Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data
In medicine, one often bases decisions upon a comparative analysis of patient data. In this paper, we build upon this observation and describe similarity-based algorithms to risk stratify patients for major adverse cardiac events. We evolve the traditional approach of comparing patient data in two w...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Association for Computing Machinery
2011
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| Online Access: | http://hdl.handle.net/1721.1/66543 https://orcid.org/0000-0003-0992-0906 |
| _version_ | 1826197811094355968 |
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| author | Syed, Zeeshan Guttag, John V. |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Syed, Zeeshan Guttag, John V. |
| author_sort | Syed, Zeeshan |
| collection | MIT |
| description | In medicine, one often bases decisions upon a comparative analysis of patient data. In this paper, we build upon this observation and describe similarity-based algorithms to risk stratify patients for major adverse cardiac events. We evolve the traditional approach of comparing patient data in two ways. First, we propose similarity-based algorithms that compare patients in terms of their long-term physiological monitoring data. Symbolic mismatch identifies functional units in long-term signals and measures changes in the morphology and frequency of these units across patients. Second, we describe similarity-based algorithms that are unsupervised and do not require comparisons to patients with known outcomes for risk stratification. This is achieved by using an anomaly detection framework to identify patients who are unlike other patients in a population and may potentially be at an elevated risk. We demonstrate the potential utility of our approach by showing how symbolic mismatch-based algorithms can be used to classify patients as being at high or low risk of major adverse cardiac events by comparing their long-term electrocardiograms to that of a large population. We describe how symbolic mismatch can be used in three different existing methods: one-class support vector machines, nearest neighbor analysis, and hierarchical clustering. When evaluated on a population of 686 patients with available long-term electrocardiographic data, symbolic mismatch-based comparative approaches were able to identify patients at roughly a two-fold increased risk of major adverse cardiac events in the 90 days following acute coronary syndrome. These results were consistent even after adjusting for other clinical risk variables. |
| first_indexed | 2024-09-23T10:53:30Z |
| format | Article |
| id | mit-1721.1/66543 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:53:30Z |
| publishDate | 2011 |
| publisher | Association for Computing Machinery |
| record_format | dspace |
| spelling | mit-1721.1/665432022-09-30T23:46:43Z Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data Syed, Zeeshan Guttag, John V. Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Guttag, John V. Guttag, John V. Syed, Zeeshan In medicine, one often bases decisions upon a comparative analysis of patient data. In this paper, we build upon this observation and describe similarity-based algorithms to risk stratify patients for major adverse cardiac events. We evolve the traditional approach of comparing patient data in two ways. First, we propose similarity-based algorithms that compare patients in terms of their long-term physiological monitoring data. Symbolic mismatch identifies functional units in long-term signals and measures changes in the morphology and frequency of these units across patients. Second, we describe similarity-based algorithms that are unsupervised and do not require comparisons to patients with known outcomes for risk stratification. This is achieved by using an anomaly detection framework to identify patients who are unlike other patients in a population and may potentially be at an elevated risk. We demonstrate the potential utility of our approach by showing how symbolic mismatch-based algorithms can be used to classify patients as being at high or low risk of major adverse cardiac events by comparing their long-term electrocardiograms to that of a large population. We describe how symbolic mismatch can be used in three different existing methods: one-class support vector machines, nearest neighbor analysis, and hierarchical clustering. When evaluated on a population of 686 patients with available long-term electrocardiographic data, symbolic mismatch-based comparative approaches were able to identify patients at roughly a two-fold increased risk of major adverse cardiac events in the 90 days following acute coronary syndrome. These results were consistent even after adjusting for other clinical risk variables. National Science Foundation (U.S.) (CAREER award 1054419) 2011-10-24T13:36:08Z 2011-10-24T13:36:08Z 2011-03 2010-11 Article http://purl.org/eprint/type/JournalArticle 1532-4435 1533-7928 http://hdl.handle.net/1721.1/66543 Syed, Zeeshan and John Guttag. "Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data." Journal of Machine Learning Research, 12 (2011) 999-1024. https://orcid.org/0000-0003-0992-0906 en_US http://jmlr.csail.mit.edu/papers/volume12/syed11a/syed11a.pdf Journal of Machine Learning Research Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Association for Computing Machinery MIT Press |
| spellingShingle | Syed, Zeeshan Guttag, John V. Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data |
| title | Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data |
| title_full | Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data |
| title_fullStr | Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data |
| title_full_unstemmed | Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data |
| title_short | Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data |
| title_sort | unsupervised similarity based risk stratification for cardiovascular events using long term time series data |
| url | http://hdl.handle.net/1721.1/66543 https://orcid.org/0000-0003-0992-0906 |
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