research-article

Towards heterogeneous temporal clinical event pattern discovery: a convolutional approach

Authors:

Shahram EbadollahiAuthors Info & Claims

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 453 - 461

https://doi.org/10.1145/2339530.2339605

Published: 12 August 2012 Publication History

Abstract

Large collections of electronic clinical records today provide us with a vast source of information on medical practice. However, the utilization of those data for exploratory analysis to support clinical decisions is still limited. Extracting useful patterns from such data is particularly challenging because it is longitudinal, sparse and heterogeneous. In this paper, we propose a Nonnegative Matrix Factorization (NMF) based framework using a convolutional approach for open-ended temporal pattern discovery over large collections of clinical records. We call the method One-Sided Convolutional NMF (OSC-NMF). Our framework can mine common as well as individual shift-invariant temporal patterns from heterogeneous events over different patient groups, and handle sparsity as well as scalability problems well. Furthermore, we use an event matrix based representation that can encode quantitatively all key temporal concepts including order, concurrency and synchronicity. We derive efficient multiplicative update rules for OSC-NMF, and also prove theoretically its convergence. Finally, the experimental results on both synthetic and real world electronic patient data are presented to demonstrate the effectiveness of the proposed method.

Supplementary Material

JPG File (307_m_talk_11.jpg)

Download
15.61 KB

MP4 File (307_m_talk_11.mp4)

Download
135.21 MB

References

[1]

I. Batal, L. Sacchi, R. Bellazzi, and M. Hauskrecht. A temporal abstraction framework for classifying clinical temporal data. In AMIA, pages 29--33, 2009.

[2]

S. Bengio, F. Pereira, Y. Singer, and D. Strelow. Group sparse coding. In NIPS, 2009.

Digital Library

[3]

B. Cao, D. Shen, J.-T. Sun, X. Wang, Q. Yang, and Z. Chen. Detect and Track Latent Factors with Online Nonnegative Matrix Factorization. In Proceedings of the 20th IJCAI, pages 2689--2694, 2007.

Digital Library

[4]

M. Cooper, T. Liu, and E. Rieffel. Video Segmentation via Temporal Pattern Classification. IEEE TMM, 9(3):610--618, 2007.

Digital Library

[5]

S. de Amo and D. A. Furtado. First-order temporal pattern mining with regular expression constraints. Data & Knowledge Engineering, 62(3):401--420, 2007.

Digital Library

[6]

X. Du, R. Jin, L. Ding, V. E. Lee, and J. H. T. Jr. Migration motif: a spatial - temporal pattern mining approach for financial markets. In KDD, pages 1135--1144, 2009.

Digital Library

[7]

J. Eggert and E. Körner. Sparse coding and nmf. In IJCNN, pages 2529--2533, 2004.

[8]

J. Fails, A. Karlson, L. Shahamat, and B. Shneiderman. A Visual Interface for Multivariate Temporal Data: Finding Patterns of Events across Multiple Histories. In Visual Analytics Science And Technology, 2006 IEEE Symposium On, pages 167--174, 2006.

[9]

C. Févotte and J. Idier. Algorithms for nonnegative matrix factorization with the beta-divergence. arXiv:1010.1763, 2010.

[10]

R. Grosse, R. Raina, H. Kwong, and A. Y. Ng. Shift-invariant Sparse Coding for Audio Classification. In UAI, 2007.

[11]

J. Han, J. Pei, Y. Yin, and R. Mao. Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach. DMKD, 8(1):53--87, 2004.

Digital Library

[12]

P. O. Hoyer. Non-negative matrix factorization with sparseness constraints. JMLR, 5:1457--1469, 2004.

Digital Library

[13]

D. D. Lee and H. S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755):788--791, 1999.

[14]

D. D. Lee and H. S. Seung. Algorithms for Non-negative Matrix Factorization. In NIPS 13, pages 556--562, 2001.

[15]

J. Lin, E. Keogh, S. Lonardi, and B. Chiu. 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, pages 2--11, 2003.

Digital Library

[16]

J. Mairal, F. Bach, J. Ponce, and G. Sapiro. Online Learning for Matrix Factorization and Sparse Coding. Journal of Machine Learning Research, 11:10--60, 2010.

