Exploring ICU Mortality Risk Prediction and Interpretability Analysis Using Machine Learning
DOI:
https://doi.org/10.53469/wjimt.2024.07(02).02Keywords:
Machine learning, Death prediction model, Intelligent medical treatment, Artificial intelligenceAbstract
Artificial intelligence, especially deep learning, is already being used in various fields through the labeling of big data, as well as significant enhancements in computing power and cloud storage. In medicine, AI is already beginning to make an impact at three levels: clinicians, health systems, and patients. On a deeper level, the health care industry has some notable and long-standing shortcomings, including a large number of serious diagnostic errors, errors in treatment, massive waste of resources, inefficient workflows, inequities, and insufficient time to communicate between patients and clinicians. Medical industry leaders and computer scientists eager for improvements assert that artificial intelligence may have a role to play in solving all these problems. The objective of this study was to develop an interpretable model to predict risk mortality in ICU patients with central force failure using the eICU Collaborative Research Database (EICU-CRD), a free and open intensive care database. In addition, the SHapley Additional Interpretation (SHAP) method is used to interpret the extreme gradient enhancement (i.e. XGBoost) model and explore prognostic factors in heart failure.
References
Akbar, A., Peoples, N., Xie, H., Sergot, P., Hussein, H., Peacock IV, W. F., & Rafique, Z. . (2022). Thrombolytic Administration for Acute Ischemic Stroke: What Processes can be Optimized?. McGill Journal of Medicine, 20(2).
Chen, Wangmei, et al. “Applying Machine Learning Algorithm to Optimize Personalized Education Recommendation System”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Feb. 2024, pp. 101-8, doi:10.53469/jtpes.2024.04(01).14.
“Implementation of Computer Vision Technology Based on Artificial Intelligence for Medical Image Analysis”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 69-76, https://doi.org/10.62051/ijcsit.v1n1.10.
Dong, Xinqi, et al. “The Prediction Trend of Enterprise Financial Risk Based on Machine Learning ARIMA Model”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Jan. 2024, pp. 65-71, doi:10.53469/jtpes.2024.04(01).09.
“A Deep Learning-Based Algorithm for Crop Disease Identification Positioning Using Computer Vision”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 85-92, https://doi.org/10.62051/ijcsit.v1n1.12.
K. Jin, Z. Z. Zhong and E. Y. Zhao, "Sustainable Digital Marketing Under Big Data: An AI Random Forest Model Approach," in IEEE Transactions on Engineering Management, vol. 71, pp. 3566-3579, 2024, doi: 10.1109/TEM.2023.3348991.
Jin, Keyan. "Impacts of Word of Mouth (WOM) on E-Business Online Pricing." JGIM vol.31, no.3 2023: pp.1-17. http://doi.org/10.4018/JGIM.324813
Du, S., Li, L., Wang, Y., Liu, Y., & Pan, Y. (2023). Application of HPV-16 in Liquid-Based thin Layer Cytology of Host Genetic Lesions Based on AI Diagnostic Technology Presentation of Liquid. Journal of Theory and Practice of Engineering Science, 3(12), 1-6.
Xin, Q., He, Y., Pan, Y., Wang, Y., & Du, S. (2023). The implementation of an AI-driven advertising push system based on a NLP algorithm. International Journal of Computer Science and Information Technology, 1(1), 30-37.
He, Yuhang, et al. “Intelligent Fault Analysis With AIOps Technology”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Feb. 2024, pp. 94-100, doi:10.53469/jtpes.2024.04(01).13.
Chen , J., Xiong, J., Wang, Y., Xin, Q., & Zhou, H. (2024). Implementation of an AI-based MRD Evaluation and Prediction Model for Multiple Myeloma. Frontiers in Computing and Intelligent Systems, 6(3), 127-131. https://doi.org/10.54097/zJ4MnbWW
Tan, Kai, et al. “Integrating Advanced Computer Vision and AI Algorithms for Autonomous Driving Systems”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Jan. 2024, pp. 41-48, doi:10.53469/jtpes.2024.04(01).06.
“Exploring New Frontiers of Deep Learning in Legal Practice: A Case Study of Large Language Models”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 131-8, https://doi.org/10.62051/ijcsit.v1n1.18.
Development of Machine Learning and Artificial Intelligence in Toxic Pathology. (2024). Frontiers in Computing and Intelligent Systems, 6(3), 137-141. https://doi.org/10.54097/Be1ExjZa
Jili Qian, et al. “Analysis and Diagnosis of Hemolytic Specimens by AU5800 Biochemical Analyzer Combined With AI Technology”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 100-3, https://doi.org/10.54097/qoseeQ5N.
