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.
“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]
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
(2014) · Cited by 33,419 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
(2010) · Cited by 14,134 · see citing works
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 13,192 · see citing works
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 · see citing works
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 · see citing works
(2016) · Cited by 11,679 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 5,882 · see citing works
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 · see citing works
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 4,748 · see citing works
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 · see citing works
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 · see citing works
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 2,877 · see citing works
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 · see citing works
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 · see citing works
Zhu, W., Lan, C., Xing, J., Zeng, W.-J., Li, Y.-H., Shen, L., & Xie, X.-H. (2016) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10451 · Cited by 934 · see citing works
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 · see citing works
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 · see citing works
Jaeger, H. (2007) · Scholarpedia · doi:10.4249/scholarpedia.2330 · Cited by 459 · see citing works
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 435 · see citing works
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 380 · see citing works
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 · see citing works
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 190 · see citing works
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 181 · see citing works
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 · see citing works
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 112 · see citing works
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 · see citing works
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 81 · see citing works
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 · see citing works
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 76 · see citing works
Bravi, A., Green, G. C., Longtin, A., & SEELY, A. J. E. (2012) · PLoS ONE · doi:10.1371/journal.pone.0045666 · Cited by 73 · see citing works
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 · see citing works
Big Neural Networks Waste CapacityPreprint
Dauphin, Y., & Bengio, Y. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.3583 · Cited by 54 · see citing works
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 · see citing works
Septic shock : providing early warnings through multivariate logistic regression models
Shavdia, D. (2007) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 27 · see citing works
Predicting outcome in patients with sepsis: new biomarkers for old expectations
Charles, P., & Gibot, S. (2014) · Critical Care · doi:10.1186/cc13723 · Cited by 18 · see citing works
Imputation-Enhanced Prediction of Septic Shock in ICU Patients
Ho, J. C., Lee, C. H., & Ghosh, J. (2014) · Cited by 14 · see citing works
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 · see citing works
Flagged first, then by citations
5 of the 52 references couldn’t be matched to a known paper.
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 · see citing works
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 · see citing works
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 580 · see citing works
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 · see citing works
Baek, Y., & Kim, H. Y. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.07.019 · Cited by 371 · see citing works
Self-citationPrediction of sepsis patients using machine learning approach: a meta-analysis
Islam, M. M., Nasrin, T., Walther, B. A., Wu, C.-C., Yang, H.-C., & Li, Y.-C. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.12.027 · Cited by 259 · see citing works