Digital Library

[17]

F. Mörchen and A. Ultsch. Efficient mining of understandable patterns from multivariate interval time series. DMKD, 15(2):181--215, 2007.

Digital Library

[18]

M. Mørup, M. N. Schmidt, and L. K. Hansen. Shift invariant sparse coding of image and music data. Technical report, 2008.

[19]

P. N. E. Nohuddin, F. Coenen, R. Christley, and C. Setzkorn. Detecting temporal pattern and cluster changes in social networks: A study focusing uk cattle movement database. In IFIP Advances in Information and Communication Technology, pages 163--172, 2010.

[20]

G. Norén, J. Hopstadius, A. Bate, K. Star, and I. Edwards. Temporal pattern discovery in longitudinal electronic patient records. DMKD, 20(3):361--387, 2010.

Digital Library

[21]

P. D. O'Grady and B. A. Pearlmutter. Discovering convolutive speech phones using sparseness and non-negativity. In ICA, 2007.

Digital Library

[22]

J. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, and M. Hsu. Mining sequential patterns by pattern-growth: The prefixspan approach. IEEE TKDE, 16(11):1424--1440, 2004.

Digital Library

[23]

C. Plaisant, S. Lam, B. Shneiderman, M. S. Smith, D. Roseman, G. Marchand, M. Gillam, C. Feied, J. Handler, and H. Rappaport. Searching electronic health records for temporal patterns in patient histories: a case study with microsoft amalga. In AMIA, pages 601--605, 2008.

[24]

M. Robert Moskovitch and Y. Shahar. Medical temporal-knowledge discovery via temporal abstraction. In AMIA, pages 452--456, 2009.

[25]

B. R. Shah, J. Drozda, and E. D. Peterson. Leveraging observational registries to inform comparative effectiveness research. American Heart Journal, 160(1):8--15, 2010.

[26]

P. Smaragdis. Non-negative matrix factor deconvolution; extraction of multiple sound sources from monophonic inputs. In ICA, pages 494--499. 2004.

[27]

H. A. Varian. Microeconomic Analysis Third Edition. W.W. Norton and Company, 1992.

[28]

F. Wang, C. Tan, P. Li, and C. König. Efficient document clustering via online nonnegative matrix factorization. In Proceedings of the 11th SDM, 2011.

[29]

T. Wang, C. Plaisant, A. Quinn, R. Stanchak, B. Shneiderman, and S. Murphy. Aligning temporal data by sentinel events: Discovering patterns in electronic health records. In Proc. of ACM Conference on Human Factors in Computing Systems, pages 457--466, 2008.

Digital Library

[30]

M. J. Zaki. Spade: An efficient algorithm for mining frequent sequences. Machine Learning, 42:31--60, 2001.

Digital Library

Cited By

Shahidi FMacdonald MSeitz DBarry RMessier G(2025)Exploring the Preprocessing of a Time-Series Healthcare Administrative Dataset on Deep Learning to Improve PredictionIEEE Access10.1109/ACCESS.2024.352042513(3485-3496)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2024.3520425
Vivek YUmamaheswari KSai Sriharika TNikesh RRupesh G(2024)Breast Cancer Assessment Using a HybridANN-CNN Approach2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)10.1109/IITCEE59897.2024.10468038(1-5)Online publication date: 24-Jan-2024
https://doi.org/10.1109/IITCEE59897.2024.10468038
Rafiei AMoore RJahromi SHajati FKamaleswaran R(2024)Meta-learning in Healthcare: A SurveySN Computer Science10.1007/s42979-024-03166-95:6Online publication date: 12-Aug-2024
https://doi.org/10.1007/s42979-024-03166-9
Show More Cited By

Index Terms

Towards heterogeneous temporal clinical event pattern discovery: a convolutional approach
1. Applied computing
  1. Life and medical sciences
    1. Consumer health
    2. Health informatics
2. Computing methodologies
  1. Machine learning

Recommendations

Statistically sound pattern discovery
KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Pattern discovery is a core data mining activity. Initial approaches were dominated by the frequent pattern discovery paradigm -- only patterns that occur frequently in the data were explored. Having been thoroughly researched and its limitations now ...
Hyperclique pattern discovery

