AIMC Topic: Deep Learning

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Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Sepsis is the leading cause of mortality in the ICU. It is challenging to manage because individual patients respond differently to treatment. Thus, tailoring treatment to the individual patient is essential for the best outcomes. In this paper, we t...

A hybrid approach for automated mutation annotation of the extended human mutation landscape in scientific literature.

AMIA ... Annual Symposium proceedings. AMIA Symposium
As the cost of DNA sequencing continues to fall, an increasing amount of information on human genetic variation is being produced that could help progress precision medicine. However, information about such mutations is typically first made available...

Finding medication doses in the liteature.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Medication doses, one of the determining factors in medication safety and effectiveness, are present in the literature, but only in free-text form. We set out to determine if the systems developed for extracting drug prescription information from cli...

Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography.

Echocardiography (Mount Kisco, N.Y.)
BACKGROUND: Heart disease (HD) is the leading cause of global death; there are several mortality prediction models of HD for identifying critically-ill patients and for guiding decision making. The existing models, however, cannot be used during init...

Is Intensity Inhomogeneity Correction Useful for Classification of Breast Cancer in Sonograms Using Deep Neural Network?

Journal of healthcare engineering
The sonogram is currently an effective cancer screening and diagnosis way due to the convenience and harmlessness in humans. Traditionally, lesion boundary segmentation is first adopted and then classification is conducted, to reach the judgment of b...

Combining high-throughput imaging flow cytometry and deep learning for efficient species and life-cycle stage identification of phytoplankton.

BMC ecology
BACKGROUND: Phytoplankton species identification and counting is a crucial step of water quality assessment. Especially drinking water reservoirs, bathing and ballast water need to be regularly monitored for harmful species. In times of multiple envi...

BetaDL: A protein beta-sheet predictor utilizing a deep learning model and independent set solution.

Computers in biology and medicine
The sequence-based prediction of beta-residue contacts and beta-sheet structures contain key information for protein structure prediction. However, the determination of beta-sheet structures poses numerous challenges due to long-range beta-residue in...

Patch-based system for Classification of Breast Histology images using deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In this work, we proposed a patch-based classifier (PBC) using Convolutional neural network (CNN) for automatic classification of histopathological breast images. Presence of limited images necessitated extraction of patches and augmentation to boost...