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Found 9 papers, 1 papers with code
Generation of a Compendium of Transcription Factor Cascades and Identification of Potential Therapeutic Targets using Graph Machine Learning
We constructed a knowledge graph of 81, 488 unique TF cascades, with the longest cascade consisting of 62 TFs.
Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs
Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life.
Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health
Human space exploration beyond low Earth orbit will involve missions of significant distance and duration.
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients
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.
Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records
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.
Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale
With these results, we demonstrate that ConvAE can generate patient representations that lead to clinically meaningful insights.
Federated Learning for Healthcare Informatics
With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies and pharmaceutical industries, among others.
Time Aggregation and Model Interpretation for Deep Multivariate Longitudinal Patient Outcome Forecasting Systems in Chronic Ambulatory Care
Clinical data for ambulatory care, which accounts for 90% of the nations healthcare spending, is characterized by relatively small sample sizes of longitudinal data, unequal spacing between visits for each patient, with unequal numbers of data points collected across patients.
Deep Learning Predicts Hip Fracture using Confounding Patient and Healthcare Variables
In this study, we trained deep learning models on 17, 587 radiographs to classify fracture, five patient traits, and 14 hospital process variables.
