Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review

Madanu, Ravichandra; Abbod, Maysam F.; Hsiao, Fu Jung; Chen, Wei–Ta; Shieh, Jiann-Shing

doi:10.3390/technologies10030074

Cited by 12 publications

(6 citation statements)

References 74 publications

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“…XAI methods are broadly used in many applications association with healthcare, especially pain modeling [ 52 ]. The utilization of AI in healthcare has reduced the burden on the biomedical system exclusively.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Human-Centered Explainable Artificial Intelligence: Automotive Occupational Health Protection Profiles in Prevention Musculoskeletal Symptoms

Mollaei

Fujão²,

Silva

et al. 2022

IJERPH

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In automotive and industrial settings, occupational physicians are responsible for monitoring workers’ health protection profiles. Workers’ Functional Work Ability (FWA) status is used to create Occupational Health Protection Profiles (OHPP). This is a novel longitudinal study in comparison with previous research that has predominantly relied on the causality and explainability of human-understandable models for industrial technical teams like ergonomists. The application of artificial intelligence can support the decision-making to go from a worker’s Functional Work Ability to explanations by integrating explainability into medical (restriction) and support in contexts of individual, work-related, and organizational risk conditions. A sample of 7857 for the prognosis part of OHPP based on Functional Work Ability in the Portuguese language in the automotive industry was taken from 2019 to 2021. The most suitable regression models to predict the next medical appointment for the workers’ body parts protection were the models based on CatBoost regression, with an RMSLE of 0.84 and 1.23 weeks (mean error), respectively. CatBoost algorithm is also used to predict the next body part severity of OHPP. This information can help our understanding of potential risk factors for OHPP and identify warning signs of the early stages of musculoskeletal symptoms and work-related absenteeism.

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Section: Experiments and Discussionmentioning

confidence: 99%

Human-Centered Explainable Artificial Intelligence: Automotive Occupational Health Protection Profiles in Prevention Musculoskeletal Symptoms

Mollaei

Fujão²,

Silva

et al. 2022

IJERPH

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“…In [ 27 ], the authors analyze the explainable AI (XAI) as increased attention, and deployed for automatic assessment of pain. The authors review literature for identifying the pain-scaling approaches and their application to chest pain, shoulder pain, and chest pain, to name a few.…”

Section: Related Workmentioning

confidence: 99%

XAI–reduct: accuracy preservation despite dimensionality reduction for heart disease classification using explainable AI

Das

Sultana²,

Bhattacharya³

et al. 2023

J Supercomput

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Machine learning (ML) has been used for classification of heart diseases for almost a decade, although understanding of the internal working of the black boxes, i.e., non-interpretable models, remain a demanding problem. Another major challenge in such ML models is the curse of dimensionality leading to resource intensive classification using the comprehensive set of feature vector (CFV). This study focuses on dimensionality reduction using explainable artificial intelligence, without negotiating on accuracy for heart disease classification. Four explainable ML models, using SHAP, were used for classification which reflected the feature contributions (FC) and feature weights (FW) for each feature in the CFV for generating the final results. FC and FW were taken into account in generating the reduced dimensional feature subset (FS). The findings of the study are as follows: (a) XGBoost classifies heart diseases best with explanations, with an increase in 2% in model accuracy over existing best proposals, (b) explainable classification using FS exhibits better accuracy than most of the literary proposals, and (c) with the increase in explainability, accuracy can be preserved using XGBoost classifier for classifying heart diseases, and (d) the top four features responsible for diagnosis of heart disease have been exhibited which have common occurrences in all the explanations reflected by the five explainable techniques used on XGBoost classifier based on feature contributions. To the best of our knowledge, this is first attempt to explain XGBoost classification for diagnosis of heart diseases using five explainable techniques.

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“…Artificial intelligence (AI) and machine learning (ML) models are capable of analyzing facial expressions, which can serve as reliable indicators of pain and suffering. This technical application exhibits potential in the field of healthcare, specifically in the assessment of pain levels among patients [23].…”

Section: Pain Assessmentmentioning

confidence: 99%

Healthcare Trust Evolution with Explainable Artificial Intelligence: Bibliometric Analysis

Dhiman,

Bonkra,

Kaur

et al. 2023

Information

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Recent developments in IoT, big data, fog and edge networks, and AI technologies have had a profound impact on a number of industries, including medical. The use of AI for therapeutic purposes has been hampered by its inexplicability. Explainable Artificial Intelligence (XAI), a revolutionary movement, has arisen to solve this constraint. By using decision-making and prediction outputs, XAI seeks to improve the explicability of standard AI models. In this study, we examined global developments in empirical XAI research in the medical field. The bibliometric analysis tools VOSviewer and Biblioshiny were used to examine 171 open access publications from the Scopus database (2019–2022). Our findings point to several prospects for growth in this area, notably in areas of medicine like diagnostic imaging. With 109 research articles using XAI for healthcare classification, prediction, and diagnosis, the USA leads the world in research output. With 88 citations, IEEE Access has the greatest number of publications of all the journals. Our extensive survey covers a range of XAI applications in healthcare, such as diagnosis, therapy, prevention, and palliation, and offers helpful insights for researchers who are interested in this field. This report provides a direction for future healthcare industry research endeavors.

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Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review

Cited by 12 publications

References 74 publications

Human-Centered Explainable Artificial Intelligence: Automotive Occupational Health Protection Profiles in Prevention Musculoskeletal Symptoms

Human-Centered Explainable Artificial Intelligence: Automotive Occupational Health Protection Profiles in Prevention Musculoskeletal Symptoms

XAI–reduct: accuracy preservation despite dimensionality reduction for heart disease classification using explainable AI

Healthcare Trust Evolution with Explainable Artificial Intelligence: Bibliometric Analysis

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