Ayala Solares, J. R., Diletta Raimondi, F. E., Zhu, Y., Rahimian, F., Canoy, D., Tran, J., Pinho Gomes, A. C., Payberah, A. H., Zottoli, M., Nazarzadeh, M., et al. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103337 · Cited by 247 · see citing works
Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review
Moor, M., Rieck, B., Horn, M., Jutzeler, C. R., & Borgwardt, K. M. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.607952 · Cited by 234 · see citing works
Masino, A. J., Harris, M. C., Forsyth, D., Ostapenko, S., Srinivasan, L., Bonafide, C. P., Balamuth, F., Schmatz, M., & Grundmeier, R. W. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212665 · Cited by 183 · see citing works
Predicting sepsis with a recurrent neural network using the MIMIC III database
Scherpf, M., Gräßer, F., Malberg, H., & Zaunseder, S. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103395 · Cited by 167 · see citing works
Multi-disease prediction using LSTM recurrent neural networks
Men, L., Ilk, N., Tang, X., & Liu, Y. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114905 · Cited by 140 · see citing works
Clinical applications of artificial intelligence in sepsis: A narrative review
Schinkel, M., Paranjape, K., Nannan Panday, R. S., Skyttberg, N., & Nanayakkara, P. W. B. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103488 · Cited by 130 · see citing works
Sheu, R.-K., & Pardeshi, M. S. (2022) · Sensors · doi:10.3390/s22208068 · Cited by 129 · see citing works
The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges
Sarwar, T., Seifollahi, S., Chan, J., Zhang, X., Aksakallı, V., Hudson, I. L., Verspoor, K. M., & Cavedon, L. (2022) · ACM Computing Surveys · doi:10.1145/3490234 · Cited by 129 · see citing works
LiSep LSTM: A Machine Learning Algorithm for Early Detection of Septic Shock
Fagerström, J., Bång, M., Wilhelms, D., & Chew, M. S. (2019) · Scientific Reports · doi:10.1038/s41598-019-51219-4 · Cited by 125 · see citing works
Islam, K. R., Prithula, J., Kumar, J., Tan, T. L., Reaz, M. B. I., Sumon, M. S. I., & Chowdhury, M. E. H. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12175658 · Cited by 124 · see citing works
An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis
Yang, M., Liu, C., Wang, X., Li, Y., Gao, H., Liu, X., & Li, J. (2020) · Critical Care Medicine · doi:10.1097/ccm.0000000000004550 · Cited by 120 · see citing works
A deep learning model for real-time mortality prediction in critically ill children
Kim, S. Y., Kim, S., Cho, J., Kim, Y. S., Sol, I. S., Sung, Y., Cho, I., Park, M., Jang, H., Kim, Y. H., et al. (2019) · Critical Care · doi:10.1186/s13054-019-2561-z · Cited by 117 · see citing works
Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU*
Kamaleswaran, R., Akbilgiç, O., Hallman, M. A., West, A. N., Davis, R. L., & Shah, S. (2018) · Pediatric Critical Care Medicine · doi:10.1097/pcc.0000000000001666 · Cited by 114 · see citing works
Machine learning for early detection of sepsis: an internal and temporal validation study
Bedoya, A. D., Futoma, J., Clement, M. E., Corey, K., Brajer, N., Lin, A., Simons, M. G., Gao, M., Nichols, M., Balu, S., et al. (2020) · JAMIA Open · doi:10.1093/jamiaopen/ooaa006 · Cited by 109 · see citing works
Luz, C. F., Vollmer, M., Decruyenaere, J., Nijsten, M. W., Glasner, C., & Sinha, B. (2020) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2020.02.003 · Cited by 106 · see citing works
Automated prediction of sepsis using temporal convolutional network
Kok, C., Jahmunah, V., Oh, S. L., Zhou, X., Gururajan, R., Tao, X., Cheong, K. H., Gururajan, R., Molinari, F., & Acharya, U. R. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103957 · Cited by 89 · see citing works
Zhang, L., Huang, J., & Liu, L. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1356-8 · Cited by 79 · see citing works
RetractedAn interpretable deep-learning model for early prediction of sepsis in the emergency department
Zhang, D., Yin, C., Hunold, K. M., Jiang, X., Caterino, J. M., & Zhang, P. (2021) · Patterns · doi:10.1016/j.patter.2020.100196 · Cited by 79 · see citing works
Abidin, A. Z., Deng, B., DSouza, A. M., Nagarajan, M., Coan, P., & Wismüller, A. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.01.008 · Cited by 76 · see citing works
Hong, N., Liu, C., Gao, J., Han, L., Chang, F., Gong, M., & Su, L. (2022) · JMIR Medical Informatics · doi:10.2196/28781 · Cited by 76 · see citing works
