Anomaly Detection in COVID-19 Time-Series Data
Anomaly detection and explanation in big volumes of real-world medical data, such as those pertaining to COVID-19, pose some challenges. First, we are dealing with time-series data. Typical time-series data describe behavior of a single object over time. In medical data, we are dealing with time-ser...
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| Veröffentlicht in: | SN computer science Jg. 2; H. 4; S. 279 |
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01.07.2021
Springer Nature B.V |
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| Abstract | Anomaly detection and explanation in big volumes of real-world medical data, such as those pertaining to COVID-19, pose some challenges. First, we are dealing with time-series data. Typical time-series data describe behavior of a single object over time. In medical data, we are dealing with time-series data belonging to multiple entities. Thus, there may be multiple subsets of records such that records in each subset, which belong to a single entity are temporally dependent, but the records in different subsets are unrelated. Moreover, the records in a subset contain different types of attributes, some of which must be grouped in a particular manner to make the analysis meaningful. Anomaly detection techniques need to be customized for time-series data belonging to multiple entities. Second, anomaly detection techniques fail to explain the cause of outliers to the experts. This is critical for new diseases and pandemics where current knowledge is insufficient. We propose to address these issues by extending our existing work called IDEAL, which is an LSTM-autoencoder based approach for data quality testing of sequential records, and provides explanations of constraint violations in a manner that is understandable to end-users. The extension (1) uses a novel two-level reshaping technique that splits COVID-19 data sets into multiple temporally-dependent subsequences and (2) adds a data visualization plot to further explain the anomalies and evaluate the level of abnormality of subsequences detected by IDEAL. We performed two systematic evaluation studies for our anomalous subsequence detection. One study uses aggregate data, including the number of cases, deaths, recovered, and percentage of hospitalization rate, collected from a COVID tracking project, New York Times, and Johns Hopkins for the same time period. The other study uses COVID-19 patient medical records obtained from Anschutz Medical Center health data warehouse. The results are promising and indicate that our techniques can be used to detect anomalies in large volumes of real-world unlabeled data whose accuracy or validity is unknown. |
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| AbstractList | Anomaly detection and explanation in big volumes of real-world medical data, such as those pertaining to COVID-19, pose some challenges. First, we are dealing with time-series data. Typical time-series data describe behavior of a single object over time. In medical data, we are dealing with time-series data belonging to multiple entities. Thus, there may be multiple subsets of records such that records in each subset, which belong to a single entity are temporally dependent, but the records in different subsets are unrelated. Moreover, the records in a subset contain different types of attributes, some of which must be grouped in a particular manner to make the analysis meaningful. Anomaly detection techniques need to be customized for time-series data belonging to multiple entities. Second, anomaly detection techniques fail to explain the cause of outliers to the experts. This is critical for new diseases and pandemics where current knowledge is insufficient. We propose to address these issues by extending our existing work called IDEAL, which is an LSTM-autoencoder based approach for data quality testing of sequential records, and provides explanations of constraint violations in a manner that is understandable to end-users. The extension (1) uses a novel two-level reshaping technique that splits COVID-19 data sets into multiple temporally-dependent subsequences and (2) adds a data visualization plot to further explain the anomalies and evaluate the level of abnormality of subsequences detected by IDEAL. We performed two systematic evaluation studies for our anomalous subsequence detection. One study uses aggregate data, including the number of cases, deaths, recovered, and percentage of hospitalization rate, collected from a COVID tracking project, New York Times, and Johns Hopkins for the same time period. The other study uses COVID-19 patient medical records obtained from Anschutz Medical Center health data warehouse. The results are promising and indicate that our techniques can be used to detect anomalies in large volumes of real-world unlabeled data whose accuracy or validity is unknown. Anomaly detection and explanation in big volumes of real-world medical data, such as those pertaining to COVID-19, pose some challenges. First, we are dealing with time-series data. Typical time-series data describe behavior of a single object over time. In medical data, we are dealing with time-series data belonging to multiple entities. Thus, there may be multiple subsets of records such that records in each subset, which belong to a single entity are temporally dependent, but the records in different subsets are unrelated. Moreover, the records in a subset contain different types of attributes, some of which must be grouped in a particular manner to make the analysis meaningful. Anomaly detection techniques need to be customized for time-series data belonging to multiple entities. Second, anomaly detection techniques fail to explain the cause of outliers to the experts. This is critical for new diseases and pandemics where current knowledge is insufficient. We propose to address these issues by extending our existing work called IDEAL, which is an LSTM-autoencoder based approach for data quality testing of sequential records, and