The key knob of our framework is a unique positive sampling approach tailored for the medical images, by seamlessly integrating radiomic features as a knowledge augmentation.
Electronic Health Record (EHR) data has been of tremendous utility in Artificial Intelligence (AI) for healthcare such as predicting future clinical events.
Traditionally, radiomics, as a subfield of radiology that can extract a large number of quantitative features from medical images, demonstrates its potential to facilitate medical imaging diagnosis before the deep learning era.
no code implementations • 11 Jan 2021 • Tingyi Wanyan, Hossein Honarvar, Suraj K. Jaladanki, Chengxi Zang, Nidhi Naik, Sulaiman Somani, Jessica K. De Freitas, Ishan Paranjpe, Akhil Vaid, Riccardo Miotto, Girish N. Nadkarni, Marinka Zitnik, ArifulAzad, Fei Wang, Ying Ding, Benjamin S. Glicksberg
This has been a major issue for developing ML models for the coronavirus-disease 2019 (COVID-19) pandemic where data is highly imbalanced, particularly within electronic health records (EHR) research.
In this paper, we demonstrate that it is unnecessary for spare retraining to strictly inherit those properties from the dense network.
In this work, we train a Heterogeneous Graph Model (HGM) on Electronic Health Record data and use the resulting embedding vector as additional information added to a Convolutional Neural Network (CNN) model for predicting in-hospital mortality.
This paper analyzes team collaboration in the field of Artificial Intelligence (AI) from the perspective of geographic distance.
Here we propose using the latest graph representation learning and embedding models to refine and complete biomedical knowledge graphs.
After a number of iterations and with the help of radiomic features, our framework can converge to more accurate image regions.
The emergence of the novel COVID-19 pandemic has had a significant impact on global healthcare and the economy over the past few months.
COVID-19 resulted in an infodemic, which could erode public trust, impede virus containment, and outlive the pandemic itself.
Social and Information Networks Computers and Society
We present a multi-filtering Graph Convolution Neural Network (GCN) framework for network embedding task.
In this context, scientific writing increasingly plays an important role in scholars' scientific careers.
We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.
The observations suggest marginal differences between groups in syntactical and lexical complexity.
More importantly, as a model-based approach, DIMM-SC is able to quantify the clustering uncertainty for each single cell, facilitating rigorous statistical inference and biological interpretations, which are typically unavailable from existing clustering methods.
Moreover, by considering different linkage paths in the network, one can capture the subtlety of different types of dependencies among objects.