Early prediction and monitoring of sepsis using sequential long short term memory model

[1]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[2]  R. Balk,et al.  SEVERE SEPSIS AND SEPTIC SHOCK , 2000 .

[3]  Mingzhe Jiang,et al.  Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach , 2018, Future Gener. Comput. Syst..

[4]  Rabindra K. Barik,et al.  FogLearn: Leveraging Fog-based Machine Learning for Smart System Big Data Analytics , 2017, Int. J. Fog Comput..

[5]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[6]  R. Bellomo,et al.  The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). , 2016, JAMA.

[7]  Saakshi Bhargava,et al.  Early detection and diagnosis using deep learning , 2021 .

[8]  M. Vollmer,et al.  Quality Improvement Initiative for Severe Sepsis and Septic Shock Reduces 90-Day Mortality: A 7.5-Year Observational Study* , 2017, Critical care medicine.

[9]  Prasad Calyam,et al.  Predictive analytics for fog computing using machine learning and GENI , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[10]  Rajkumar Buyya,et al.  Quality of Experience (QoE)-aware placement of applications in Fog computing environments , 2019, J. Parallel Distributed Comput..

[11]  Shamim Nemati,et al.  Early Prediction of Sepsis From Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019 , 2019, 2019 Computing in Cardiology (CinC).

[12]  Hagen Malberg,et al.  Predicting sepsis with a recurrent neural network using the MIMIC III database , 2019, Comput. Biol. Medicine.

[13]  Ioannis Chatzigiannakis,et al.  Design and Evaluation of a Person-Centric Heart Monitoring System over Fog Computing Infrastructure , 2017, HumanSys@SenSys.

[14]  Yu Xue,et al.  An Evolutionary Computation Based Feature Selection Method for Intrusion Detection , 2018, Secur. Commun. Networks.

[15]  Sandeep K. Sood,et al.  Fog Assisted-IoT Enabled Patient Health Monitoring in Smart Homes , 2018, IEEE Internet of Things Journal.

[16]  Joel J. P. C. Rodrigues,et al.  FAAL: Fog computing-based patient monitoring system for ambient assisted living , 2017, 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom).

[17]  R. Bellomo,et al.  Mortality related to severe sepsis and septic shock among critically ill patients in Australia and New Zealand, 2000-2012. , 2014, JAMA.

[18]  Le Song,et al.  GRAM: Graph-based Attention Model for Healthcare Representation Learning , 2016, KDD.

[19]  Larry A. Rendell,et al.  The Feature Selection Problem: Traditional Methods and a New Algorithm , 1992, AAAI.

[20]  Ritankar Das,et al.  Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial , 2017, BMJ Open Respiratory Research.

[21]  Bo Thiesson,et al.  Early detection of sepsis utilizing deep learning on electronic health record event sequences , 2019, Artif. Intell. Medicine.

[22]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[23]  Ekaba Bisong,et al.  Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners , 2019 .

[24]  Shamim Nemati,et al.  Early Prediction of Sepsis From Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019 , 2019, 2019 Computing in Cardiology (CinC).

[25]  Parisa Rashidi,et al.  Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis , 2017, IEEE Journal of Biomedical and Health Informatics.

[26]  Vladimir Stantchev,et al.  Smart Items, Fog and Cloud Computing as Enablers of Servitization in Healthcare , 2015 .

[27]  Sebastian Ruder,et al.  An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.

[28]  Rajkumar Buyya,et al.  iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments , 2016, Softw. Pract. Exp..

[29]  Alan Davy,et al.  Resource aware placement of IoT application modules in Fog-Cloud Computing Paradigm , 2017, 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM).

[30]  Victor I. Chang,et al.  Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare , 2018, Future Gener. Comput. Syst..

[31]  Christopher W. Barton,et al.  A computational approach to early sepsis detection , 2016, Comput. Biol. Medicine.

[32]  Magnus Bång,et al.  LiSep LSTM: A Machine Learning Algorithm for Early Detection of Septic Shock , 2019, Scientific Reports.