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Tytuł pozycji:

A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction.

Tytuł:
A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarction.
Autorzy:
Zhang Z; School of Computer Science and Engineering, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, Sichuan, PR China.; Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.
Qiu H; School of Computer Science and Engineering, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731, Chengdu, Sichuan, PR China. .; Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China. .
Li W; Cardiology Division, West China Hospital, Sichuan University, No.17 People's South Road,Chengdu, 610041, Chengdu, Sichuan, PR China.; West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Chen Y; Cardiology Division, West China Hospital, Sichuan University, No.17 People's South Road,Chengdu, 610041, Chengdu, Sichuan, PR China. .; West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China. .
Źródło:
BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2020 Dec 14; Vol. 20 (1), pp. 335. Date of Electronic Publication: 2020 Dec 14.
Typ publikacji:
Journal Article; Research Support, Non-U.S. Gov't
Język:
English
Imprint Name(s):
Original Publication: London : BioMed Central, [2001-
MeSH Terms:
Decision Support Techniques*
Patient Readmission*
Myocardial Infarction/*therapy
Clinical Decision-Making ; Humans ; Models, Theoretical ; Myocardial Infarction/diagnosis ; Myocardial Infarction/epidemiology ; Patient Discharge ; Risk Assessment ; Risk Factors ; Support Vector Machine ; Time Factors ; Treatment Outcome
References:
Accid Anal Prev. 2019 Jan;122:226-238. (PMID: 30390518)
Lancet. 2017 Jan 14;389(10065):197-210. (PMID: 27502078)
ESC Heart Fail. 2019 Apr;6(2):428-435. (PMID: 30810291)
Comput Methods Programs Biomed. 2018 Nov;166:123-135. (PMID: 30415712)
J Biomed Inform. 2014 Dec;52:418-26. (PMID: 25182868)
J Biomol Struct Dyn. 2006 Dec;24(3):239-42. (PMID: 17054381)
Artif Intell Med. 2015 Oct;65(2):89-96. (PMID: 26363683)
J Stroke Cerebrovasc Dis. 2019 Dec;28(12):104441. (PMID: 31627995)
Epidemiology. 2010 Jan;21(1):128-38. (PMID: 20010215)
IEEE Trans Pattern Anal Mach Intell. 2005 Jun;27(6):957-68. (PMID: 15943426)
Med Care. 2010 Nov;48(11):981-8. (PMID: 20940649)
BMJ Qual Saf. 2013 Dec;22(12):998-1005. (PMID: 23904506)
Int J Med Inform. 2020 Jul;139:104136. (PMID: 32353752)
Circ Cardiovasc Qual Outcomes. 2011 Mar;4(2):243-52. (PMID: 21406673)
BMC Med Inform Decis Mak. 2018 Jun 22;18(1):44. (PMID: 29929496)
JAMA. 2013 Feb 13;309(6):587-93. (PMID: 23403683)
IEEE J Biomed Health Inform. 2020 Feb;24(2):447-456. (PMID: 31484143)
CMAJ. 2010 Apr 6;182(6):551-7. (PMID: 20194559)
Aliment Pharmacol Ther. 2018 Mar;47(6):763-772. (PMID: 29359519)
Am J Cardiol. 2017 Sep 1;120(5):723-728. (PMID: 28728745)
Circ Cardiovasc Qual Outcomes. 2018 Jan;11(1):e003885. (PMID: 29321135)
BMC Med Inform Decis Mak. 2019 Oct 15;19(1):193. (PMID: 31615569)
Sci Total Environ. 2018 May 15;624:661-672. (PMID: 29272835)
Health Care Manag Sci. 2015 Mar;18(1):19-34. (PMID: 24792081)
Ann Intern Med. 2013 Mar 5;158(5 Pt 2):433-40. (PMID: 23460101)
Comput Methods Programs Biomed. 2018 Oct;164:49-64. (PMID: 30195431)
Stud Health Technol Inform. 2017;245:476-480. (PMID: 29295140)
Comput Biol Med. 2010 May;40(5):509-18. (PMID: 20347072)
Can J Cardiol. 2020 Jun;36(6):878-885. (PMID: 32204950)
Circ Cardiovasc Qual Outcomes. 2016 Nov;9(6):629-640. (PMID: 28263938)
Age Ageing. 2012 Nov;41(6):784-9. (PMID: 22644078)
Am J Cardiol. 2017 Nov 15;120(10):1761-1767. (PMID: 28865892)
N Engl J Med. 2009 Apr 2;360(14):1418-28. (PMID: 19339721)
Artif Intell Med. 2016 Sep;72:72-82. (PMID: 27664509)
Sci Rep. 2017 Aug 7;7(1):7402. (PMID: 28784991)
Bioinformatics. 2007 Oct 1;23(19):2507-17. (PMID: 17720704)
J Hosp Med. 2013 Dec;8(12):689-95. (PMID: 24227707)
Grant Information:
71661167005 International National Natural Science Foundation of China; 2018SZ0114, 2019YFS0271 International Key Research and Development Program of Sichuan Province; 2018HXFH023, ZYJC18013 International 1·3·5 Project for Disciplines of Excellence-Clinical Research Incubation Project, West China Hospital, Sichuan University
Contributed Indexing:
Keywords: Acute myocardial infarction; Clinical data; Hospital readmission; Machine learning; Self-adaptive; Stacking-based model learning
Entry Date(s):
Date Created: 20201215 Date Completed: 20210113 Latest Revision: 20210113
Update Code:
20240104
PubMed Central ID:
PMC7734833
DOI:
10.1186/s12911-020-01358-w
PMID:
33317534
Czasopismo naukowe
Background: Acute myocardial infarction (AMI) is a serious cardiovascular disease, followed by a high readmission rate within 30-days of discharge. Accurate prediction of AMI readmission is a crucial way to identify the high-risk group and optimize the distribution of medical resources.
Methods: In this study, we propose a stacking-based model to predict the risk of 30-day unplanned all-cause hospital readmissions for AMI patients based on clinical data. Firstly, we conducted an under-sampling method of neighborhood cleaning rule (NCR) to alleviate the class imbalance and then utilized a feature selection method of SelectFromModel (SFM) to select effective features. Secondly, we adopted a self-adaptive approach to select base classifiers from eight candidate models according to their performances in datasets. Finally, we constructed a three-layer stacking model in which layer 1 and layer 2 were base-layer and level 3 was meta-layer. The predictions of the base-layer were used to train the meta-layer in order to make the final forecast.
Results: The results show that the proposed model exhibits the highest AUC (0.720), which is higher than that of decision tree (0.681), support vector machine (0.707), random forest (0.701), extra trees (0.709), adaBoost (0.702), bootstrap aggregating (0.704), gradient boosting decision tree (0.710) and extreme gradient enhancement (0.713).
Conclusion: It is evident that our model could effectively predict the risk of 30-day all cause hospital readmissions for AMI patients and provide decision support for the administration.
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