ArticleHealth information science and systems2023
Detection and explanation of anomalies in healthcare data.
Article in Health information science and systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Enhancing healthcare information security through VAE-driven anomaly detection in EHR access patterns.Scientific reports · 2026Article
- Dynamic isolation forest for anomaly detection in post-PCI myocardial infarction patients.Scientific reports · 2026Article
- Fuzz Testing Molecular Representation Using Deep Variational Anomaly Generation.Journal of chemical information and modeling · 2025Article
- A Framework for an Effective Healthy Longevity Clinic.Aging and disease · 2024Review
- Detecting outliers beyond tolerance limits derived from statistical process control in patient-specific quality assurance.Journal of applied clinical medical physics · 2024Article
- Early warning systems for malaria outbreaks in Thailand: an anomaly detection approach.Malaria journal · 2024Article
- Breaking Barriers: Artificial Intelligence Interpreting the Interplay between Mental Illness and Pain as Defined by the International Association for the Study of Pain.Biomedicines · 2023Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.