AIMC Topic: Neural Networks, Computer

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Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans.

IEEE transactions on medical imaging
Pulmonary lobe segmentation in computed tomography scans is essential for regional assessment of pulmonary diseases. Recent works based on convolution neural networks have achieved good performance for this task. However, they are still limited in ca...

'I'm over the moon!': patient-perceived outcomes of hepatitis C treatment.

Australian journal of primary health
Understanding patient-perceived outcomes is crucial for assessing the effectiveness and acceptability of hepatitis C virus (HCV) treatment. This study aimed to explore patient-perceived outcomes of receiving direct-acting antivirals (DAAs). This stud...

Automatic Segmentation of Meniscus in Multispectral MRI Using Regions with Convolutional Neural Network (R-CNN).

Journal of digital imaging
The meniscus has a significant function in human anatomy, and Magnetic Resonance Imaging (MRI) has an essential role in meniscus examination. Due to a variety of MRI data, it is excessively difficult to segment the meniscus with image processing meth...

Machine-learning algorithms for predicting results in liver transplantation: the problem of donor-recipient matching.

Current opinion in organ transplantation
PURPOSE OF REVIEW: Classifiers based on artificial intelligence can be useful to solve decision problems related to the inclusion or removal of possible liver transplant candidates, and assisting in the heterogeneous field of donor-recipient (D-R) ma...

Artificial intelligence and organ transplantation: challenges and expectations.

Current opinion in organ transplantation
PURPOSE OF REVIEW: Classifiers based on artificial intelligence have emerged in all areas of medicine. Although very subtle, many decisions in organ transplantation can now be addressed in a more concisely manner with the support of these classifiers...

Constructing co-occurrence network embeddings to assist association extraction for COVID-19 and other coronavirus infectious diseases.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: As coronavirus disease 2019 (COVID-19) started its rapid emergence and gradually transformed into an unprecedented pandemic, the need for having a knowledge repository for the disease became crucial. To address this issue, a new COVID-19 m...

Artificial neural network and bioavailability of the immunosuppression drug.

Current opinion in organ transplantation
PURPOSE OF REVIEW: The success of organ transplant is determined by number of demographic, clinical, immunological and genetic variables. Artificial intelligence tools, such as artificial neural networks (ANNs) or classification and regression trees ...

An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: In an effort to improve the efficiency of computer algorithms applied to screening for coronavirus disease 2019 (COVID-19) testing, we used natural language processing and artificial intelligence-based methods with unstructured patient dat...

Dataset-aware multi-task learning approaches for biomedical named entity recognition.

Bioinformatics (Oxford, England)
MOTIVATION: Named entity recognition is a critical and fundamental task for biomedical text mining. Recently, researchers have focused on exploiting deep neural networks for biomedical named entity recognition (Bio-NER). The performance of deep neura...

A multimodal deep learning framework for predicting drug-drug interaction events.

Bioinformatics (Oxford, England)
MOTIVATION: Drug-drug interactions (DDIs) are one of the major concerns in pharmaceutical research. Many machine learning based methods have been proposed for the DDI prediction, but most of them predict whether two drugs interact or not. The studies...