Existing algorithms for mining association patterns often rely on the support-based pruning strategy to prune a combinatorial search space. However, this strategy is not effective for discovering potentially interesting patterns at low levels of ...
A constrained frequent pattern mining system for handling aggregate constraints
IDEAS '12: Proceedings of the 16th International Database Engineering & Applications Sysmposium

Frequent pattern mining searches data for sets of items that are frequently co-occurring together. Most of algorithms find all the frequent patterns. However, there are many real-life situations in which users is interested in only some small portions ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2012

1616 pages

ISBN:9781450314626

DOI:10.1145/2339530

General Chair:
Qiang Yang
Hong Kong University of Science and Technology
,
Program Chairs:
Deepak Agarwal
LinkedIn
,
Jian Pei
Simon Fraser University

Copyright © 2012 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 August 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

KDD '12

Sponsor:

KDD '12: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 12 - 16, 2012

Beijing, China

Acceptance Rates

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Upcoming Conference

KDD '25

Sponsor:
sigkdd
sigkdd

The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

59
Total Citations
View Citations
1,102
Total Downloads

Downloads (Last 12 months)14
Downloads (Last 6 weeks)4

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Shahidi FMacdonald MSeitz DBarry RMessier G(2025)Exploring the Preprocessing of a Time-Series Healthcare Administrative Dataset on Deep Learning to Improve PredictionIEEE Access10.1109/ACCESS.2024.352042513(3485-3496)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2024.3520425
Vivek YUmamaheswari KSai Sriharika TNikesh RRupesh G(2024)Breast Cancer Assessment Using a HybridANN-CNN Approach2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)10.1109/IITCEE59897.2024.10468038(1-5)Online publication date: 24-Jan-2024
https://doi.org/10.1109/IITCEE59897.2024.10468038
Rafiei AMoore RJahromi SHajati FKamaleswaran R(2024)Meta-learning in Healthcare: A SurveySN Computer Science10.1007/s42979-024-03166-95:6Online publication date: 12-Aug-2024
https://doi.org/10.1007/s42979-024-03166-9
Uppara RYadav SKavitha D(2023)Voting Classifier on Ensemble Algorithms for Breast Cancer Prediction2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT)10.1109/IDCIoT56793.2023.10053498(653-660)Online publication date: 5-Jan-2023
https://doi.org/10.1109/IDCIoT56793.2023.10053498
S IB HDevi KK H(2023)Automatic Scikit-learn based detection and classification of Breast Cancer using Machine./ Learning techniques2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)10.1109/ICAECT57570.2023.10117662(1-8)Online publication date: 5-Jan-2023
https://doi.org/10.1109/ICAECT57570.2023.10117662
Chen NRen J(2023)An EHR data quality evaluation approach based on medical knowledge and text matchingIRBM10.1016/j.irbm.2023.100782(100782)Online publication date: Apr-2023
https://doi.org/10.1016/j.irbm.2023.100782
Xu JXi XChen JSheng VMa JCui Z(2022)A Survey of Deep Learning for Electronic Health RecordsApplied Sciences10.3390/app12221170912:22(11709)Online publication date: 17-Nov-2022
https://doi.org/10.3390/app122211709
Munappa SMaremalla PKatupilla MVeeranki HKanuru SJangam EItta S(2022)Novel Approach to Predict the Chances of Breast Cancer by Demographic Data using Machine Learning Algorithms2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS)10.1109/ICICCS53718.2022.9788124(1360-1368)Online publication date: 25-May-2022
https://doi.org/10.1109/ICICCS53718.2022.9788124
Ju RZhou PWen SWei WXue YHuang XYang X(2021)3D-CNN-SPP: A Patient Risk Prediction System From Electronic Health Records via 3D CNN and Spatial Pyramid PoolingIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2019.29604745:2(247-261)Online publication date: Apr-2021
https://doi.org/10.1109/TETCI.2019.2960474
Zeng XLin SLiu C(2021)Multi-View Deep Learning Framework for Predicting Patient Expenditure in HealthcareIEEE Open Journal of the Computer Society10.1109/OJCS.2021.30525182(62-71)Online publication date: 2021
https://doi.org/10.1109/OJCS.2021.3052518
Show More Cited By

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.

Figures

Tables

Media

View Table of Conten