[1]XIONG Lipeng,XU Xiuyuan,NIU Hao,et al.Predicting postoperative pulmonary complications after lung surgery using nmODE[J].CAAI Transactions on Intelligent Systems,2025,20(1):198-205.[doi:10.11992/tis.202401007]
Copy

Predicting postoperative pulmonary complications after lung surgery using nmODE

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 1 Page number: 198-205 Column: 学术论文—人工智能基础 Public date: 2025-01-05
Title:
Predicting postoperative pulmonary complications after lung surgery using nmODE
Author(s):
XIONG Lipeng1;  XU Xiuyuan1;  NIU Hao1;  CHEN Nan2;  ZHANG Yi1
1. College of Computer Science, Sichuan University, Chengdu 610065, China;
2. West China Hospital, Sichuan University, Chengdu 610065, China
Keywords:
disease prediction; heterogeneous tabular data; neural memory ordinary differential equation; extreme gradient boosting; long short-term memory; synthetic minority oversampling technique; class imbalance; patient prognosis
CLC:
TP399
DOI:
10.11992/tis.202401007
Abstract:
In order to accurately predict the occurrence of postoperative complications in patients’ lungs, a complication prediction model combining neural memory ordinary differential equation (nmODE) is proposed. The method of this model is as follows: firstly, an extreme gradient boosting (XGBoost) tree structure is used to encode the data and extract its feature importance. Then, a long short-term memory neural network is employed to analyze the dependency of the data’s relevant features and extract the processed features. Finally, by utilizing the memory and learning capabilities of nmODE, the extracted features are deeply analyzed to obtain the final prediction results. Experimental evaluation has demonstrated the effectiveness of the proposed model in the dataset of postoperative complications in the lungs, showing superior performance compared with existing models. Furthermore, it can provide more accurate results for predicting the occurrence of postoperative complications in lung surgery.
References:
[1] SHELLEY B, MARCZIN N. Do we have the ‘power’ to ‘drive’ down the incidence of pulmonary complications after thoracic surgery[J]. British journal of anaesthesia, 2023, 130(1): e37-e40.
[2] 周菲, 焦桂梅, 赵凤兰, 等. Logistic回归的主成分改进方法探讨及其医学应用[J]. 数理医药学杂志, 2014, 27(1): 25-28.
ZHOU Fei, JIAO Guimei, ZHAO Fenglan, et al. The study on the enhancing methods of principal component of logistic regression and their application to medicine[J]. Journal of mathematical medicine, 2014, 27(1): 25-28.
[3] 李河, 郭兰, 孙家珍. COX回归模型在临床医学科研中的价值[J]. 循证医学, 2011, 11(1): 51-53, 59.
LI He, GUO Lan, SUN Jiazhen. The values of COX regression model in the clinical medicine research[J]. The journal of evidence-based medicine, 2011, 11(1): 51-53, 59.
[4] CHEN Tianqi, GUESTRIN C. XGBoost: a scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: ACM, 2016: 785-794.
[5] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural computation, 1997, 9: 1735-1780.
[6] YI Zhang. nmODE: neural memory ordinary differential equation[J]. Artificial intelligence review, 2023, 56(12): 14403-14438.
[7] CLOYD J M, CHEN J, MA Yifei, et al. Association between weekend discharge and hospital readmission rates following major surgery[J]. JAMA surgery, 2015, 150(9): 849-856.
[8] MONTAIGNE D, MARECHAL X, MODINE T, et al. Daytime variation of perioperative myocardial injury in cardiac surgery and its prevention by Rev-Erbα antagonism: a single-centre propensity-matched cohort study and a randomised study[J]. The lancet, 2018, 391(10115): 59-69.
[9] 瞿冲, 王式功, 李河利, 等. 贵阳地区气候季节变化对心脑血管发病人数占比的影响研究[J]. 沙漠与绿洲气象, 2023, 17(6): 23-31.
QU Chong, WANG Shigong, LI Heli, et al. The influence of seasonal changes of climate on the proportion of cardiovascular and cerebrovascular patients in Guiyang City[J]. Desert and oasis meteorology, 2023, 17(6): 23-31.
