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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2019

Open Access 01-12-2019 | Heart Failure | Research article

Representation learning in intraoperative vital signs for heart failure risk prediction

Authors: Yuwen Chen, Baolian Qi

Published in: BMC Medical Informatics and Decision Making | Issue 1/2019

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Abstract

Background

The probability of heart failure during the perioperative period is 2% on average and it is as high as 17% when accompanied by cardiovascular diseases in China. It has been the most significant cause of postoperative death of patients. However, the patient is managed by the flow of information during the operation, but a lot of clinical information can make it difficult for medical staff to identify the information relevant to patient care. There are major practical and technical barriers to understand perioperative complications.

Methods

In this work, we present three machine learning methods to estimate risks of heart failure, which extract intraoperative vital signs monitoring data into different modal representations (statistical learning representation, text learning representation, image learning representation). Firstly, we extracted features of vital signs monitoring data of surgical patients by statistical analysis. Secondly, the vital signs data is converted into text information by Piecewise Approximate Aggregation (PAA) and Symbolic Aggregate Approximation (SAX), then Latent Dirichlet Allocation (LDA) model is used to extract text topics of patients for heart failure prediction. Thirdly, the vital sign monitoring time series data of the surgical patient is converted into a grid image by using the grid representation, and then the convolutional neural network is directly used to identify the grid image for heart failure prediction. We evaluated the proposed methods in the monitoring data of real patients during the perioperative period.

Results

In this paper, the results of our experiment demonstrate the Gradient Boosting Decision Tree (GBDT) classifier achieves the best results in the prediction of heart failure by statistical feature representation. The sensitivity, specificity and the area under the curve (AUC) of the best method can reach 83, 85 and 84% respectively.

