Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier
暂无分享,去创建一个
Katherine A. Heller | Joseph Futoma | Sanjay Hariharan | K. Heller | J. Futoma | S. Hariharan | Joseph D. Futoma
[1] T. Clemmer,et al. Sepsis syndrome: a valid clinical entity. Methylprednisolone Severe Sepsis Study Group. , 1989, Critical care medicine.
[2] T. Clemmer,et al. Sepsis syndrome: a valid clinical entity , 1989 .
[3] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[4] H. Wackernagle,et al. Multivariate geostatistics: an introduction with applications , 1998 .
[5] J. Vincent,et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure , 1996, Intensive Care Medicine.
[6] K. Wood,et al. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock* , 2006, Critical care medicine.
[7] J. Gardner-Thorpe,et al. The value of Modified Early Warning Score (MEWS) in surgical in-patients: a prospective observational study. , 2006, Annals of the Royal College of Surgeons of England.
[8] Edwin V. Bonilla,et al. Multi-task Gaussian Process Prediction , 2007, NIPS.
[9] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[10] W. Knaus,et al. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. 1992. , 2009, Chest.
[11] Alan E. Jones,et al. Lactate clearance vs central venous oxygen saturation as goals of early sepsis therapy: a randomized clinical trial. , 2010, JAMA.
[12] Philip S. Yu,et al. Early classification on time series , 2012, Knowledge and Information Systems.
[13] Gary B. Smith,et al. The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. , 2013, Resuscitation.
[14] Michael J. Rothman,et al. Development and validation of a continuous measure of patient condition using the Electronic Medical Record , 2013, J. Biomed. Informatics.
[15] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[16] Edmond Chow,et al. Preconditioned Krylov Subspace Methods for Sampling Multivariate Gaussian Distributions , 2014, SIAM J. Sci. Comput..
[17] Le Song,et al. Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression , 2015, NIPS.
[18] P. Pronovost,et al. A targeted real-time early warning score (TREWScore) for septic shock , 2015, Science Translational Medicine.
[19] David A. Clifton,et al. Multitask Gaussian Processes for Multivariate Physiological Time-Series Analysis , 2015, IEEE Transactions on Biomedical Engineering.
[20] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[21] Peter Szolovits,et al. A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data , 2015, AAAI.
[22] Suchi Saria,et al. A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure , 2015, NIPS.
[23] Adil Rafiq Rather,et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) , 2015 .
[24] R. Bellomo,et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). , 2016, JAMA.
[25] Charles Elkan,et al. Learning to Diagnose with LSTM Recurrent Neural Networks , 2015, ICLR.
[26] Mihaela van der Schaar,et al. A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics , 2016, NIPS.
[27] Katherine A. Heller,et al. Scalable Joint Modeling of Longitudinal and Point Process Data for Disease Trajectory Prediction and Improving Management of Chronic Kidney Disease , 2016, UAI.
[28] Benjamin M. Marlin,et al. A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification , 2016, NIPS.
[29] Mihaela van der Schaar,et al. ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission , 2016, ICML.
[30] S. Lemeshow,et al. Time to Treatment and Mortality during Mandated Emergency Care for Sepsis , 2017, The New England journal of medicine.
[31] Jimeng Sun,et al. Using recurrent neural network models for early detection of heart failure onset , 2016, J. Am. Medical Informatics Assoc..
[32] Yan Liu,et al. Recurrent Neural Networks for Multivariate Time Series with Missing Values , 2016, Scientific Reports.