Electronic health record (EHR) management systems require the adoption of effective technologies when health information is being exchanged.
Cryptography and Security
-Interpretation:The representations learned by deep learning methods should align with medical knowledge.
Inspired by the recent success of transformers in Natural Language Processing (NLP) in long-range sequence learning, we reformulate the task of volumetric (3D) medical image segmentation as a sequence-to-sequence prediction problem.
Ranked #1 on Medical Image Segmentation on Synapse multi-organ CT
RETAIN was tested on a large health system EHR dataset with 14 million visits completed by 263K patients over an 8 year period and demonstrated predictive accuracy and computational scalability comparable to state-of-the-art methods such as RNN, and ease of interpretability comparable to traditional models.
Ranked #2 on Disease Trajectory Forecasting on UK CF trust
Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications.
Deep learning models exhibit state-of-the-art performance for many predictive healthcare tasks using electronic health records (EHR) data, but these models typically require training data volume that exceeds the capacity of most healthcare systems.
As the only open-source software in Germany that explicitly addresses the stringent use and analysis of health data, Conquery is of great value to the healthcare community.
Reinforcement Learning (RL) has recently been applied to sequential estimation and prediction problems identifying and developing hypothetical treatment strategies for septic patients, with a particular focus on offline learning with observational data.
To demonstrate the model fidelity, we show that CorGAN generates synthetic data with performance similar to that of real data in various Machine Learning settings such as classification and prediction.
Ranked #1 on Synthetic Data Generation on UCI Epileptic Seizure Recognition (using extra training data)