Conclusions

The experimental results demonstrate that representation learning model of vital signs monitoring data of intraoperative patients can effectively capture the physiological characteristics of postoperative heart failure.
Appendix
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Literature
1.
Thuraisingham RA. A classification system to detect congestive heart failure using second-order difference plot of RR intervals. Cardiol Res Pract. 2009;2009:807379.CrossRef
2.
Isler Y, Kuntalp M. Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure. Comput Biol Med. 2007;37(10):1502–10.CrossRef
3.
Yu SN, Lee MY. Conditional mutual information-based feature selection for congestive heart failure recognition using heart rate variability. Comput Methods Prog Biomed. 2012;108(1):299–309.CrossRef
4.
Masetic Z, Subasi A. Congestive heart failure detection using random forest classifier. Comput Methods Prog Biomed. 2016;130:54–64.CrossRef
5.
Melillo P, Fusco R, Sansone M, Bracale M, Pecchia L. Discrimination power of long-term heart rate variability measures for chronic heart failure detection. Med Biol Eng Comput. 2011;49(1):67–74.CrossRef
6.
Pecchia L, Melillo P, Sansone M, Bracale M. Discrimination power of short-term heart rate variability measures for CHF assessment. IEEE Trans Inf Technol Biomed. 2011;15(1):40–6.CrossRef
7.
Choi E, Schuetz A, Stewart WF, Sun J. Using recurrent neural network models for early detection of heart failure onset. J Am Med Inform Assoc. 2017;24(2):361–70.PubMed
8.
Koulaouzidis G, Iakovidis DK, Clark AL. Telemonitoring predicts in advance heart failure admissions. Int J Cardiol. 2016;216:78–84.CrossRef
9.
Shameer K, Johnson KW, Yahi A, Miotto R, Li LI, Ricks D, Jebakaran J, Kovatch P, Sengupta PP, Gelijns SJPSB. Predictive modeling of hospital readmission rates using electronic medical record-wide machine learning: a case-study using mount sinai heart failure cohort. Pac Symp Biocomput. 2016;22:276-87.
10.
Zheng B, Zhang J, Yoon SW, Lam SS, Khasawneh M, Poranki S. Predictive modeling of hospital readmissions using metaheuristics and data mining. Expert Syst Appl. 2015;42(20):7110–20.CrossRef
11.
Chen W, Zheng L, Li K, Wang Q, Liu G, Jiang Q. A novel and effective method for congestive heart failure detection and quantification using dynamic heart rate variability measurement. PLoS One. 2016;11(11):e0165304.CrossRef
12.
Churpek MM, Yuen TC, Park SY, Meltzer DO, Hall JB, Edelson DP. Derivation of a cardiac arrest prediction model using ward vital signs*. Crit Care Med. 2012;40(7):2102–8.CrossRef
13.
Fox A, Elliott N: Early warning scores: a sign of deterioration in patients and systems.
14.
Petersen JA, Antonsen K, Rasmussen LS. Frequency of early warning score assessment and clinical deterioration in hospitalized patients: a randomized trial. Resuscitation. 2016;101:91–6.CrossRef
15.
Geurts P. Pattern extraction for time series classification. In, Berlin, Heidelberg. Principles of Data Mining and Knowledge Discovery. Springer Berlin Heidelberg, 2001;115–27.CrossRef
16.
Wei WW. Time series analysis. In: The Oxford Handbook of Quantitative Methods in Psychology: Vol 2; 2006.
17.
Lior R , Oded M. Data mining with decision trees theory and applications[M]. World scientific; 2007.
18.
Quinlan JR. Induction of Decision Trees[J]. Machine Learning, 1986, 1(1):81–106
19.
Ye J, Chow J-H, Chen J, Zheng Z: Stochastic gradient boosted distributed decision trees. In: Proceedings of the 18th ACM conference on Information and knowledge management. ACM, 2009. p. 2061–4.
20.
Friedman JHJCs, analysis d: Stochastic gradient boosting. 2002, 38(4):367–378.CrossRef
21.
22.
Pelikan M, Goldberg DE, Cantú-Paz E. BOA: The Bayesian optimization algorithm. In: Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation-Volume 1. Morgan Kaufmann Publishers Inc., 1999. p. 525–32.
23.
Joachims T: Making large-scale SVM learning practical. In.: Technical report, SFB 475: Komplexitätsreduktion in Multivariaten …; 1998.
24.
25.
Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller P-A: Deep learning for time series classification: a review. Data Mining and Knowledge Discovery. 2019;33)4:917–63.CrossRef
26.
Chen Y, Sun QL, Zhong K: Semi-supervised spatio-temporal CNN for recognition of surgical workflow. EURASIP Journal on Image and Video Processing. 2018;(2018)1:76.
27.
Lin J, Keogh E, Lonardi S, Chiu B: A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery. ACM, 2003. p. 2–11.
28.
Blei DM, Andrew YN, Michael IJ. Latent dirichlet allocation. Journal of machine Learning research. 2003;993–1022.
29.
Ye Y, Jiang J, Ge B, Dou Y, Yang K. Similarity measures for time series data classification using grid representation and matrix distance. Knowl Inf Syst. 2019;(60)2:1105–34.CrossRef
30.
Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Fei-Fei L. Large-Scale Video Classification with Convolutional Neural Networks. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 2014. p. 1725–1732.
31.
Liu S, Deng W: Very deep convolutional neural network based image classification using small training sample size. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR): 3–6 Nov. 2015 2015; 2015: 730–734.
32.
Deng L, Li J, Huang J, Yao K, Yu D, Seide F, Seltzer M, Zweig G, He X, Williams J et al: Recent advances in deep learning for speech research at Microsoft. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing: 26–31 May 2013 2013; 2013: 8604–8608.
33.
Zeiler MD. Fergus RJecocv: visualizing and understanding convolutional. Networks. 2014:818–33.
Metadata
Title
Representation learning in intraoperative vital signs for heart failure risk prediction
Authors
Yuwen Chen
Baolian Qi
Publication date
01-12-2019
Publisher
BioMed Central
Keyword
Heart Failure
Published in
BMC Medical Informatics and Decision Making / Issue 1/2019
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-019-0978-6

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