More Web Proxy on the site http://driver.im/

research-article

Machine Learning-based Prediction of Postoperative 30-days Mortality

Authors:

Li LuAuthors Info & Claims

CSAE '21: Proceedings of the 5th International Conference on Computer Science and Application Engineering

Article No.: 55, Pages 1 - 9

https://doi.org/10.1145/3487075.3487130

Published: 07 December 2021 Publication History

Abstract

Surgical patients aged 65 and over are facing a 2-10 times higher risk of death after surgery. Early prediction of postoperative mortality is essential, as timely and appropriate treatment can improve survival outcomes. With the development of medical and computer technology, numerous available health-related data can be recorded for research. Among various patient indicators which may affect the accuracy of prediction, it is necessary to find highly relevant and efficient features. The aims of this study were to use machine learning algorithms, specifically Bagging and Boosting Algorithms (e.g. Random Forest, eXtreme Gradient Boosting), to predict the postoperative 30-days mortality in surgical patients aged over 65, and to identify the optimal features using genetic algorithm(GA). This prospective study was developed and validated on the cohort from electronic health records (EHRs) of West China Hospital, Sichuan University, which contained 7467 surgical patients (0.924% mortality rate) who underwent surgery between July 1, 2019 and October 31, 2020. Compared with models like the traditional logistic regression model and the baseline ASA physical status, We found that XGBoost with hyper-parameters had best performance based solely on the automatically obtained features (area under the curve [AUC] of 0.9318, 95% confidence interval [CI] 0.9041 - 0.9594). The AUC of baseline ASA-PS was 0.6787 (95% CI 0.6471 - 0.7103) using XGBoost. When both ASA-PS and the selected features are included as inputs, XGboost achieved the AUC of 0.9345 (95% CI 0.9076 - 0.9613).

References

[1]

Meara, J.G., (2015). Global Surgery 2030: evidence and solutions for achieving health, welfare, and economic development. The Lancet, 386(9993): p. 569-624.

[2]

Nepogodiev, D., (2019). Global burden of postoperative death. The Lancet, 93(10170).

[3]

GBD 2016 Causes of Death Collaborators (2017). Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 2017; 390: 1151–210.

[4]

Pearse RM, Harrison DA, James P, Watson D, Hinds C, Rhodes A, Grounds RM, Bennett ED (2006). Identification and characterisation of the high-risk surgical population in the United Kingdom. Crit Care. 10(3):R81. Epub 2006 Jun 2. 16749940; PMCID: PMC1550954.

[5]

Chiew C J, Liu N, Wong T H, (2020). Utilizing machine learning methods for preoperative prediction of postsurgical mortality and intensive care unit admission[J]. Annals of surgery, 272(6): 1133.

[6]

Revenig, L.M., Ogan, K., Guzzo, T.J. (2014). The Use of Frailty as a Surgical Risk Assessment Tool in Elderly Patients. Curr Geri Rep 3, 1-7.

[7]

Gilbert T, Neuburger J, Kraindler J, (2018). Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study[J]. The Lancet, 391(10132): 1775-1782.

[8]

Preeya K. Mistry, Geoffrey S. Gaunay, David M. Hoenig (2017). Prediction of surgical complications in the elderly: Can we improve outcomes?,Asian Journal of Urology, Volume 4, Issue 1, Pages 44-49,ISSN 2214-3882.

[9]

Aggarwal, G.; Broughton, K.J.; Williams, L.J.; Peden, C.J.; Quiney, N (2020). Early Postoperative Death in Patients Undergoing Emergency High-Risk Surgery: Towards a Better Understanding of Patients for Whom Surgery May not Be Beneficial. J. Clin. Med. 9, 1288.

[10]

Naito T, Mitsunaga S, Miura S, (2019). Feasibility of early multimodal interventions for elderly patients with advanced pancreatic and non‐small‐cell lung cancer[J]. Journal of cachexia, sarcopenia and muscle, 2019, 10(1): 73-83.