On classifying sepsis heterogeneity in the ICU: insight using machine learning
Ibrahim, Z. M., Wu, H., Hamoud, A., Stappen, L., Dobson, R., & Agarossi, A. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz211 · Cited by 73 · see citing works
Artificial Intelligence for Clinical Decision Support in Sepsis
Wu, M., Du, X., Gu, R., & Wei, J. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.665464 · Cited by 70 · see citing works
A deep learning approach for sepsis monitoring via severity score estimation
Aşuroğlu, T., & Oğul, H. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105816 · Cited by 69 · see citing works
Brufau, S. R., Whitford, D., Johnson, M. G., Hickman, J. A., Morlan, B. W., Therneau, T. M., Naessens, J. M., & Huddleston, J. M. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa347 · Cited by 68 · see citing works
Saqib, M., Sha, Y., & Wang, M. D. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8513254 · Cited by 68 · see citing works
Syed, M., Syed, S., Sexton, K. W., Syeda, H. B., Garza, M. Y., Zozus, M. N., Syed, F., Begum, S., Syed, A. U., Sanford, J. A., et al. (2021) · Informatics · doi:10.3390/informatics8010016 · Cited by 68 · see citing works
Kindle, R. D., Badawi, O., Celi, L. A., & Sturland, S. (2019) · Critical Care Clinics · doi:10.1016/j.ccc.2019.02.005 · Cited by 67 · see citing works
Evaluating machine learning models for sepsis prediction: A systematic review of methodologies
Deng, H., Sun, M., Wang, Y., Zeng, J., Yuan, T., Li, T., Li, D.-H., Chen, W., Zhou, P., Wang, Q., et al. (2021) · iScience · doi:10.1016/j.isci.2021.103651 · Cited by 66 · see citing works
van Wyk, F., Khojandi, A., Mohammed, A., Begoli, E., Davis, R. L., & Kamaleswaran, R. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.12.002 · Cited by 66 · see citing works
Bourcier, S., Klug, J., & Nguyen, L. S. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i26.4088 · Cited by 65 · see citing works
Fu, J., Li, W., Du, J., & Xiao, B. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104048 · Cited by 65 · see citing works
Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective
Giacobbe, D. R., Signori, A., Puente, F. D., Mora, S., Carmisciano, L., Briano, F., Vena, A., Ball, L., Robba, C., Pelosi, P. P., et al. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.617486 · Cited by 68 · see citing works
MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis
Rosnati, M., & Fortuin, V. (2021) · PLoS ONE · doi:10.1371/journal.pone.0251248 · Cited by 60 · see citing works
El-Rashidy, N. M., Abuhmed, T., Alarabi, L., El-Bakry, H. M., Abdelrazek, S. M., Ali, F., & El–Sappagh, S. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06631-1 · Cited by 57 · see citing works
Zhu, H.-H., Wang, X., Chen, X.-Q., & Zhang, L.-Y. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103178 · Cited by 56 · see citing works
Guo, F., Zhu, X., Wu, Z., Zhu, L., Wu, J., & Zhang, F. (2022) · Journal of Translational Medicine · doi:10.1186/s12967-022-03469-6 · Cited by 54 · see citing works
A Survey of Deep Learning for Alzheimer’s Disease
Zhou, Q., Wang, J., Yu, X., Wang, S., & Zhang, Y. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5020035 · Cited by 57 · see citing works
Xiang, Y., Ji, H., Zhou, Y., Li, F., Du, J., Rasmy, L., Wu, S., Zheng, W. J., Xu, H., Zhi, D., et al. (2020) · Journal of Medical Internet Research · doi:10.2196/16981 · Cited by 51 · see citing works
Creation of a Robust and Generalizable Machine Learning Classifier for Patient Ventilator Asynchrony
Rehm, G. B., Han, J., Kuhn, B., Delplanque, J., Anderson, N., Adams, J. Y., & Chuah, C. (2018) · Methods of Information in Medicine · doi:10.3414/me17-02-0012 · Cited by 50 · see citing works
Machine Learning for Early Prediction of Sepsis in Intensive Care Unit (ICU) Patients
Alanazi, A., Aldakhil, L., Aldhoayan, M. D., & Aldosari, B. (2023) · Medicina · doi:10.3390/medicina59071276 · Cited by 49 · see citing works
Zeng, S., Arjomandi, M., Tong, Y., Liao, Z. C., & Luo, G. (2022) · Journal of Medical Internet Research · doi:10.2196/28953 · Cited by 48 · see citing works
Multi-Branching Temporal Convolutional Network for Sepsis Prediction
