XAIRE: An ensemble-based methodology for determining the relative importance of variables in regression tasks. Application to a hospital emergency department

Rivas, Antonio Jesús Rivera; Muñoz, J. Cobo; Pérez-Goody, M.D.; Pedro, B. Sáez de San; Charte, Francisco; Elizondo, David; Rodriguez, Camilo; Abolafia, M.L.; Perea, A.; Jesus, María José del

doi:10.1016/j.artmed.2023.102494

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…With the aid of explainable AI (XAI), these methods can be better explained and more transparent to decision-makers. XAI is most frequently applied in industries requiring high decision-making accuracies and accountability levels, such as healthcare (Faust et al, 2023;Rivera et al, 2023;Z. Wang et al, 2023;Zhao, Ren, Zhang, Wu, & Lyu, 2023) and management (Angelotti & Díaz-Rodríguez, 2023;Langer & König, 2023;Lee, Jung, Lee, Kim, & Park, 2021).…”

Section: Introductionmentioning

confidence: 99%

Explainable artificial intelligence (XAI) enabled anomaly detection and fault classification of an industrial asset

Sharma

Mittal

Soni

2023

Preprint

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Predictive maintenance helps organizations to reduce equipment downtime, optimize maintenance schedules, and enhance operational efficiency. By leveraging machine learning algorithms to predict when equipment failure will likely occur, maintenance teams can proactively schedule maintenance activities and prevent unexpected breakdowns. Anomaly detection and fault classification are essential components of predictive maintenance. Anomaly detection involves analyzing sensor data collected from equipment to identify deviations from normal behavior. Fault classification, on the other hand, involves identifying the root cause of a fault or failure. A dataset of an industrial asset is used to evaluate the proposed study. Four distinct data-driven anomaly detection methodologies were employed after the pre-processing of the data, with the deep learning-based autoencoder producing the best results of all the techniques. Implementing machine learning-based fault categorization approaches revealed that Random Forest had the best results. Bayesian optimization and sequential model-based hyperparameter optimization technique is used for greater accuracy and optimized hyperparameters. Significant progress has been made in anomaly detection and fault classification using machine learning, but the degree of their explainability is significantly limited by the ``black-box" character of some machine learning techniques. Less emphasis has been placed on explainable artificial intelligence (XAI) approaches in the domain of maintenance. Therefore, the XAI tools have been used to acknowledge the extent of the variables to analyze the influence of respective features. A stability metric has been included to improve the explanation's overall quality. The findings of this article suggest that the utilization of eXplainable Artificial Intelligence (XAI) can offer significant contributions in terms of insights and solutions for addressing critical maintenance issues. As a result, decision-making processes can become more informed and effective.

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Section: Introductionmentioning

confidence: 99%

Explainable artificial intelligence (XAI) enabled anomaly detection and fault classification of an industrial asset

Sharma

Mittal

Soni

2023

Preprint

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An explainable machine learning approach for hospital emergency department visits forecasting using continuous training and multi-model regression

Peláez-Rodríguez,

Torres-López,

Pérez-Aracil

et al. 2024

Computer Methods and Programs in Biomedicine

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Recent Applications of Explainable AI (XAI): A Systematic Literature Review

Saarela,

Podgorelec

2024

Applied Sciences

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This systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of explainable AI (XAI) over the past three years. From an initial pool of 664 articles identified through the Web of Science database, 512 peer-reviewed journal articles met the inclusion criteria—namely, being recent, high-quality XAI application articles published in English—and were analyzed in detail. Both qualitative and quantitative statistical techniques were used to analyze the identified articles: qualitatively by summarizing the characteristics of the included studies based on predefined codes, and quantitatively through statistical analysis of the data. These articles were categorized according to their application domains, techniques, and evaluation methods. Health-related applications were particularly prevalent, with a strong focus on cancer diagnosis, COVID-19 management, and medical imaging. Other significant areas of application included environmental and agricultural management, industrial optimization, cybersecurity, finance, transportation, and entertainment. Additionally, emerging applications in law, education, and social care highlight XAI’s expanding impact. The review reveals a predominant use of local explanation methods, particularly SHAP and LIME, with SHAP being favored for its stability and mathematical guarantees. However, a critical gap in the evaluation of XAI results is identified, as most studies rely on anecdotal evidence or expert opinion rather than robust quantitative metrics. This underscores the urgent need for standardized evaluation frameworks to ensure the reliability and effectiveness of XAI applications. Future research should focus on developing comprehensive evaluation standards and improving the interpretability and stability of explanations. These advancements are essential for addressing the diverse demands of various application domains while ensuring trust and transparency in AI systems.

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XAIRE: An ensemble-based methodology for determining the relative importance of variables in regression tasks. Application to a hospital emergency department

Cited by 3 publications

References 27 publications

Explainable artificial intelligence (XAI) enabled anomaly detection and fault classification of an industrial asset

Explainable artificial intelligence (XAI) enabled anomaly detection and fault classification of an industrial asset

An explainable machine learning approach for hospital emergency department visits forecasting using continuous training and multi-model regression

Recent Applications of Explainable AI (XAI): A Systematic Literature Review

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