Deep Residual Networks and Bayesian Data Priors in the Survival Prediction and Classification
DOI:
https://doi.org/10.54097/hset.v56i.10095Keywords:
Sepsis, Mortality projections, Deep residual network, Bayesian data priorAbstract
Sepsis is a highly lethal disease in intensive care units, and patient indicators are constantly changing, making accurate prediction of patient mortality crucial for doctors to develop appropriate treatment plans. While machine learning and deep learning have been applied to sepsis research, model generalization performance can suffer from underfitting or gradient issues. To address these challenges, we propose using a deep residual network and a deep residual network incorporating Bayesian data prior to predict patient mortality using the MIMIC-III dataset. The accuracy of the two models on the validation set reached 0.9392 and 0.9329, respectively.
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Singer, Mervyn., Deutschman, Clifford S., Seymour, Christopher Warren., Shankar-Hari, Manu., & Annane, Djillali. (2016). The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA, 315(8), 801-10.
Rhodes, Andrew., Evans, Laura E., Alhazzani, Waleed., Levy, Mitchell M., & Antonelli, Massimo.. (2017). Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016. Critical care medicine, 45(3), 486-552.
Zargoush, Manaf., Sameh, Alireza., Javadi, Mahdi., Shabani, Siyavash., & Ghazalbash, Somayeh. (2021). The impact of recency and adequacy of historical information on sepsis predictions using machine learning. scientific reports, 11(1), 20869.
Moreno-Torres, Víctor., Royuela, Ana., Múñez, Elena., Ortega, Alfonso., & Gutierrez, Ángela.. (2021). Better prognostic ability of NEWS2, SOFA and SAPS-II in septic patients. Medicina clinica.
Islam, Md Mohaimenul., Nasrin, Tahmina., Walther, Bruno Andreas., Wu, Chieh-Chen., & Yang, Hsuan-Chia.. (2018). Prediction of sepsis patients using machine learning approach: A meta-analysis. Computer methods and programs in biomedicine, 170.
Delahanty, Ryan J., Alvarez, JoAnn., Flynn, Lisa M., Sherwin, Robert L., & Jones, Spencer S.. (2019). Development and Evaluation of a Machine Learning Model for the Early Identification of Patients at Risk for Sepsis. Annals of emergency medicine, 73(4), 334-344.
Wang, Dong., Wang, Dong., Wang, Dong., Li, Jinbo., & Li, Jinbo.. (2021). A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients. Frontiers in public health, 9.
Singh, Yash Veer., Singh, Pushpendra., Khan, Shadab., & Singh, Ram Sewak.. (2022). A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients. Journal of healthcare engineering, 2022, 9263391.
Goh, Kim Huat., Wang, Le., Yeow, Adrian Yong Kwang., Poh, Hermione., & Li, Ke.. (2021). Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nature communications, 12(1), 711.
Guo, Fei., Zhu, Xishun., Wu, Zhiheng., Zhu, Li., & Wu, Jianhua.. (2022). Clinical applications of machine learning in the survival prediction and classification of sepsis: coagulation and heparin usage matter. Journal of translational medicine.
Zhang, Dongdong., Yin, Changchang., Yin, Changchang., Hunold, Katherine M., & Jiang, Xiaoqian.. (2021). An interpretable deep-learning model for early prediction of sepsis in the emergency department. Patterns (New York, N.Y.), 2(2), 100196.
Zargoush, Manaf., Sameh, Alireza., Javadi, Mahdi., Shabani, Siyavash., & Ghazalbash, Somayeh.. (2021). The impact of recency and adequacy of historical information on sepsis predictions using machine learning. Scientific reports, 11(1), 20869.
Aşuroğlu, Tunç., & Oğul, Hasan.. (2020). A deep learning approach for sepsis monitoring via severity score estimation. Computer methods and programs in biomedicine, 198.
Kwon, Joon-Myoung., Kwon, Joon-Myoung., Kwon, Joon-Myoung., Kwon, Joon-Myoung., & Lee, Ye Rang.. (2021). Deep-learning model for screening sepsis using electrocardiography. Scandinavian journal of trauma, resuscitation and emergency medicine, 29(1).