Wang, Z., & Yao, B. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3092835 · Cited by 47 · see citing works
Wulff, A., Montag, S., Marschollek, M., & Jack, T. (2019) · Methods of Information in Medicine · doi:10.1055/s-0039-1695717 · Cited by 47 · see citing works
Kaieski, N., da Costa, C. A., Righi, R. D. R., Lora, P. S., & Eskofier, B. M. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106612 · Cited by 45 · see citing works
Supervised classification techniques for prediction of mortality in adult patients with sepsis
Rodríguez, A., Mendoza, D., Ascuntar, J., & Jaimes, F. A. (2020) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2020.09.013 · Cited by 42 · see citing works
Yadgarov, M. Y., Landoni, G., Бондоевич, Б. Л., Polyakov, P. A., Kadantseva, K., Smirnova, A. V., Kuznetsov, I. V., Shemetova, M. M., Yakovlev, A. А., & Likhvantsev, V. V. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1491358 · Cited by 41 · see citing works
Deep learning systems for automatic diagnosis of infant cry signals
Lahmiri, S., Tadj, C., Gargour, C. S., & Bekiros, S. D. (2021) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2021.111700 · Cited by 40 · see citing works
Tensor learning of pointwise mutual information from EHR data for early prediction of sepsis
Nesaragi, N., Patidar, S., & Aggarwal, V. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104430 · Cited by 39 · see citing works
A neural network approach to predict early neonatal sepsis
Martínez, F., Núñez‐Valdéz, E. R., Gómez, J. L., & García‐Díaz, V. (2019) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2019.04.015 · Cited by 39 · see citing works
Lerman, I. R., Bu, Y., Singh, R., Silverman, H. A., Bhardwaj, A., Mann, A., Widge, A. S., Palin, J., Puleo, C., & Lim, H. H. (2025) · Bioelectronic Medicine · doi:10.1186/s42234-024-00163-4 · Cited by 41 · see citing works
Predicting Infections Using Computational Intelligence – A Systematic Review
Baldominos, A., Puello, A., Oğul, H., Aşuroğlu, T., & Colomo‐Palacios, R. (2020) · IEEE Access · doi:10.1109/access.2020.2973006 · Cited by 35 · see citing works
Machine Learning Models for Early Prediction of Sepsis on Large Healthcare Datasets
Camacho-Cogollo, J. E., Bonet, I., Gil, B., & Iadanza, E. (2022) · Electronics · doi:10.3390/electronics11091507 · Cited by 35 · see citing works
Sadasivuni, S., Saha, M., Bhatia, N. K., Banerjee, I., & Sanyal, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-09712-w · Cited by 34 · see citing works
Lin, P.-C., Chen, K.-T., Chen, H.-C., Islam, M. M., & Lin, M. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11111055 · Cited by 32 · see citing works
Lung Cancer Prediction Using Electronic Claims Records: A Transformer-Based Approach
Chen, H., Wang, H., Lin, C., Yang, R., & Lee, C.-C. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3324191 · Cited by 34 · see citing works
Lind, M. L., Mooney, S. J., Carone, M., Althouse, B. M., Liu, C., Evans, L., Patel, K. B., Vo, P., Pergam, S. A., & Phipps, A. I. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.4514 · Cited by 31 · see citing works
TWIN: Personalized Clinical Trial Digital Twin Generation
Das, T., Wang, Z., & Sun, J. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599534 · Cited by 30 · see citing works
Zubair, M., Din, I., Sarwar, N., Boltayevich, E. B., Mаkhmudov, S., & Trabelsi, Z. (2025) · BMC Infectious Diseases · doi:10.1186/s12879-025-11423-2 · Cited by 30 · see citing works
Early prediction of sepsis using double fusion of deep features and handcrafted features
Duan, Y., Huo, J., Chen, M., Hou, F., Yan, G., Li, S., & Wang, H. (2023) · Applied Intelligence · doi:10.1007/s10489-022-04425-z · Cited by 30 · see citing works
AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units
Özyurt, Y., Kraus, M., Hatt, T., & Feuerriegel, S. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467143 · Cited by 28 · see citing works
Wang, S., Liu, X., Yuan, S., Bian, Y., Wu, H. M., & Ye, Q. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01643-w · Cited by 27 · see citing works
ricu: R’s interface to intensive care data
Bennett, N., Plečko, D., Ukor, I.-F., Meinshausen, N., & Bühlmann, P. (2022) · GigaScience · doi:10.1093/gigascience/giad041 · Cited by 25 · see citing works
The Promise of Machine Learning: When Will it be Delivered?