[10] 马亮, 严海琳, 肖雪, 等. 季节及气温对缺血性心脏病患者消化道出血的影响研究[J]. 中国全科医学, 2022, 25(21): 2589-2596.
MA Liang, YAN Hailin, XIAO Xue, et al. Effects of season and temperature on gastrointestinal bleeding in patients with ischemic heart disease[J]. Chinese general practice, 2022, 25(21): 2589-2596.
[11] 唐圣辉, 王宇清. 儿童呼吸道合胞病毒感染与气候因素的关系研究[J]. 儿科药学杂志, 2013, 19(5): 1-3.
TANG Shenghui, WANG Yuqing. The relationship between meteorological conditions and respiratory syncytial virus infection in hospitalized children[J]. Journal of pediatric pharmacy, 2013, 19(5): 1-3.
[12] RAMAEKERS K, KEYAERTS E, RECTOR A, et al. Prevalence and seasonality of six respiratory viruses during five consecutive epidemic seasons in Belgium[J]. Journal of clinical virology, 2017, 94: 72-78.
[13] 赖玉田, 苏建华, 杨梅, 等. 术前短期综合肺康复训练对肺癌合并轻中度慢性阻塞性肺病患者的影响: 一项前瞻性随机对照试验[J]. 中国肺癌杂志, 2016, 19(11): 746-753.
LAI Yutian, SU Jianhua, YANG Mei, et al. Impact and effect of preoperative short-term pulmonary rehabilitation training on lung cancer patients with mild to moderate chronic obstructive pulmonary disease: a randomized trial[J]. Chinese journal of lung cancer, 2016, 19(11): 746-753.
[14] LAN G, YOSHUA B, AARON C. Deep learning[M]. Cambridge: MIT Press, 2016.
[15] KADRA A, LINDAUER M, HUTTER F, et al. Well-tuned simple nets excel on tabular datasets[J]. Advances in neural information processing systems, 2021, 34: 23928-23941.
[16] KATZIR L, ELIDAN G, EL-YANIV R. Net-dnf: effective deep modeling of tabular data[C]//The Eighth International Conference on Learning Representations. Virtual Only: ICLR, 2020.
[17] RICKY T Q, CHEN Yulia, RUBANOVA J B, et al. NODE: neural ordinary differential equations for tabular data[J]. Advances in neural information processing systems, 2022, 32: 7537-7547.
[18] LUO Haoran, CHENG Fan, YU Heng, et al. SDTR: soft decision tree regressor for tabular data[J]. IEEE access, 2021, 9: 55999-56011.
[19] ARIK S ?, PFISTER T. TabNet: attentive interpretable tabular learning[C]//Proceedings of the AAAI conference on artificial intelligence. New York: AAAI press, 2021, 35(8): 6679-6687.
[20] SARKAR T. XBNet: an extremely boosted neural network[J]. Intelligent systems with applications, 2022, 15: 200097.
[21] KE Guolin, XU Zhenhui, ZHANG Jia, et al. DeepGBM: a deep learning framework distilled by GBDT for online prediction tasks[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Anchorage: ACM, 2019: 384-394.
[22] WAN A, DUNLAP L, HO D, et al. NBDT: neural-backed decision trees[EB/OL]. (2020-04-01)[2024-01-04]. https://arxiv.org/abs/2004.00221v3.
[23] FRIEDMAN J H. Greedy function approximation: a gradient boosting machine[J]. The annals of statistics, 2001, 29(5): 1189-1232.
[24] FREUND Y, SCHAPIRE R E. A decision-theoretic generalization of on-line learning and an application to boosting[J]. Journal of computer and system sciences, 1997, 55(1): 119-139.
[25] BREIMAN L. Random forests[J]. Machine learning, 2001, 45: 5-32.
[26] KE Guolin, MENG Qi, FINLEY T, et al. LightGBM: a highly efficient gradient boosting decision tree[J]. Advances in neural information processing systems, 2017, 30: 3815895.
[27] MYLES A J, FEUDALE R N, LIU Yang, et al. An introduction to decision tree modeling[J]. Journal of chemometrics, 2004, 18(6): 275-285.
[28] CHEN R T Q, AMOS B, NICKEL M. Learning neural event functions for ordinary differential equations[EB/OL]. (2020-11-08)[2024-01-04]. https://arxiv.org/abs/2011.03902v4.
[29] DUPONT E, DOUCET A, TEH Y. Augmented neural ODEs[EB/OL]. (2019-04-02)[2024-01-04]. https://doi.org/10.48550/arXiv.1904.01681.
[30] CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of artificial intelligence research, 2002, 16: 321-357.
Similar References:

Memo

-

Last Update: 2025-01-05

Copyright © CAAI Transactions on Intelligent Systems