ArticleResearch and practice in thrombosis and haemostasis2021
Deep learning detects and visualizes bleeding events in electronic health records.
Article in Research and practice in thrombosis and haemostasis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- An End-to-End Deep Learning System for Gastrointestinal Bleeding Detection and Quantification in Wireless Capsule Endoscopy.Diagnostics (Basel, Switzerland) · 2026Article
- Differences in bleeding outcome capture between electronic health record review using natural language processing and ICD-10 coding in hospitalised children.Pediatric research · 2026Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Natural Language Processing andJMIR medical informatics · 2025Article
- Artificial intelligence in clinical thrombosis and hemostasis: A review.Research and practice in thrombosis and haemostasis · 2025Review
- Prediction model for major bleeding in anticoagulated patients with cancer-associated venous thromboembolism using machine learning and natural language processing.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Observational
- Artificial intelligence: revolutionizing robotic surgery: review.Annals of medicine and surgery (2012) · 2024Review
- Validity of Major Osteoporotic Fracture Diagnoses in the Danish National Patient Registry.Clinical epidemiology · 2024Article
- Deep Learning in Hematology: From Molecules to Patients.Clinical hematology international · 2024Review
- Doctors Identify Hemorrhage Better during Chart Review when Assisted by Artificial Intelligence.Applied clinical informatics · 2023Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBleeding is associated with a significantly increased morbidity and mortality. Bleeding events are often described in the unstructured text of electronic health records, which makes them difficult to identify by manual inspection.
objectivesTo develop a deep learning model that detects and visualizes bleeding events in electronic health records. PATIENTS/
methodsThree hundred electronic health records with
resultsOn a balanced test set of 1178 sentences, the best-performing deep learning model achieved a sensitivity of 0.90, specificity of 0.90, and negative predictive value of 0.90. On a test set consisting of 700 notes, of which 49 were positive for bleeding, the model achieved a note-level sensitivity of 1.00, specificity of 0.52, and negative predictive value of 1.00. By using a sentence-level model on a note level, the model can explain its predictions by visualizing the exact sentence in a note that contains information regarding bleeding. Moreover, we found that the model performed consistently well across different types of bleedings.
conclusionsA deep learning model can be used to detect and visualize bleeding events in the free text of electronic health records. The deep learning model can thus facilitate systematic assessment of bleeding risk, and thereby optimize patient care and safety.
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