Mining with Improved Deep Auto-Encoder for Medical Data Record Analysis Using Feature Representation
Keywords:
data analysis, learning model, medical data representation, clustering, mining dataAbstract
Data mining is the finest technique for extracting knowledge from patient routes. Medical occurrences are intricately organized in event logs and frequently documented using several medical codes. Before applying process mining analysis, labeling these occurrences properly is challenging. This study presents a new method for managing complex events in medical records. Improved deep auto-encoding (IDAE) generates precise labels by grouping similar events in latent space. Also, an explanation is given by decoding the instances that correspond to the generated labels. When tested on simulated events, the method successfully uncovered hidden clusters in sparse binary data and provided precise justification for created labels. Real medical data are used in a case study. The outcomes support the theory's effectiveness in knowledge extraction from complicated event logs depicting patient pathways.
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Mauro AD, Greco M, Grimaldi M. A formal definition of big data based on its essential features. Libr Rev. 2016;65(3):122–35.
Reisman M. EHRs: the challenge of making electronic data usable and interoperable. Pharm Ther. 2017;42(9):572–5.
Yin Y, et al. The Internet of things in healthcare: an overview. J Ind Inf Integr. 2016;1:3–13
Yuan, Z. Ge, B. Huang, and Z. Song, “A probabilistic just-in-time learning framework for soft sensor development with missing data,” IEEE. T. Contr. Syst. T., vol. 25, no. 3, pp. 1124-1132, 2017.
Yuan, Y. Wang, C. Yang, W. Gui, and L. Ye, “Probabilistic density-based regression model for soft sensing of nonlinear industrial processes,” J. Process Contr., vol. 57, pp. 15-25, 2017.
Yuan, C. Ou, Y. Wang, C. Yang, and W. Gui, “Deep quality-related feature extraction for soft sensing modeling: A deep learning approach with hybrid VW-SAE,” Neurocomputing, DOI: 10.1016/j.neucom.2018.11.107, 2020.
Wang, Z. Pan, X. Yuan, C. Yang, and W. Gui, “A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network,” ISA T., vol. 96, pp. 457-467, 2020.
Yuan, L. Li, Y. Wang, C. Yang, and W. Gui, "Deep learning for quality prediction of a nonlinear dynamic process with variable attention-based long short-term memory network," Can. J. Chem. Eng., DOI: 10.1002/cjce.23665, 2019.
Yuan, L. Li, Y. A. W. Shardt, Y. Wang, and C. Yang, “Deep learning with spatiotemporal attention-based LSTM for industrial soft sensor model development,” IEEE. T. Ind. Electron., pp. to be published, 2020.
Liu, C. Yang, Z. Gao, and Y. Yao, “Ensemble deep kernel learning with application to quality prediction in industrial polymerization processes,” Chemometrics. Intell. Lab. Syst, vol. 174, pp. 15-21, 2018.
Wang, B. Gopaluni, J. Chen, and Z. Song, “Deep Learning of Complex Batch Process Data and Its Application on Quality Prediction,” IEEE. T. Ind. Inf., vol. 10.1109/TII.2018.2880968 2019.
Yuan, C. Ou, Y. Wang, C. Yang, and W. Gui, “A novel semi-supervised pre-training strategy for deep networks and its application for quality variable prediction in industrial processes,” Chem. Eng. Sci., vol. 217, pp. 115509, 2020.
Shang, F. Yang, D. Huang, and W. Liu, “Data-driven soft sensor development based on deep learning technique,” J. Process Contr., vol. 24, no. 3, pp. 223-233, 2014.
Yuan, Y. Wang, C. Yang, and W. Gui, “Stacked isomorphic autoencoder based soft analyzer and its application to the sulfur recovery unit,” Inform. Sci., vol. To be published, 2020.
Wang, D. Wu, and X. Yuan, "A two-layer ensemble learning framework for the data-driven soft sensor of the diesel attributes in an industrial hydrocracking process," J. Chemometrics., vol. 33, no. 12, pp. e3185, 2019.
Vandromme, J. Jacques, J. Taillard, A. Hansske, L. Jourdan, and C. Dhaenens, “Extraction and optimization of classification rules for temporal sequences: Application to hospital data,” Knowledge-Based Systems, vol. 122, pp. 148–158, Apr. 2017.
Rav`ı, C. Wong, F. Deligianni, M. Berthelot, J. Andreu-Perez, B. Lo, and G.-Z. Yang, “Deep Learning for Health Informatics,” IEEE Journal of Biomedical and Health Informatics, vol. 21, pp. 4–21, Jan. 2017.
Miotto, F. Wang, S. Wang, X. Jiang, and J. T. Dudley, “Deep learning for healthcare: review, opportunities and challenges,” Briefings in Bioinformatics, vol. 19, pp. 1236–1246, Nov. 2018.
Helm, A. M. Lin, D. Baumgartner, A. C. Lin, and J. Kung, “Towards ¨ the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare,” International Journal of Environmental Research and Public Health, vol. 17, Feb. 2020
Valikodath NG, et al. Agreement of ocular symptom reporting between patient-reported outcomes and medical records. JAMA Ophthalmol. 2017;135(3):225–31
Echaiz JF, et al. Low correlation between self-report and medical record documentation of urinary tract infection symptoms. Am J Infect Control. 2015;43(9):983–6.
Sherubha, "Graph-Based Event Measurement for Analyzing Distributed Anomalies in Sensor Networks”, Sådhanå(Springer), 45:212, https://doi.org/10.1007/s12046-020-01451-w
Sherubha, “An Efficient Network Threat Detection and Classification Method using ANP-MVPS Algorithm in Wireless Sensor Networks”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278-3075, Volume-8 Issue-11, September 2019
Sherubha, “An Efficient Intrusion Detection and Authentication Mechanism for Detecting Clone Attack in Wireless Sensor Networks”, Journal of Advanced Research in Dynamical and Control Systems (JARDCS), Volume 11, issue 5, Pg No. 55-68
Yuan, L. Li, and Y. Wang, “Nonlinear dynamic soft sensor modeling with supervised long short-term memory network,” IEEE. T. Ind. Inf., vol. 16, no. 5, pp. 3168-3176, 2020.
Liao, J. Y. Tang, and X. Zhao, Course drop-out prediction on MOOC platform via clustering and tensor completion, Tsinghua Science and Technology, vol. 24, no. 4, pp. 412–422, 2019.
Luo, and S. S. Zhao, Context-aware social media user sentiment analysis, Tsinghua Science and Technology, vol. 25, no. 4, pp. 528–541, 2020.
J. Chen, Z. Lv, and H. Song, “Design of personnel big data management system based on blockchain,” Future Gener. Comput. Syst., vol. 101, pp. 1122–1129, 2019
C. Zhao, L. Ren, Z. Zhang, and Z. Meng, “Master data management for manufacturing big data: A method of evaluation for data network,” World Wide Web, vol. 23, pp. 1407–1421, 2019
D. Wu, L. Zhu, Q. Lu, and S. Sakr, “HDM: A composable framework for big data processing,” IEEE Trans. Big Data, vol. 4, no. 2, pp. 150–163, Jan. 2018
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