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Found 19 papers, 10 papers with code
Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources
Scarcity of health care resources could result in the unavoidable consequence of rationing.
Exploring Large Language Models for Knowledge Graph Completion
Knowledge graphs play a vital role in numerous artificial intelligence tasks, yet they frequently face the issue of incompleteness.
AD-BERT: Using Pre-trained contextualized embeddings to Predict the Progression from Mild Cognitive Impairment to Alzheimer's Disease
Materials and Methods: We identified 3657 patients diagnosed with MCI together with their progress notes from Northwestern Medicine Enterprise Data Warehouse (NMEDW) between 2000-2020.
AKI-BERT: a Pre-trained Clinical Language Model for Early Prediction of Acute Kidney Injury
However, few have explored BERT on disease-specific medical domain tasks such as AKI early prediction.
Distribution Preserving Graph Representation Learning
Besides the generalizability, by applying an expressive GNN backbone, DP-GNN can also have high expressive power.
PANTHER: Pathway Augmented Nonnegative Tensor factorization for HighER-order feature learning
We apply genetically motivated constrained tensor factorization to group pathways in a way that reflects molecular mechanism interactions.
Towards Expressive Graph Representation
Graph Neural Network (GNN) aggregates the neighborhood of each node into the node embedding and shows its powerful capability for graph representation learning.
KG-BERT: BERT for Knowledge Graph Completion
Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness.
Ranked #5 on
Link Prediction
on UMLS
MedGCN: Medication recommendation and lab test imputation via graph convolutional networks
In this study, we construct a graph to associate 4 types of medical entities, i. e., patients, encounters, lab tests, and medications, and applied a graph neural network to learn node embeddings for medication recommendation and lab test imputation.
ImageGCN: Multi-Relational Image Graph Convolutional Networks for Disease Identification with Chest X-rays
However, most of the existing approaches for image representation ignore the relations between images and consider each input image independently.
Local Probabilistic Model for Bayesian Classification: a Generalized Local Classification Model
In this paper, with the insight that the distribution in a local sample space should be simpler than that in the whole sample space, a local probabilistic model established for a local region is expected much simpler and can relax the fundamental assumptions that may not be true in the whole sample space.
Local Distribution in Neighborhood for Classification
Additionally, based on the local distribution, we generate a generalized local classification form that can be effectively applied to various datasets through tuning the parameters.
Early Prediction of Acute Kidney Injury in Critical Care Setting Using Clinical Notes
We developed data-driven prediction models to estimate the risk of new AKI onset.
Supervised Nonnegative Matrix Factorization to Predict ICU Mortality Risk
We used the simulation data to verify the effectiveness of this method, and then we applied it to ICU mortality risk prediction and demonstrated its superiority over other conventional supervised NMF methods.
Deep Generative Classifiers for Thoracic Disease Diagnosis with Chest X-ray Images
However, most of the previous deep neural network classifiers were based on deterministic architectures which are usually very noise-sensitive and are likely to aggravate the overfitting issue.
Distribution Networks for Open Set Learning
In this paper, we recognize that novel classes should be different from each other, and propose distribution networks for open set learning that can model different novel classes based on probability distributions.
Graph Convolutional Networks for Text Classification
We build a single text graph for a corpus based on word co-occurrence and document word relations, then learn a Text Graph Convolutional Network (Text GCN) for the corpus.
Ranked #6 on
Text Classification
on Ohsumed
Developing a Portable Natural Language Processing Based Phenotyping System
Our system facilitates portable phenotyping of obesity and its 15 comorbidities based on the unstructured patient discharge summaries, while achieving a performance that often ranked among the top 10 of the challenge participants.
Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks
Clinical text classification is an important problem in medical natural language processing.
Ranked #2 on
Clinical Note Phenotyping
on I2B2 2008: Obesity
