AIMC Journal:
Journal of the American Medical Informatics Association : JAMIA
Showing 441 to 450 of 493 articles
Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review.
Journal of the American Medical Informatics Association : JAMIA
Apr 1, 2019
OBJECTIVE: Natural language processing (NLP) of symptoms from electronic health records (EHRs) could contribute to the advancement of symptom science. We aim to synthesize the literature on the use of NLP to process or analyze symptom information doc...
Spell checker for consumer language (CSpell).
Journal of the American Medical Informatics Association : JAMIA
Mar 1, 2019
OBJECTIVE: Automated understanding of consumer health inquiries might be hindered by misspellings. To detect and correct various types of spelling errors in consumer health questions, we developed a distributable spell-checking tool, CSpell, that han...
Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation.
Journal of the American Medical Informatics Association : JAMIA
Mar 1, 2019
OBJECTIVE: Alcohol misuse is present in over a quarter of trauma patients. Information in the clinical notes of the electronic health record of trauma patients may be used for phenotyping tasks with natural language processing (NLP) and supervised ma...
An expandable approach for design and personalization of digital, just-in-time adaptive interventions.
Journal of the American Medical Informatics Association : JAMIA
Mar 1, 2019
OBJECTIVE: We aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategi...
Classifying relations in clinical narratives using segment graph convolutional and recurrent neural networks (Seg-GCRNs).
Journal of the American Medical Informatics Association : JAMIA
Mar 1, 2019
We propose to use segment graph convolutional and recurrent neural networks (Seg-GCRNs), which use only word embedding and sentence syntactic dependencies, to classify relations from clinical notes without manual feature engineering. In this study, t...
Synthesizing electronic health records using improved generative adversarial networks.
Journal of the American Medical Informatics Association : JAMIA
Mar 1, 2019
OBJECTIVE: The aim of this study was to generate synthetic electronic health records (EHRs). The generated EHR data will be more realistic than those generated using the existing medical Generative Adversarial Network (medGAN) method.
Integrating ontologies of human diseases, phenotypes, and radiological diagnosis.
Journal of the American Medical Informatics Association : JAMIA
Feb 1, 2019
Mappings between ontologies enable reuse and interoperability of biomedical knowledge. The Radiology Gamuts Ontology (RGO)-an ontology of 16 918 diseases, interventions, and imaging observations-provides a resource for differential diagnosis and auto...
Automated and flexible identification of complex disease: building a model for systemic lupus erythematosus using noisy labeling.
Journal of the American Medical Informatics Association : JAMIA
Jan 1, 2019
UNLABELLED: Accurate and efficient identification of complex chronic conditions in the electronic health record (EHR) is an important but challenging task that has historically relied on tedious clinician review and oversimplification of the disease....
Expert-level sleep scoring with deep neural networks.
Journal of the American Medical Informatics Association : JAMIA
Dec 1, 2018
OBJECTIVES: Scoring laboratory polysomnography (PSG) data remains a manual task of visually annotating 3 primary categories: sleep stages, sleep disordered breathing, and limb movements. Attempts to automate this process have been hampered by the com...
Predicting individual physiologically acceptable states at discharge from a pediatric intensive care unit.
Journal of the American Medical Informatics Association : JAMIA
Dec 1, 2018
OBJECTIVE: Quantify physiologically acceptable PICU-discharge vital signs and develop machine learning models to predict these values for individual patients throughout their PICU episode.
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