AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
May 17, 2021
The wide adoption of Electronic Health Records (EHR) has resulted in large amounts of clinical data becoming available, which promises to support service delivery and advance clinical and informatics research. Deep learning techniques have demonstrat...
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
May 17, 2021
Adverse drug events (ADEs) are unexpected incidents caused by the administration of a drug or medication. To identify and extract these events, we require information about not just the drug itself but attributes describing the drug (e.g., strength, ...
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
May 17, 2021
HL7 Fast Healthcare Interoperability Resources (FHIR) is one of the current data standards for enabling electronic healthcare information exchange. Previous studies have shown that FHIR is capable of modeling both structured and unstructured data fro...
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
May 17, 2021
Deep learning models in healthcare may fail to generalize on data from unseen corpora. Additionally, no quantitative metric exists to tell how existing models will perform on new data. Previous studies demonstrated that NLP models of medical notes ge...
Computational and mathematical methods in medicine
May 17, 2021
Semantic segmentation plays a crucial role in cardiac magnetic resonance (MR) image analysis. Although supervised deep learning methods have made significant performance improvements, they highly rely on a large amount of pixel-wise annotated data, w...
Non-segmented MRI brain images are used for the identification of new Magnetic Resonance Imaging (MRI) biomarkers able to differentiate between schizophrenic patients (SCZ), major depressive patients (MD) and healthy controls (HC). Brain texture meas...
Advancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resul...
OBJECTIVES: To develop and validate a deep learning algorithm to automatically detect and segment an orbital abscess depicted on computed tomography (CT).
PURPOSE: To meet the demands imposed by the continuing growth of the Age-related macular degeneration (AMD) patient population, automation of follow-ups by detecting retinal oedema using deep learning might be a viable approach. However, preparing an...
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