Akbilgiç, O., & Davis, R. L. (2019) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2019.04.006 · Cited by 26 · see citing works
Plate, J. D. J., van de Leur, R. R., Leenen, L. P. H., Hietbrink, F., Peelen, L. M., & Eijkemans, M. J. C. (2019) · BMC Medical Research Methodology · doi:10.1186/s12874-019-0847-0 · Cited by 25 · see citing works
Kim, J., Park, Y. R., Lee, J. H., Lee, J., Kim, Y., & Huh, J. W. (2019) · JMIR Medical Informatics · doi:10.2196/16349 · Cited by 25 · see citing works
Kim, T., Tae, Y., Yeo, H. J., Jang, J. H., Cho, K., Yoo, D., Lee, Y., Ahn, S., Kim, Y., Lee, N., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12227156 · Cited by 30 · see citing works
Bi-Directional Gated Recurrent Unit Based Ensemble Model for the Early Detection of Sepsis
Wickramaratne, S., & Mahmud, M. S. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175223 · Cited by 23 · see citing works
Gupta, A., Liu, T., & Crick, C. (2020) · Statistical Methods in Medical Research · doi:10.1177/0962280220929045 · Cited by 22 · see citing works
Park, H., Jung, D. Y., Ji, W., & Choi, C. (2020) · Journal of Medical Internet Research · doi:10.2196/19512 · Cited by 22 · see citing works
Deep learning approach on tabular data to predict early-onset neonatal sepsis
Alvi, R. H., Rahman, M. H., Khan, A. A. S., & Rahman, R. M. (2020) · Journal of Information and Telecommunication · doi:10.1080/24751839.2020.1843121 · Cited by 22 · see citing works
Enhancing sepsis management through machine learning techniques: A review
Ocampo-Quintero, N., Vidal-Cortés, P., del Río Carbajo, L., Fdez-Riverola, F., Reboiro-Jato, M., & Glez-Peña, D. (2022) · Medicina Intensiva · doi:10.1016/j.medin.2020.04.003 · Cited by 20 · see citing works
Zargoush, M., Sameh, A., Javadi, M., Shabani, S., Ghazalbash, S., & Perri, D. (2021) · Scientific Reports · doi:10.1038/s41598-021-00220-x · Cited by 20 · see citing works
Early Prediction of Sepsis Onset Using Neural Architecture Search Based on Genetic Algorithms
Kim, J. K., Ahn, W., Park, S., Lee, S.-H., & Kim, L. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19042349 · Cited by 19 · see citing works
Representation Learning for Early Sepsis Prediction
Tran, L., Shahabi, C., & Nguyen, M. H. (2019) · Computing in cardiology · doi:10.22489/cinc.2019.021 · Cited by 15 · see citing works
Graph Convolutional Networks-Based Noisy Data Imputation in Electronic Health Record
Lee, B. T., Kwon, O., Park, H., Cho, K.-J., Kwon, J., & Lee, Y. (2020) · Critical Care Medicine · doi:10.1097/ccm.0000000000004583 · Cited by 15 · see citing works
Schaack, D., Weigand, M. A., & Uhle, F. (2021) · PLoS ONE · doi:10.1371/journal.pone.0251800 · Cited by 15 · see citing works
Improving Sepsis Prediction Performance Using Conditional Recurrent Adversarial Networks
Apalak, M., & Kıasaleh, K. (2022) · IEEE Access · doi:10.1109/access.2022.3230324 · Cited by 14 · see citing works
Zheng, X., Meng, D., Chen, D., Wong, W.-K., To, K.-H., Zhu, L., Wu, J., Liang, Y., Leung, K.-S., Wong, M.-H., et al. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1012083 · Cited by 14 · see citing works
Luo, G. (2019) · Global Transitions · doi:10.1016/j.glt.2018.11.001 · Cited by 14 · see citing works
Isabona, J., & Ojuh, D. O. (2021) · International Journal of Mathematical Sciences and Computing · doi:10.5815/ijmsc.2021.02.02 · Cited by 14 · see citing works
Classifying sepsis from photoplethysmography
