Predicting Survival of Intensive Care Unit Paitents with Support Vector Machines
Guide to running
Warnings and Caveats
We used easy packages to install packages. as part of this it will run the install from everything in pkgs.r. As such, running a render or a preview will attempt to install any packages without prompting you to agree to installing them. We have hardcoded the repo for cran in the package installer but if thats compremised the auto install could cause security concerns.
Rendering the page
For best results, run quarto render --cache as this takes a long time to render the visualizations and build and tune the model.
You will need to install the following system libraries in order for the r code to work properly
- libgit2
- libharfbuzz-dev (debian, ubuntu, etc)
- harfbuzz-devel(fedora, EPEL)
- libfribidi-dev (debian, ubuntu, etc)
- fribidi-devl (fedora, EPEL) libv8 or libnode-dev
sudo apt-get install libcurl4-openssl-dev
sudo apt-get install libcurl4-openssl-dev sudo apt-get install libcurl4-openssl-dev # Complete References ::: {#refs} :::
References
Adeoye, et al., Abiodun O. 2021. “Utilizing Support Vector Machines for Diabetes Mellitus Classification from Electronic Medical Records,” International Journal of Advanced Computer Science and Information Technology (IJACSIT) 11 (10): 120–14.
Bansal, Malti, Apoorva Goyal, and Apoorva Choudhary. 2022. “A Comparative Analysis of k-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory Algorithms in Machine Learning.” Decision Analytics Journal 3: 100071. https://doi.org/https://doi.org/10.1016/j.dajour.2022.100071.
Chris J. Sidey-Gibbons, Jenni A. M. Sidey-Gibbons &. 2019. “Machine Learning in Medicine: A Practical Introduction.” BMC Medical Research Methodology Volume 19 (64).
Cristianini, Nello, and Bernhard Scholkopf. Fall 2002. “"Support Vector Machines and Kernel Methods: The New Generation of Learning Machines. (Articles).” AI Magazine 23 (3): 31.
Fouodo, et al, Cesaire. 2022. “Support Vector Machines for Survival Analysis with r.” R Journal 14 (2): 92–107.
Greco, Massimiliano, Pier F Caruso, and Maurizio Cecconi. 2020. “Artificial Intelligence in the Intensive Care Unit.” In Seminars in Respiratory and Critical Care Medicine, 42:002–9. 01. Thieme Medical Publishers, Inc. 333 Seventh Avenue, 18th Floor, New York, NY ….
Han, Kamber, J., and J. Pei. 2012. Data Mining: Concepts and Techniques. Morgan Kaufmann.
Houthooft, Rein, Joeri Ruyssinck, Joachim van der Herten, Sean Stijven, Ivo Couckuyt, Bram Gadeyne, Femke Ongenae, et al. 2015. “Predictive Modelling of Survival and Length of Stay in Critically Ill Patients Using Sequential Organ Failure Scores.” Artificial Intelligence in Medicine 63 (3): 191–207.
Hu, Wei Huang, Xiangfen, and Qiang Wu. n.d. “A New Support Vector Machine Algorithm for Data Mining.” Knowledge-Based Systems 112 (2016): 118–28.
Ismail, et al, Gaber A. 2020. “An Approach Using Support Vector Machines to Predict Hospital Readmission.” Journal of Medical Systems 44 (9): 1–10.
Karatzoglou, Alexandros, David Meyer, and Kurt Hornik. 2006. “Support Vector Machines in r.” Journal of Statistical Software 15 (9): 1–28. https://doi.org/10.18637/jss.v015.i09.
Liu, X. X., Chen. June 2018. “Mortality Prediction Based on Imbalanced High-Dimensional ICU Big Data.” Computers in Industry 98 (June 2018): 218–25.
Mantovani, Rafael G., André L. D. Rossi, Joaquin Vanschoren, Bernd Bischl, and André C. P. L. F. de Carvalho. 2015. “Effectiveness of Random Search in SVM Hyper-Parameter Tuning.” In 2015 International Joint Conference on Neural Networks (IJCNN), 1–8. https://doi.org/10.1109/IJCNN.2015.7280664.
Mohan, Lalit, Janmejay Pant, Priyanka Suyal, and Arvind Kumar. 2020. “Support Vector Machine Accuracy Improvement with Classification.” In 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN), 477–81. https://doi.org/10.1109/CICN49253.2020.9242572.
Pölsterl, Sebastian, Nassir Navab, and Amin Katouzian. 2015. “Fast Training of Support Vector Machines for Survival Analysis.” In Machine Learning and Knowledge Discovery in Databases, edited by Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, João Gama, Alípio Jorge, and Carlos Soares, 243–59. Cham: Springer International Publishing.
Sapankevych, Nicholas I., and Ravi Sankar. 2009. “Time Series Prediction Using Support Vector Machines: A Survey.” IEEE Computational Intelligence Magazine 4 (2): 24–38. https://doi.org/10.1109/MCI.2009.932254.
Veropoulos, Konstantinos, Colin Campbell, Nello Cristianini, et al. 1999. “Controlling the Sensitivity of Support Vector Machines.” In Proceedings of the International Joint Conference on AI, 55:60. Stockholm.
Xu, Lihong Li, Fei, and Zhihua Zhou. 2010. “SVM Kernels for Data Mining: A Comparative Study.” Proceedings of the 2010 SIAM International Conference on Data Mining (SDM), 585–96.
Yonas B. Dibike, Dimitri Solomatine, Slavco Velickov, and Michael B. Abbott. 2001. “Model Induction with Support Vector Machines: Introduction and Applications.” Journal of Computing in Civil Engineering 15 (3).
Zeng, Zhi-Qiang, Hong-Bin Yu, Hua-Rong Xu, Yan-Qi Xie, and Ji Gao. 2008. “Fast Training Support Vector Machines Using Parallel Sequential Minimal Optimization.” In 2008 3rd International Conference on Intelligent System and Knowledge Engineering, 1:997–1001. https://doi.org/10.1109/ISKE.2008.4731075.
Zhou, et al, Xingyu. 2023. “Using Support Vector Machines for Deep Mining of Electronic Medical Records in Order to Predict Prognosis of Severe, Acute Myocardial Infarction.” Frontiers in Cardiovascular Medicine 10: 918.