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Anomaly Detection in Irregular Time Series using Long Short-Term Memory with Attention

Shih, Hanniel
http://orcid.org/0009-0005-0396-6630
http://rave.ohiolink.edu/etdc/view?acc_num=ucin171386599492079

Abstract Details

2023, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
Anomaly Detection in Irregular Time Series is an under-explored topic, especially in the healthcare domain. An example of this is weight entry errors. Erroneous weight records pose significant challenges to healthcare data quality, impacting clinical decision-making and patient safety. Existing studies primarily utilize rule-based methods, achieving an Area Under the Receiver Operating Characteristic Curve (AUROC) ranging from 0.546 to 0.620. This thesis introduces a two-module method, employing bi-directional Long Short-Term Memory (bi-LSTM) with Attention Mechanism, for the prospective detection of anomalous weight entries in electronic health records. The proposed method consists of a predictor and a classifier module, both leveraging bi-LSTM and Attention Mechanism. The predictor module learns the normal pattern of weight changes, and the classifier module identifies anomalous weight entries. The performance of both modules was evaluated, exhibiting a clear superiority over other methods in distinguishing normal from anomalous data points. Notably, the proposed approach achieved an AUROC of 0.986 and a precision of 9.28%, significantly outperforming other methods when calibrated for a similar sensitivity. This study contributes to the field of entry error detection in healthcare data, offering a promising solution for real-time anomaly detection in electronic health records.
Raj Bhatnagar, Ph.D. (Committee Chair)
Danny T. Y. Wu, PhD (Committee Member)
Vikram Ravindra, Ph.D. (Committee Member)
101 p.
Computer Science
Anomaly Detection; Time Series; Attention; bi-LSTM; Deep Learning; Health Informatics

Recommended Citations

Citations

  • Shih, H. (2023). Anomaly Detection in Irregular Time Series using Long Short-Term Memory with Attention [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin171386599492079

    APA Style (7th edition)

  • Shih, Hanniel. Anomaly Detection in Irregular Time Series using Long Short-Term Memory with Attention. 2023. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin171386599492079.

    MLA Style (8th edition)

  • Shih, Hanniel. "Anomaly Detection in Irregular Time Series using Long Short-Term Memory with Attention." Master's thesis, University of Cincinnati, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=ucin171386599492079

    Chicago Manual of Style (17th edition)

ucin171386599492079
55
© 2023, some rights reserved.Creative Commons License Anomaly Detection in Irregular Time Series using Long Short-Term Memory with Attention by Hanniel Shih is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.