Learning representations for the early detection of sepsis with deep neural networks

Computers in Biology and Medicine - Tập 89 - Trang 248-255 - 2017
Hye Jin Kam1, Ha Young Kim2
1Health Innovation Bigdata Center, Asan Institute for Life Sciences, Asan Medical Center, 88, Olympic-ro 43 gil, Songpa-gu, Seoul, 05505, South Korea
2Department of Financial Engineering, School of Business, Ajou University, Worldcupro 206, Yeongtong-gu, Suwon, 16499, South Korea

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Lukaszewski, 2008, Presymptomatic prediction of sepsis in intensive care unit patients, Clin. Vaccine Immunol., 15, 1089, 10.1128/CVI.00486-07

Angus, 2001, Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care, Crit. Care Med., 29, 1303, 10.1097/00003246-200107000-00002

Torio, 2011, vol. 160, 2006

Torio, 2011, vol. 160, 2006

Ho, 2012, Imputation-enhanced prediction of septic shock in icu patients, 18

Henry, 2015, A targeted real-time early warning score (trewscore) for septic shock, Sci. Transl. Med., 7, 10.1126/scitranslmed.aab3719

Thiel, 2010, Early prediction of septic shock in hospitalized patients, J. Hosp. Med., 5, 19, 10.1002/jhm.530

Henry, 2014, 63: rews: Real-time early warning score for septic shock, Crit. Care Med., 42, A1384, 10.1097/01.ccm.0000457596.46586.cd

Shavdia, 2007

Gultepe, 2014, From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system, J. Am. Med. Inf. Assoc., 315, 10.1136/amiajnl-2013-001815

Marty, 2013, Lactate clearance for death prediction in severe sepsis or septic shock patients during the first 24 hours in intensive care unit: an observational study, Ann. Intensive Care, 3, 3, 10.1186/2110-5820-3-3

Charles, 2014, Predicting outcome in patients with sepsis: new biomarkers for old expectations, Crit. Care, 18, 108, 10.1186/cc13723

Ford, 2016, A severe sepsis mortality prediction model and score for use with administrative data, Crit. Care Med., 44, 319, 10.1097/CCM.0000000000001392

Bone, 1992, Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis, Chest, 101, 1644, 10.1378/chest.101.6.1644

Jones, 2014, Spontaneous neutrophil migration patterns during sepsis after major burns, PLoS One, 9, e114509, 10.1371/journal.pone.0114509

Kim, 2010

Calvert, 2016, A computational approach to early sepsis detection, Comput. Biol. Med., 74, 69, 10.1016/j.compbiomed.2016.05.003

Fairchild, 2013, Predictive monitoring for early detection of sepsis in neonatal icu patients, Curr. Opin. Pediatr., 25, 172, 10.1097/MOP.0b013e32835e8fe6

Bravi, 2012, Monitoring and identification of sepsis development through a composite measure of heart rate variability, PLoS One, 7, e45666, 10.1371/journal.pone.0045666

Goldberger, 2000, Physiobank, physiotoolkit, and physionet, Circulation, 101, e215, 10.1161/01.CIR.101.23.e215

Singer, 2016, The third international consensus definitions for sepsis and septic shock (sepsis-3), Jama, 315, 801, 10.1001/jama.2016.0287

Goodfellow, 2016

LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539

Bengio, 2013, Representation learning: a review and new perspectives, IEEE Trans. Pattern Anal. Mach. Intell., 35, 1798, 10.1109/TPAMI.2013.50

Dauphin, 2013

Szegedy, 2017, Inception-v4, inception-resnet and the impact of residual connections on learning, 4278

He, 2016, Identity mappings in deep residual networks, 630

Zhang, 2016

Xie, 2016

Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735

Graves, 2005, Framewise phoneme classification with bidirectional lstm and other neural network architectures, Neural Netw., 18, 602, 10.1016/j.neunet.2005.06.042

Greff, 2017, Lstm: a search space odyssey, IEEE Trans. Neural Netw. Learn. Syst., PP, 1

Zhu, 2016, Co-occurrence feature learning for skeleton based action recognition using regularized deep lstm networks, vol. 2, 8

Ordóñez, 2016, Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition, Sensors, 16, 115, 10.3390/s16010115

Glorot, 2010, Understanding the difficulty of training deep feedforward neural networks, 249

Hagiwara, 1992, Theoretical derivation of momentum term in back-propagation, vol. 1, 682

Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15, 1929

Jia, 2014, Caffe: convolutional architecture for fast feature embedding, 675

Jaeger, 2007, Echo state network, Scholarpedia, 2, 2330, 10.4249/scholarpedia.2330

Gao, 1996, A modified elman neural network model with application to dynamical systems identification, vol. 2, 1376

Kingma, 2014

Abadi, 2016

Levy, 2003, 2001 sccm/esicm/accp/ats/sis international sepsis definitions conference, Intensive Care Med., 29, 530, 10.1007/s00134-003-1662-x

Güler, 2005, Recurrent neural networks employing lyapunov exponents for eeg signals classification, Expert Syst. Appl., 29, 506, 10.1016/j.eswa.2005.04.011

Petrosian, 2001, Recurrent neural network-based approach for early recognition of alzheimer's disease in eeg, Clin. Neurophysiol., 112, 1378, 10.1016/S1388-2457(01)00579-X

Zeiler, 2014, Visualizing and understanding convolutional networks, 818

Simonyan, 2013

Nguyen, 2016, Synthesizing the preferred inputs for neurons in neural networks via deep generator networks, Adv. Neural Inf. Process. Syst., 3387

Bach, 2015, On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation, PloS One, 10, e0130140, 10.1371/journal.pone.0130140

Montavon, 2017, Explaining nonlinear classification decisions with deep taylor decomposition, Pattern Recognit., 65, 211, 10.1016/j.patcog.2016.11.008

Sturm, 2016, Interpretable deep neural networks for single-trial eeg classification, J. Neurosci. Methods, 274, 141, 10.1016/j.jneumeth.2016.10.008