[11]

Han J, Kamber M, Pei J (2011). Data mining concepts and techniques third edition[J]. The Morgan Kaufmann Series in Data Management Systems, 5(4): 83-124.

[12]

Witten I H, Frank E (2002). Data mining: practical machine learning tools and techniques with Java implementations[J]. Acm Sigmod Record, 31(1): 76-77.

Digital Library

[13]

Jordan M I, Mitchell T M. Machine learning: Trends, perspectives, and prospects[J]. Science, 2015, 349(6245): 255-260.

[14]

Babatunde O H, Armstrong L, Leng J, (2014). A genetic algorithm-based feature selection[J].

[15]

Etzioni D A, Liu J H, Maggard M A, (2003). The aging population and its impact on the surgery workforce[J]. Annals of surgery, 238(2): 170.

[16]

The International Surgical Outcomes Study group, Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries, BJA: British Journal of Anaesthesia, Volume 117, Issue 5, November 2016, Pages 601–609, https://doi.org/10.1093/bja/aew316.

[17]

Glance L G, Lustik S J, Hannan E L, (2012). The Surgical Mortality Probability Model: derivation and validation of a simple risk prediction rule for noncardiac surgery[J]. Annals of surgery, 255(4): 696-702.

[18]

Le Manach Y, Collins G, Rodseth R, (2016). Preoperative score to predict postoperative mortality (POSPOM). Anesthesiology, 124: 570e9.

[19]

Protopapa K L, Simpson J C, Smith N C E, (2014). Development and validation of the surgical outcome risk tool (SORT)[J]. The British journal of surgery, 101(13): 1774.

[20]

Charlson M, Szatrowski T P, Peterson J, (1994). Validation of a combined comorbidity index[J]. Journal of clinical epidemiology, 47(11): 1245-1251.

[21]

Bilimoria K Y, Liu Y, Paruch J L, (2013). Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons[J]. Journal of the American College of Surgeons, 217(5): 833-842. e3.

[22]

Cohen M E, Bilimoria K Y, Ko C Y, (2009). Effect of subjective preoperative variables on risk-adjusted assessment of hospital morbidity and mortality[J]. Annals of surgery, 249(4): 682-689.

[23]

Hill B L, Brown R, Gabel E, (2019). An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data[J]. British journal of anaesthesia, 123(6): 877-886.

[24]

Pirracchio R, Petersen M L, Carone M, (2015). Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study[J]. The Lancet Respiratory Medicine, 3(1): 42-52.

[25]

Guyon I, Elisseeff A (2003). An introduction to variable and feature selection[J]. Journal of machine learning research, 3(Mar): 1157-1182.

Digital Library

[26]

Mao Y, Yang Y (2019). A wrapper feature subset selection method based on randomized search and multilayer structure[J]. BioMed research international, 2019.

[27]

WHO. International Statistical Classification of Diseases and Related Health Problems-10th Revision[M]. 1993.

[28]

Haldun Akoglu (2018). User's guide to correlation coefficients,Turkish Journal of Emergency Medicine,Volume 18, Issue 3, Pages 91-93, ISSN 2452-2473.

[29]

Mazumder R, Hastie T, Tibshirani R (2010). Spectral regularization algorithms for learning large incomplete matrices[J]. The Journal of Machine Learning Research, 11: 2287-2322.

Digital Library

[30]

Chawla N V, Bowyer K W, Hall L O, (2002). SMOTE: synthetic minority over-sampling technique[J]. Journal of artificial intelligence research, 16: 321-357.

[31]

Lemaître G, Nogueira F, Aridas C K (2017). Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning[J]. The Journal of Machine Learning Research, 18(1): 559-563.

Digital Library

[32]

B. Xue, M. Zhang, W. N. Browne and X. Yao (2016). A Survey on Evolutionary Computation Approaches to Feature Selection," in IEEE Transactions on Evolutionary Computation, vol. 20, no. 4, pp. 606-626.

Digital Library

[33]

Chandrashekar G, Sahin F (2014). A survey on feature selection methods[J]. Computers & Electrical Engineering, 40(1): 16-28.

