AIMC Topic: Neural Networks, Computer

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Does BERT need domain adaptation for clinical negation detection?

Journal of the American Medical Informatics Association : JAMIA
INTRODUCTION: Classifying whether concepts in an unstructured clinical text are negated is an important unsolved task. New domain adaptation and transfer learning methods can potentially address this issue.

Classification of Current Procedural Terminology Codes from Electronic Health Record Data Using Machine Learning.

Anesthesiology
BACKGROUND: Accurate anesthesiology procedure code data are essential to quality improvement, research, and reimbursement tasks within anesthesiology practices. Advanced data science techniques, including machine learning and natural language process...

Depression screening using mobile phone usage metadata: a machine learning approach.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Depression is currently the second most significant contributor to non-fatal disease burdens globally. While it is treatable, depression remains undiagnosed in many cases. As mobile phones have now become an integral part of daily life, th...

Using deep learning to associate human genes with age-related diseases.

Bioinformatics (Oxford, England)
MOTIVATION: One way to identify genes possibly associated with ageing is to build a classification model (from the machine learning field) capable of classifying genes as associated with multiple age-related diseases. To build this model, we use a pr...

ImPLoc: a multi-instance deep learning model for the prediction of protein subcellular localization based on immunohistochemistry images.

Bioinformatics (Oxford, England)
MOTIVATION: The tissue atlas of the human protein atlas (HPA) houses immunohistochemistry (IHC) images visualizing the protein distribution from the tissue level down to the cell level, which provide an important resource to study human spatial prote...

Prediction of mutation effects using a deep temporal convolutional network.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate prediction of the effects of genetic variation is a major goal in biological research. Towards this goal, numerous machine learning models have been developed to learn information from evolutionary sequence data. The most effecti...

Protein docking model evaluation by 3D deep convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Many important cellular processes involve physical interactions of proteins. Therefore, determining protein quaternary structures provide critical insights for understanding molecular mechanisms of functions of the complexes. To complemen...

DeepMSA: constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins.

Bioinformatics (Oxford, England)
MOTIVATION: The success of genome sequencing techniques has resulted in rapid explosion of protein sequences. Collections of multiple homologous sequences can provide critical information to the modeling of structure and function of unknown proteins....

Performance of a Deep Neural Network Algorithm Based on a Small Medical Image Dataset: Incremental Impact of 3D-to-2D Reformation Combined with Novel Data Augmentation, Photometric Conversion, or Transfer Learning.

Journal of digital imaging
Collecting and curating large medical-image datasets for deep neural network (DNN) algorithm development is typically difficult and resource-intensive. While transfer learning (TL) decreases reliance on large data collections, current TL implementati...

A Holistically-Nested U-Net: Surgical Instrument Segmentation Based on Convolutional Neural Network.

Journal of digital imaging
Surgical instrument segmentation is an essential task in the domain of computer-assisted surgical system. It is critical to increase the context-awareness of surgeons during the operation. We propose a new model based on the U-Net architecture for su...