“A Deep Learning-Based Algorithm for Crop Disease Identification Positioning Using Computer Vision”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 85-92, https://doi.org/10.62051/ijcsit.v1n1.12.
Pan, Yiming, et al. “Application of Three-Dimensional Coding Network in Screening and Diagnosis of Cervical Precancerous Lesions”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 61-64, https://doi.org/10.54097/mi3VM0yB.
Wei, Kuo, et al. “Strategic Application of AI Intelligent Algorithm in Network Threat Detection and Defense”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Jan. 2024, pp. 49-57, doi:10.53469/jtpes.2024.04(01).07.
Chen, Wangmei, et al. “Applying Machine Learning Algorithm to Optimize Personalized Education Recommendation System”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Feb. 2024, pp. 101-8, doi:10.53469/jtpes.2024.04(01).14.
“Implementation of Computer Vision Technology Based on Artificial Intelligence for Medical Image Analysis”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 69-76, https://doi.org/10.62051/ijcsit.v1n1.10.
Dong, Xinqi, et al. “The Prediction Trend of Enterprise Financial Risk Based on Machine Learning ARIMA Model”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Jan. 2024, pp. 65-71, doi:10.53469/jtpes.2024.04(01).09.
Xin, Q., He, Y., Pan, Y., Wang, Y., & Du, S. (2023). The implementation of an AI-driven advertising push system based on a NLP algorithm. International Journal of Computer Science and Information Technology, 1(1), 30-37.0
“Machine Learning Model Training and Practice: A Study on Constructing a Novel Drug Detection System”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 139-46, https://doi.org/10.62051/ijcsit.v1n1.19.
Q. Cheng, M. Tian, L. Yang, J. Zheng, and D. Xin, “Enhancing High-Frequency Trading Strategies with Edge Computing and Deep Learning”, Journal of Industrial Engineering & Applied Science, vol. 2, no. 1, pp. 32–38, Feb. 2024.
“A Deep Learning-Based Algorithm for Crop Disease Identification Positioning Using Computer Vision”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 85-92, https://doi.org/10.62051/ijcsit.v1n1.12.
“Unveiling the Future Navigating Next-Generation AI Frontiers and Innovations in Application”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 147-56, https://doi.org/10.62051/ijcsit.v1n1.20.
Zong, Yanqi, et al. “Improvements and Challenges in StarCraft II Macro-Management A Study on the MSC Dataset”. Journal of Theory and Practice of Engineering Science, vol. 3, no. 12, Dec. 2023, pp. 29-35, doi:10.53469/jtpes.2023.03(12).05.
An Overview of the Development of Stereotactic Body Radiation Therapy. (2024). Frontiers in Computing and Intelligent Systems, 6(3), 56-60. https://doi.org/10.54097/09nIy12x.
Zheng, Jiajian, et al. “The Credit Card Anti-Fraud Detection Model in the Context of Dynamic Integration Selection Algorithm”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 119-22, https://doi.org/10.54097/a5jafgdv.
Wang, Sihao, et al. “Diabetes Risk Analysis Based on Machine Learning LASSO Regression Model”. Journal of Theory and Practice of Engineering Science, vol. 4, no. 01, Jan. 2024, pp. 58-64, doi:10.53469/jtpes.2024.04(01).08.
“Enhancing Computer Digital Signal Processing through the Utilization of RNN Sequence Algorithms”. International Journal of Computer Science and Information Technology, vol. 1, no. 1, Dec. 2023, pp. 60-68, https://doi.org/10.62051/ijcsit.v1n1.09.
Yu, L., Liu, B., Lin, Q., Zhao, X., & Che, C. (2024). Semantic Similarity Matching for Patent Documents Using Ensemble BERT-related Model and Novel Text Processing Method. arXiv preprint arXiv:2401.06782.
Huang, J., Zhao, X., Che, C., Lin, Q., & Liu, B. (2024). Enhancing Essay Scoring with Adversarial Weights Perturbation and Metric-specific AttentionPooling. arXiv preprint arXiv:2401.05433.
Du, Shuqian, et al. “Application of HPV-16 in Liquid-Based Thin Layer Cytology of Host Genetic Lesions Based on AI Diagnostic Technology Presentation of Liquid”. Journal of Theory and Practice of Engineering Science, vol. 3, no. 12, Dec. 2023, pp. 1-6, doi:10.53469/jtpes.2023.03(12).01.
Pan, Yiming, et al. “Application of Three-Dimensional Coding Network in Screening and Diagnosis of Cervical Precancerous Lesions”. Frontiers in Computing and Intelligent Systems, vol. 6, no. 3, Jan. 2024, pp. 61-64, https://doi.org/10.54097/mi3VM0yB.