Lombardi, S., Partanen, P., Francia, P., Calamai, I., Deodati, R., Luchini, M., Spina, R., & Bocchi, L. (2022) · Health Information Science and Systems · doi:10.1007/s13755-022-00199-3 · Cited by 14 · see citing works
Luo, G., Stone, B. L., Koebnick, C., He, S., Au, D. H., Sheng, X., Murtaugh, M. A., Sward, K. A., Schatz, M. X., Zeiger, R. S., et al. (2019) · JMIR Research Protocols · doi:10.2196/13783 · Cited by 14 · see citing works
An Effective Diagnostic Model for Personalized Healthcare Using Deep Learning Techniques
Agarwal, P., Hassan, S. I., Mustafa, S. K., & Ahmad, J. (2020) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-7998-2101-4.ch005 · Cited by 13 · see citing works
Accurate diagnosis of sepsis using a neural network: Pilot study using routine clinical variables
Arriaga‐Pizano, L., Gonzalez-Olvera, M. A., Ferat-Osorio, E. A., Escobar, J. A., Hernández-Pérez, A. L., Revilla, M., López-Macías, C. I. R., León-Pedroza, J. I., Cabrera‐Rivera, G. L., Guadarrama-Aranda, U., et al. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106366 · Cited by 13 · see citing works
A Novel Switching State-Space Model for Post-ICU Mortality Prediction and Survival Analysis
Yin, Y., & Chou, C.-A. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3068357 · Cited by 13 · see citing works
Machine learning model for daily prediction of pediatric sepsis using Phoenix criteria
Chanci, D., Grunwell, J. R., Rafiei, A., Brown, S. R., Ripple, M. J., Bishop, N. R., Rajapreyar, P., Lima, L. M., & Kamaleswaran, R. (2025) · Pediatric Research · doi:10.1038/s41390-025-04221-8 · Cited by 12 · see citing works
Solís-García, J., Vega-Márquez, B., Nepomuceno, J. A., Riquelme, J. C., & Nepomuceno-Chamorro, I. A. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05124-z · Cited by 12 · see citing works
Cozzolino, C., Mao, S., Bassan, F., Bilato, L., Compagno, L., Salvò, V., Chiusaroli, L., Cocchio, S., & Baldo, V. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103137 · Cited by 14 · see citing works
Yasnitsky, L. N., Артурович, Д. А., Черепанов, Ф. М., Леонидович, Я. В., & Uteva, N. A. (2021) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2021.1993595 · Cited by 12 · see citing works
Rodrigues, D., Nastri, A. C. D. S. S., Magri, M. M., de Oliveira, M. S., Sabino, E., Figueiredo, P. H. M. F., Levin, A., Freire, M., Harima, L., Nunes, F. L. S., et al. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01931-5 · Cited by 11 · see citing works
PoEMS: Policy Network-Based Early Warning Monitoring System for Sepsis in Intensive Care Units
Dai, H., Hwang, H., & Tseng, V. S. M. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3272486 · Cited by 11 · see citing works
Hernandez, B., Ming, D. K. Y., Rawson, T. M., Bolton, W., Wilson, R. C., Vasikasin, V., Daniels, J., Rodríguez-Manzano, J., Davies, F., Georgiou, P., et al. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.103008 · Cited by 13 · see citing works
An attention-based bidirectional LSTM-CNN architecture for the early prediction of sepsis
Das, P. P., Wiese, L., Mast, M., Böhnke, J., Wulff, A., Marschollek, M., Bode, L., Rathert, H., Jack, T., Schamer, S., et al. (2024) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00568-z · Cited by 10 · see citing works
Upadhyaya, D. P., Tarabichi, Y., Prantzalos, K., Ayub, S., Kaelber, D. C., & Sahoo, S. S. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109251 · Cited by 10 · see citing works
- 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 › Epidemiology › 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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