- Title
- Detection and explanation of anomalies in healthcare data
- Creator
- Samariya, Durgesh; Ma, Jiangang; Aryal, Sunil; Zhao, Xiaohui
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/199289
- Identifier
- vital:19195
- Identifier
-
https://doi.org/10.1007/s13755-023-00221-2
- Identifier
- ISSN:2047-2501
- Abstract
- The growth of databases in the healthcare domain opens multiple doors for machine learning and artificial intelligence technology. Many medical devices are available in the medical field however, medical errors remain a severe challenge. Different algorithms are developed to identify and solve medical errors, such as detecting anomalous readings, anomalous health conditions of a patient, etc. However, they fail to answer why those entries are considered an anomaly. This research gap leads to an outlying aspect mining problem. The problem of outlying aspect mining aims to discover the set of features (a.k.a subspace) in which the given data point is dramatically different than others. In this paper, we present a framework that detects anomalies in healthcare data and then provides an explanation of anomalies. This paper aims to effectively and efficiently detect anomalies and explain why they are considered anomalies by detecting outlying aspects. First, we re-introduced four anomaly detection techniques and outlying aspect mining algorithms. Then, we evaluate the performance of anomaly detection techniques and choose the best anomaly detection algorithm. Later, we detect the top k anomaly as a query and detect their outlying aspect. Lastly, we evaluate their performance on 16 real-world healthcare datasets. The experimental results show that the latest isolation-based outlying aspect mining measure, SiNNE, has outstanding performance on this task and has promising results.
- Publisher
- Springer International Publishing
- Relation
- Health Information Science and Systems Vol. 11, no. 1 (2023), p. 20-20
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright Springer Link
- Subject
- Medical informatics; Research; Bioinformatics; Computer science; Errors; Medical instruments and apparatus; Algorithms; Medical errors; Machine learning; Medical electronics; Artificial intelligence; 4203 Health services and systems; 4601 Applied computing
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