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Pulmonary Embolism

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Autonomous detection, grading, and reporting of postoperative complications using natural language processing.

Surgery
INTRODUCTION: Natural language processing, a computer science technique that allows interpretation of narrative text, is infrequently used to identify surgical complications. We designed a natural language processing algorithm to identify and grade t...

Evaluating Report Text Variation and Informativeness: Natural Language Processing of CT Chest Imaging for Pulmonary Embolism.

Journal of the American College of Radiology : JACR
OBJECTIVE: The aim of this study was to quantify the variability of language in free text reports of pulmonary embolus (PE) studies and to gauge the informativeness of free text to predict PE diagnosis using machine learning as proxy for human unders...

Radiology report annotation using intelligent word embeddings: Applied to multi-institutional chest CT cohort.

Journal of biomedical informatics
We proposed an unsupervised hybrid method - Intelligent Word Embedding (IWE) that combines neural embedding method with a semantic dictionary mapping technique for creating a dense vector representation of unstructured radiology reports. We applied I...

Deep Learning to Classify Radiology Free-Text Reports.

Radiology
Purpose To evaluate the performance of a deep learning convolutional neural network (CNN) model compared with a traditional natural language processing (NLP) model in extracting pulmonary embolism (PE) findings from thoracic computed tomography (CT) ...

The safety and efficacy of full- versus reduced-dose betrixaban in the Acute Medically Ill VTE (Venous Thromboembolism) Prevention With Extended-Duration Betrixaban (APEX) trial.

American heart journal
BACKGROUND: The APEX trial assessed the safety and efficacy of extended-duration thromboprophylaxis using betrixaban versus standard dosing of enoxaparin among hospitalized, acutely ill medical patients. The 80-mg betrixaban dose was halved to 40 mg ...

Mapping Phenotypic Information in Heterogeneous Textual Sources to a Domain-Specific Terminological Resource.

PloS one
Biomedical literature articles and narrative content from Electronic Health Records (EHRs) both constitute rich sources of disease-phenotype information. Phenotype concepts may be mentioned in text in multiple ways, using phrases with a variety of st...

Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

IEEE transactions on medical imaging
Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that ha...

Neural hypernetwork approach for pulmonary embolism diagnosis.

BMC research notes
BACKGROUND: Hypernetworks are based on topological simplicial complexes and generalize the concept of two-body relation to many-body relation. Furthermore, Hypernetworks provide a significant generalization of network theory, enabling the integration...

A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data.

Journal of the American Medical Informatics Association : JAMIA
BACKGROUND: Venous thromboembolisms (VTEs), which include deep vein thrombosis (DVT) and pulmonary embolism (PE), are associated with significant mortality, morbidity, and cost in hospitalized patients. To evaluate the success of preventive measures,...

[Pulmonary vascular interventions: innovating through adaptation and advancing through differentiation].

Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases
Pulmonary vascular intervention technology, with its minimally invasive and precise advantages, has been a groundbreaking advancement in the treatment of pulmonary vascular diseases. Techniques such as balloon pulmonary angioplasty (BPA), pulmonary a...