Rafiei, Alireza., Rezaee, Alireza., Hajati, Farshid., Gheisari, Soheila., & Golzan, Mojtaba.. (2020). SSP: Early prediction of sepsis using fully connected LSTM-CNN model. Computers in biology and medicine, 128.
Lv, Yi., & Huang, Zhijia.. (2022). Account of Deep Learning-Based Ultrasonic Image Feature in the Diagnosis of Severe Sepsis Complicated with Acute Kidney Injury. Computational and mathematical methods in medicine, 2022, 8158634.
Mekruksavanich, Sakorn., Jitpattanakul, Anuchit., & Jitpattanakul, Anuchit.. (2022). Deep Residual Network for Smartwatch-Based User Identification through Complex Hand Movements. Sensors (Basel, Switzerland), 22(8).
Han, Yongming., Cao, Lian., Geng, Zhiqiang., Ping, Weiying., & Zuo, Xiaoyu.. (2022). Novel economy and carbon emissions prediction model of different countries or regions in the world for energy optimization using improved residual neural network. The Science of the total environment.
Li, Juan., Wang, Baoxiang., Cui, Xuerong., Li, Shibao., & Liu, Jianhang.. (2022). Underwater Acoustic Target Recognition Based on Attention Residual Network. Entropy (Basel, Switzerland).
Pan, Jinghui., Qu, Lili., & Peng, Kaixiang.. (2022). Deep residual neural-network-based robot joint fault diagnosis method. Scientific reports.
Zhang, Zhao., Tang, Zemin., Wang, Yang., Zhang, Zheng., & Zhan, Choujun.. (2021). Dense Residual Network: Enhancing global dense feature flow for character recognition. Neural networks : the official journal of the International Neural Network Society, 139, 77-85.
Bao, Yin-Xin., Cao, Yang., Cao, Yang., Shen, Qin-Qin., & Shi, Quan.. (2022). Global-Local Spatial-Temporal Residual Correlation Network for Urban Traffic Status Prediction. Computational intelligence and neuroscience, 2022, 7344522.
Wang, Pei., Wang, Pei., Wang, Pei., Wang, Pei., & Luo, Fan.. (2022). S-ResNet: An improved ResNet neural model capable of the identification of small insects. Frontiers in plant science.
Milanés-Hermosilla, Daily., Trujillo-Codorniú, Rafael., Trujillo-Codorniú, Rafael., Lamar-Carbonell, Saddid., & Sagaró-Zamora, Roberto.. (2023). Robust Motor Imagery Tasks Classification Approach Using Bayesian Neural Network. Sensors (Basel, Switzerland).
Joshi, Prasoon., & Dhar, Riddhiman.. (2022). EpICC: A Bayesian neural network model with uncertainty correction for a more accurate classification of cancer. Scientific reports.
Kim, QHwan., Ko, Joon-Hyuk., Kim, Sunghoon., Park, Nojun., & Jhe, Wonho.. (2021). Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction. Bioinformatics (Oxford, England).
Shi, Lei., Copot, Cosmin., & Vanl&uit, Steve.. (2021). A Bayesian Deep Neural Network for Safe Visual Servoing in Human-Robot Interaction. Frontiers in robotics and AI, 8.
Johnson, Alistair E W., Pollard, Tom J., Shen, Lu., Lehman, Li-Wei H., & Feng, Mengling.. (2016). MIMIC-III, a freely accessible critical care database. Scientific data, 3, 160035.
Mahmud, F., Khudzari, A.Z.M., Pau, C.P., Ramli, M.F., Jaffar, N., Gaaffar, I.F. (2022). Pre-assessment of Machine Learning Approaches for Patient Length of Stay Prediction. In: Mustapha, A.B., Shamsuddin, S., Zuhaib Haider Rizvi, S., Asman, S.B., Jamaian, S.S. (eds) Proceedings of the 7th International Conference on the Applications of Science and Mathematics 2021. Springer Proceedings in Physics, vol 273. Springer, Singapore. https://doi.org/10.1007/978-981-16-8903-1_32
Liu, Junxiu., Li, Mingxing., Luo, Yuling., Yang, Su., & Li, Wei.. (2021). Alzheimer's disease detection using depthwise separable convolutional neural networks. Computer methods and programs in biomedicine, 203, 106032.
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