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Learning representations for the early detection of sepsis with deep neural networks
Abstract
Background Sepsis is one of the leading causes of death in intensive care unit patients. Early detection of sepsis is vital because mortality increases as the sepsis stage worsens. Objective This study aimed to develop detection models for the early stage of sepsis using deep learning methodologies, and to compare the feasibility and performance of the new deep learning methodology with those of the regression method with conventional temporal feature extraction. Method Study group selection adhered to the InSight model. The results of the deep learning-based models and the InSight model were compared. Results With deep feedforward networks, the area under the ROC curve (AUC) of the models were 0.887 and 0.915 for the InSight and the new feature sets, respectively. For the model with the combined feature set, the AUC was the same as that of the basic feature set (0.915). For the long short-term memory model, only the basic feature set was applied and the AUC improved to 0.929 compared with the existing 0.887 of the InSight model. Conclusions The contributions of this paper can be summarized in three ways: (i) improved performance without feature extraction using domain knowledge, (ii) verification of feature extraction capability of deep neural networks through comparison with reference features, and (iii) improved performance with feedforward neural networks using long short-term memory, a neural network architecture that can learn sequential patterns.
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“numerous models were tested on the freely accessible MIMIC database [27, 33, 52–59]”
From Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy (2020) · cites this paper as [53]
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
(2014) · Cited by 33,419
The third international consensus definitions for sepsis and septic shock (Sepsis-3).
Singer, M., Deutschman, C. S., Seymour, C. W., Shankar-Hari, M., Annane, D., Bauer, M., Bellomo, R., Bernard, G. R., Chiche, J.-D., Coopersmith, C. M., et al. (2016) · JAMA · doi:10.1001/jama.2016.0287 · Cited by 28,250
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809
(2010) · Cited by 14,134
Bone, R. C., BALK, R. A., Cerra, F. B., Dellinger, R. P., Fein, A. M., Knaus, W. A., Schein, R. M. H., & Sibbald, W. J. (1992) · CHEST Journal · doi:10.1378/chest.101.6.1644 · Cited by 13,335
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
Aggregated Residual Transformations for Deep Neural Networks
Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.634 · Cited by 12,465
(2016) · Cited by 11,679
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
Angus, D. C., Linde-Zwirble, W. T., Lidicker, J., Clermont, G., Carcillo, J., & Pinsky, M. R. (2001) · Critical Care Medicine · doi:10.1097/00003246-200107000-00002 · Cited by 8,503
Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., & Schmidhuber, J. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2582924 · Cited by 6,955
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Graves, A., & Schmidhuber, J. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.06.042 · Cited by 6,152
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370
Caffe: Convolutional Architecture for Fast Feature EmbeddingPreprint
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5093 · Cited by 5,299
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524
2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference
Conference, F. T. I. S. D., Levy, M. M., Fink, M. P., Marshall, J. C., Abraham, E. H., Angus, D. C., Cook, D., Cohen, J., Opal, S. M., Vincent, J., et al. (2003) · Intensive Care Medicine · doi:10.1007/s00134-003-1662-x · Cited by 2,944
Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition
Ordóñez, F., & Roggen, D. (2016) · Sensors · doi:10.3390/s16010115 · Cited by 2,761
Explaining nonlinear classification decisions with deep taylor decomposition
Montavon, G., Lapuschkin, S., Binder, A., Samek, W., & Müller, K.-R. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.11.008 · Cited by 1,357
Zhu, W., Lan, C., Xing, J., Zeng, W., Li, Y., Shen, L., & Xie, X. S. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10451 · Cited by 958
A targeted real-time early warning score (TREWScore) for septic shock.
Henry, K. E., Hager, D. N., Pronovost, P. J., & Saria, S. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aab3719 · Cited by 598
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Güler, İ., Übeylı, E. D., & Güler, İ. (2005) · Expert Systems with Applications · doi:10.1016/j.eswa.2005.04.011 · Cited by 503
Jaeger, H. (2007) · Scholarpedia · doi:10.4249/scholarpedia.2330 · Cited by 459
Synthesizing the preferred inputs for neurons in neural networks via deep generator networksPreprint
Nguyen, A., Dosovitskiy, A., Yosinski, J., Brox, T., & Clune, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.09304 · Cited by 453
Interpretable deep neural networks for single-trial EEG classification
Sturm, I., Lapuschkin, S., Samek, W., & Müller, K. (2016) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2016.10.008 · Cited by 396
A computational approach to early sepsis detection
Calvert, J. S., Price, D. A., Chettipally, U. K., Barton, C. W., Feldman, M. D., Hoffman, J. L., Jay, M., & Das, R. (2016) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2016.05.003 · Cited by 244