Digital Library

[34]

Golberg D E (1989). Genetic algorithms in search, optimization, and machine learning[J]. Addion wesley, 1989(102): 36.

[35]

Yang J, Honavar V (1998). Feature subset selection using a genetic algorithm[M]//Feature extraction, construction and selection. Springer, Boston, MA, 117-136.

[36]

Anbarasi M, Anupriya E, Iyengar N (2010). Enhanced prediction of heart disease with feature subset selection using genetic algorithm[J]. International Journal of Engineering Science and Technology, 2(10): 5370-5376.

[37]

Quinlan J R (1986). Induction of decision trees[J]. Machine learning, 1(1): 81-106.

[38]

Fortin F A, De Rainville F M, Gardner M A G, (2012). DEAP: Evolutionary algorithms made easy[J]. The Journal of Machine Learning Research, 13(1): 2171-2175.

Digital Library

[39]

Guyon I, Weston J, Barnhill S, (2002). Gene selection for cancer classification using support vector machines[J]. Machine learning, 2002, 46(1): 389-422.

[40]

Breiman L (2001). Random forests[J]. Machine learning, 2001, 45(1): 5-32.

[41]

Chen T, Guestrin C (2016). Xgboost: A scalable tree boosting system[C]//Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 785-794.

Digital Library

[42]

Hastie T, Tibshirani R, Friedman J (2009). The elements of statistical learning: data mining, inference, and prediction[M]. Springer Science & Business Media.

[43]

Pedregosa F, Varoquaux G, Gramfort A, (2011). Scikit-learn: Machine learning in Python[J]. the Journal of machine Learning research, 12: 2825-2830.

Cited By

Periyasamy KKaivelikkal AIyer V(2022)A Predictor Generator for Healthcare Applications2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)10.1109/ICECCME55909.2022.9988351(1-8)Online publication date: 16-Nov-2022
https://doi.org/10.1109/ICECCME55909.2022.9988351

Index Terms

Machine Learning-based Prediction of Postoperative 30-days Mortality
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning

Index terms have been assigned to the content through auto-classification.

Recommendations

The Impact of Functional Dependence and Related Surgical Complications on Postoperative Mortality
AbstractPurpose
Functional dependency is a known determinant of surgical risk. To enhance our understanding of the relationship between dependency and adverse surgical outcomes, we studied how postoperative mortality following a surgical complication was ...
Mortality Prediction of ICU patients using Machine Leaning: A survey
ICCDA '17: Proceedings of the International Conference on Compute and Data Analysis

Recently health care researchers are working a lot on outcome prediction on Intensive Care Unit (ICU) and trauma. Outcome prediction in intensive care is a difficult process. Accurate synthesis of quality data and application of prior experience to the ...
Machine learning-based prediction of in-hospital mortality for post cardiovascular surgery patients admitting to intensive care unit: a retrospective observational cohort study based on a large multi-center critical care database
Highlights
- The machine learning (ML) models outperformed the traditional models predicting mortality in post cardiovascular surgery patients (PCS) admitted to intensive ...
Abstract Background and objectives
The acute physiology and chronic health evaluation-IV model (APACHE-IV), and the sequential organ failure assessment (SOFA) score are two traditional severity assessment systems that can be applied ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

CSAE '21: Proceedings of the 5th International Conference on Computer Science and Application Engineering

October 2021

660 pages

ISBN:9781450389853

DOI:10.1145/3487075

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 December 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

Sichuan Province Key Research and Development Plan
National Key R&D Program of China

Conference

CSAE 2021

CSAE 2021: The 5th International Conference on Computer Science and Application Engineering

October 19 - 21, 2021

Sanya, China

Acceptance Rates

Overall Acceptance Rate 368 of 770 submissions, 48%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
65
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)1

Reflects downloads up to 12 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Periyasamy KKaivelikkal AIyer V(2022)A Predictor Generator for Healthcare Applications2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)10.1109/ICECCME55909.2022.9988351(1-8)Online publication date: 16-Nov-2022
https://doi.org/10.1109/ICECCME55909.2022.9988351

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents