BACKGROUND: Word embeddings have been prevalently used in biomedical Natural Language Processing (NLP) applications due to the ability of the vector representations being able to capture useful semantic properties and linguistic relationships between...
OBJECTIVE: The objective of this paper was to identify health information technology (HIT) related events from patient safety event (PSE) report free-text descriptions. A difference-based scoring approach was used to prioritize and select model featu...
OBJECTIVES: To summarize recent research and present a selection of the best papers published in 2017 in the field of clinical Natural Language Processing (NLP).
With the era of big data, the utilization of machine learning algorithms in radiation oncology is rapidly growing with applications including: treatment response modeling, treatment planning, contouring, organ segmentation, image-guidance, motion tra...
The simultaneous administration of multiple drugs increases the probability of interaction among them, as one drug may affect the activities of others. This interaction among drugs may have a positive or negative impact on the therapeutic outcomes. T...
OBJECTIVE: Abbreviations sense disambiguation is a special case of word sense disambiguation. Machine learning methods based on neural networks showed promising results for word sense disambiguation (Festag and Spreckelsen, 2017) [1] and, here we ass...
BACKGROUND: This study demonstrates clinical named entity recognition (NER) methods on the clinical texts of rheumatism patients in South Korea. Despite the recent increase in the adoption rate of the electronic health record (EHR) system in global h...
IEEE journal of biomedical and health informatics
Jul 30, 2018
Text normalization into medical dictionaries is useful to support clinical tasks. A typical setting is pharmacovigilance (PV). The manual detection of suspected adverse drug reactions (ADRs) in narrative reports is time consuming and natural language...
IEEE journal of biomedical and health informatics
Jul 17, 2018
Analyzing patients' health data using machine learning techniques can improve both patient outcomes and hospital operations. However, heterogeneous patient data (e.g., vital signs) and inefficient feature learning methods affect the implementation of...
Attention Deficit Hyperactive Disorder (ADHD) is one of the most common diseases in school aged children. In this paper, we consider using fMRI data with classification techniques to aid the diagnosis of ADHD and propose a bi-objective ADHD classific...
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