provides explanations of constraint violations in a manner that is understandable to end-users. The extension (1) uses a novel two-level reshaping technique that splits COVID-19 data sets into multiple temporally-dependent subsequences and (2) adds a data visualization plot to further explain the anomalies and evaluate the level of abnormality of subsequences detected by IDEAL. We performed two systematic evaluation studies for our anomalous subsequence detection. One study uses aggregate data, including the number of cases, deaths, recovered, and percentage of hospitalization rate, collected from a COVID tracking project, New York Times, and Johns Hopkins for the same time period. The other study uses COVID-19 patient medical records obtained from Anschutz Medical Center health data warehouse. The results are promising and indicate that our techniques can be used to detect anomalies in large volumes of real-world unlabeled data whose accuracy or validity is unknown.Anomaly detection and explanation in big volumes of real-world medical data, such as those pertaining to COVID-19, pose some challenges. First, we are dealing with time-series data. Typical time-series data describe behavior of a single object over time. In medical data, we are dealing with time-series data belonging to multiple entities. Thus, there may be multiple subsets of records such that records in each subset, which belong to a single entity are temporally dependent, but the records in different subsets are unrelated. Moreover, the records in a subset contain different types of attributes, some of which must be grouped in a particular manner to make the analysis meaningful. Anomaly detection techniques need to be customized for time-series data belonging to multiple entities. Second, anomaly detection techniques fail to explain the cause of outliers to the experts. This is critical for new diseases and pandemics where current knowledge is insufficient. We propose to address these issues by extending our existing work called IDEAL, which is an LSTM-autoencoder based approach for data quality testing of sequential records, and provides explanations of constraint violations in a manner that is understandable to end-users. The extension (1) uses a novel two-level reshaping technique that splits COVID-19 data sets into multiple temporally-dependent subsequences and (2) adds a data visualization plot to further explain the anomalies and evaluate the level of abnormality of subsequences detected by IDEAL. We performed two systematic evaluation studies for our anomalous subsequence detection. One study uses aggregate data, including the number of cases, deaths, recovered, and percentage of hospitalization rate, collected from a COVID tracking project, New York Times, and Johns Hopkins for the same time period. The other study uses COVID-19 patient medical records obtained from Anschutz Medical Center health data warehouse. The results are promising and indicate that our techniques can be used to detect anomalies in large volumes of real-world unlabeled data whose accuracy or validity is unknown. |
| ArticleNumber | 279 |
| Author | Ray, Indrakshi Kahn, Michael G. Ghosh, Sudipto Gondalia, Shlok Homayouni, Hajar |
| Author_xml | – sequence: 1 givenname: Hajar surname: Homayouni fullname: Homayouni, Hajar organization: Computer Science Department, Colorado State University – sequence: 2 givenname: Indrakshi surname: Ray fullname: Ray, Indrakshi organization: Computer Science Department, Colorado State University – sequence: 3 givenname: Sudipto orcidid: 0000-0001-6000-9646 surname: Ghosh fullname: Ghosh, Sudipto email: ghosh@colostate.edu organization: Computer Science Department, Colorado State University – sequence: 4 givenname: Shlok surname: Gondalia fullname: Gondalia, Shlok organization: Computer Science Department, Colorado State University – sequence: 5 givenname: Michael G. surname: Kahn fullname: Kahn, Michael G. organization: Anschutz Medical Campus, University of Colorado Denver |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34027432$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/978-3-030-55180-3_45 10.1109/ACCESS.2018.2870151 10.1016/j.neucom.2017.08.026 10.1016/j.jcrc.2020.03.005 10.1016/j.scs.2017.08.009 10.1144/geochem2016-024 10.1109/LRA.2018.2801475 10.3390/ijerph17155330 10.1016/S0140-6736(20)30526-2 10.1093/jamia/ocaa071 10.1016/j.envsoft.2018.06.004 10.1162/neco_a_01199 10.1109/ACCESS.2020.3022366 10.1007/s10100-017-0479-6 10.1007/s13347-017-0293-z 10.1111/j.1751-5823.2011.00148.x 10.1145/3097983.3098052 10.1109/BigData50022.2020.9378192 10.1109/SIEDS.2019.8735594 10.1109/CCECE.2018.8447597 10.1109/MDM.2018.00029 10.1109/PHM-Paris.2019.00055 10.1007/978-1-4842-3516-4_9 10.1109/ICDMW.2015.104 10.1007/s10489-020-01902-1 10.1007/978-981-15-3992-3_42 10.1101/2020.07.06.20147512 10.1101/2020.09.02.20186502 10.1145/2783258.2788611 10.1109/ICCIKE47802.2019.9004325 10.1080/10618600.2019.1617160 10.1155/2020/6152041 10.1093/oso/9780198538493.001.0001 10.1145/335191.335388 10.1109/DEPCOS-RELCOMEX.2006.38 10.1109/DSAA.2016.92 10.1109/IRI.2019.00023 10.1109/BigData47090.2019.9006446 10.1145/3338840.3355641 |
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| Keywords | COVID-19 data LSTM-autoencoder Explainability Anomaly detection Time series Data quality tests |
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| SubjectTerms | Anomalies Artificial Intelligence for HealthCare Computer Imaging Computer Science Computer Systems Organization and Communication Networks COVID-19 Data analysis Data Structures and Information Theory Datasets Epidemics Health care facilities Information Systems and Communication Service Machine learning Original Research Outliers (statistics) Pattern Recognition and Graphics Quality control Scientific visualization Software Engineering/Programming and Operating Systems Subject specialists Time series Vision Visualization |
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| Title | Anomaly Detection in COVID-19 Time-Series Data |
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