unstructured text in prognostic clinical prediction models: a systematic review 92%
- Assessing the quality of clinical and administrative data extracted from hospitals: The General Medicine Inpatient Initiative (GEMINI) experience 92%
- Clinical Utility of Automatable Prediction Models for Improving Palliative and End-Of-Life Care Outcomes: Towards Routine Decision Analysis Before Implementation 92%
Similar papers in this journal
- Electronic prescribing systems as tools to improve patient care: a learning health systems approach to increase guideline concordant prescribing for venous thromboembolism prevention 93%
- An Informatics Consult approach for generating clinical evidence for treatment decisions 92%
- Development of a data-driven COVID-19 prognostication tool to inform triage and step-down care for hospitalised patients in Hong Kong: A population based cohort study 91%
Similar papers in this journal
- Real-world evaluation of AI-driven COVID-19 triage for emergency admissions: External validation & operational assessment of lab-free and high-throughput screening solutions 92%
- Remote Covid Assessment in Primary Care (RECAP) risk prediction tool: derivation and real-world validation studies 92%
- An external validation of the QCovid risk prediction algorithm for risk of mortality from COVID-19 in adults: national validation cohort study in England 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.