102 citations · 386 across the 8 of their papers we have counts for
9 papers
Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patients
Wei-Hung Weng, Mingwu Gao, Ze He +2
Glycemic control is essential for critical care. However, it is a challenging task because there has been no study on personalized optimal strategies for glycemic control. This wor…
Deep Reinforcement Learning for Sepsis Treatment
Aniruddh Raghu, Matthieu Komorowski, Imran Ahmed +3
Sepsis is a leading cause of mortality in intensive care units and costs hospitals billions annually. Treating a septic patient is highly challenging, because individual patients r…
Clinical Intervention Prediction and Understanding using Deep Networks
Harini Suresh, Nathan Hunt, Alistair Johnson +3
Real-time prediction of clinical interventions remains a challenge within intensive care units (ICUs). This task is complicated by data sources that are noisy, sparse, heterogeneou…
Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach
Aniruddh Raghu, Matthieu Komorowski, Leo Anthony Celi +2
Sepsis is a leading cause of mortality in intensive care units (ICUs) and costs hospitals billions annually. Treating a septic patient is highly challenging, because individual pat…
Transfer Learning for Named-Entity Recognition with Neural Networks
Ji Young Lee, Franck Dernoncourt, Peter Szolovits
Recent approaches based on artificial neural networks (ANNs) have shown promising results for named-entity recognition (NER). In order to achieve high performances, ANNs need to be…
NeuroNER: an easy-to-use program for named-entity recognition based on neural networks
Franck Dernoncourt, Ji Young Lee, Peter Szolovits
Named-entity recognition (NER) aims at identifying entities of interest in a text. Artificial neural networks (ANNs) have recently been shown to outperform existing NER systems. Ho…