BACKGROUND: Endobronchial ultrasound-guided transbronchial needle injection (EBUS-TBNI) is a novel technique for treating peribronchial targets. The aim of this study was to evaluate preliminary feasibility of thrombolysis of pulmonary emboli via EBU...
PURPOSE: Little is known about the characteristics and impact of acute pulmonary embolism (PE) during episodes of asthma exacerbation. We aimed to characterize patients diagnosed with acute PE in the setting of asthma exacerbation, develop a predicti...
OBJECTIVES: To take advantage of the deep learning algorithms to detect and calculate clot burden of acute pulmonary embolism (APE) on computed tomographic pulmonary angiography (CTPA).
PURPOSE: To design and evaluate a self-trainable natural language processing (NLP)-based procedure to classify unstructured radiology reports. The method enabling the generation of curated datasets is exemplified on CT pulmonary angiogram (CTPA) repo...
Diagnosing pulmonary embolism (PE) and excluding disorders that may clinically and radiologically simulate PE poses a challenging task for both human and machine perception. In this paper, we propose a novel vessel-oriented image representation (VOIR...
IMPORTANCE: Pulmonary embolism (PE) is a life-threatening clinical problem, and computed tomographic imaging is the standard for diagnosis. Clinical decision support rules based on PE risk-scoring models have been developed to compute pretest probabi...
This paper explores cutting-edge deep learning methods for information extraction from medical imaging free text reports at a multi-institutional scale and compares them to the state-of-the-art domain-specific rule-based system - PEFinder and traditi...
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...
Journal of the American College of Radiology : JACR
Feb 13, 2018
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...
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...
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