Gultepe, E., Green, J. P., Nguyen, H., Adams, J. Y., Albertson, T. E., & Tagkopoulos, I. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001815 · Cited by 196
Marty, P., Roquilly, A., Vallée, F., Luzi, A., Ferré, F., Fourcade, O., Asehnoune, K., & Minville, V. (2013) · Annals of Intensive Care · doi:10.1186/2110-5820-3-3 · Cited by 189
Recurrent neural network-based approach for early recognition of Alzheimer's disease in EEG
Petrosian, A. A., Prokhorov, D. V., Lajara-Nanson, W., & Schiffer, R. B. (2001) · Clinical Neurophysiology · doi:10.1016/s1388-2457(01)00579-x · Cited by 126
Predictive monitoring for early detection of sepsis in neonatal ICU patients
Fairchild, K. D. (2013) · Current Opinion in Pediatrics · doi:10.1097/mop.0b013e32835e8fe6 · Cited by 114
Early prediction of septic shock in hospitalized patients
Thiel, S. W., Rosini, J. M., Shannon, W. D., Doherty, J. A., Micek, S. T., & Kollef, M. H. (2010) · Journal of Hospital Medicine · doi:10.1002/jhm.530 · Cited by 103
Presymptomatic Prediction of Sepsis in Intensive Care Unit Patients
Lukaszewski, R. A., Yates, A., Jackson, M. C., Swingler, K. M., Scherer, J. M., Simpson, A. J. H., Sadler, P. J., McQuillan, P., Titball, R. W., Brooks, T. J. G., et al. (2008) · Clinical and Vaccine Immunology · doi:10.1128/cvi.00486-07 · Cited by 83
Big Neural Networks Waste CapacityPreprint
Dauphin, Y., & Bengio, Y. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.3583 · Cited by 82
A modified Elman neural network model with application to dynamical systems identification
Gao, X. Z., Gao, X., & Ovaska, S. J. (2002) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.1996.571312 · Cited by 78
Spontaneous Neutrophil Migration Patterns during Sepsis after Major Burns
Jones, C. N., Moore, M. E., Dimisko, L., Alexander, A., Ibrahim, A., Hassell, B. A., Warren, H. S., Tompkins, R. G., Fagan, S. P., & Irimia, D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0114509 · Cited by 74
Bravi, A., Green, G. C., Longtin, A., & SEELY, A. J. E. (2012) · PLoS ONE · doi:10.1371/journal.pone.0045666 · Cited by 73
A Severe Sepsis Mortality Prediction Model and Score for Use With Administrative Data
Ford, D. W., Goodwin, A. J., IV, Simpson, A. N., Johnson, E. E., Nadig, N. R., & Simpson, K. N. (2015) · Critical Care Medicine · doi:10.1097/ccm.0000000000001392 · Cited by 71
Theoretical derivation of momentum term in back-propagation
Hagiwara, M. (2003) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1992.287108 · Cited by 31
Septic shock : providing early warnings through multivariate logistic regression models
Shavdia, D. (2007) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 27
Predicting outcome in patients with sepsis: new biomarkers for old expectations
Charles, P., & Gibot, S. (2014) · Critical Care · doi:10.1186/cc13723 · Cited by 19
Imputation-Enhanced Prediction of Septic Shock in ICU Patients
Ho, J. C., Lee, C. H., & Ghosh, J. (2014) · Cited by 14
Henry, K. E., Paxton, C., Kim, K. S., Pham, J. C., & Saria, S. (2014) · Critical Care Medicine · doi:10.1097/01.ccm.0000457596.46586.cd · Cited by 3
Research works printed but not linked: 2
- Torio, 2011, vol. 160, 2006
- Torio, 2011, vol. 160, 2006
Cited by
Sorted by citations
Xiao, C., Choi, E., & Sun, J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy068 · Cited by 792
Fleuren, L. M., Klausch, T. L. T., Zwager, C. L., Schoonmade, L. J., Guo, T., Roggeveen, L. F., Swart, E. L., Girbes, A. R. J., Thoral, P., Ercole, A., et al. (2020) · Intensive Care Medicine · doi:10.1007/s00134-019-05872-y · Cited by 755
Remote patient monitoring using artificial intelligence: Current state, applications, and challenges
Shaik, T., Tao, X., Higgins, N., Li, L., Gururajan, R., Zhou, X., & Acharya, U. R. (2023) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1485 · Cited by 620
Lauritsen, S. M., Kristensen, M., Olsen, M. V., Larsen, M. S., Lauritsen, K. M., Jørgensen, M. J., Lange, J., & Thiesson, B. (2020) · Nature Communications · doi:10.1038/s41467-020-17431-x · Cited by 448
Baek, Y., & Kim, H. Y. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.07.019 · Cited by 385
Self-citation
Metadata
- Authors
- Kam, H. J., & Kim, H. Y.
- Affiliations
- Asan Medical Center (KR)
- Ajou University (KR)
- Corresponding author
- Kim, Ha Young
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2017-10
- Volume
- 89
- Pages
- 248-255
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W2750557731
- Cited by
- 218
- References
- 52
- Citations by year
- 2026: 10 · 2025: 22 · 2024: 21 · 2023: 28 · 2022: 29 · 2021: 41 · 2020: 31 · 2019: 26 · 2018: 9 · 2017: 1
- FWCI
- 14.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ajou University
- Korea Health Technology R&D Project
- Korea Health Industry Development Institute
- Ministry of Health & Welfare, Republic of Korea (HI16C0982)
- Topics
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Sepsis Diagnosis and Treatment
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- sepsis, early detection, deep neural network, long short-term memory, feedforward neural networks, feature extraction, intensive care unit patients, temporal feature extraction, sepsis mortality
- MeSH terms
- Machine Learning, Adult, Critical Care, Female, Humans, Male, Models, Biological, Neural Networks, Computer, Sepsis
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