A LIME-Based Explainable Machine Learning Model for Predicting the Severity Level of COVID-19 Diagnosed Patients

Gabbay, Freddy; Bar‐Lev, Shirly; Montano, Ofer; Hadad, Noam

doi:10.3390/app112110417

Cited by 31 publications

(14 citation statements)

References 34 publications

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“…To derive a representation that is understandable by humans, LIME tries to find the importance of contiguous superpixels in a source image towards the output class. It has been widely implemented in COVID-19 diagnosis tasks [41], [45]- [48] to further explain the process of feature extraction, which contributes to a better understanding of what features in CT/X-ray images characterize the onset of COVID-19. Ahsan et al [45] implemented LIME to interpret top features in COVID-19 X-ray imaging and build trust in an AI framework to distinguish between patients with COVID-19 symptoms with other patients.…”

Section: Perturbation-basedmentioning

confidence: 99%

See 1 more Smart Citation

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

Giuste

Shi

Zhu

et al. 2023

IEEE Rev. Biomed. Eng.

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Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-based solutions to COVID-19 challenges during the pandemic, few have made a significant clinical impact, especially in diagnosis and disease precision staging. One major cause for such low impact is the lack of model transparency, significantly limiting the AI adoption in real clinical practice. To solve this problem, AI models need to be explained to users. Thus, we have conducted a comprehensive study of Explainable Artificial Intelligence (XAI) using PRISMA technology. Our findings suggest that XAI can improve model performance, instill trust in the users, and assist users in decisionmaking. In this systematic review, we introduce common XAI techniques and their utility with specific examples of their application. We discuss the evaluation of XAI results because it is an important step for maximizing the value of AI-based clinical decision support systems. Additionally, we present the traditional, modern, and advanced XAI models to demonstrate the evolution of novel techniques. Finally, we provide a best practice guideline that developers can refer to during the model experimentation. We also offer potential solutions with specific examples for common challenges in AI model experimentation. This comprehensive review, hopefully, can promote AI adoption in biomedicine and healthcare.

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Section: Perturbation-basedmentioning

confidence: 99%

“…Feature occlusion and ablation [26]- [28], [32], [33] SHAP feature importance [35]- [41] Local interpretable model-agnostic explanations (LIME) [41], [44]- [48] Activation-based Activation maximization [49] Class activation maps (CAM) [50], [51] Gradient-based…”

Section: Perturbation-basedmentioning

confidence: 99%

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

Giuste

Shi

Zhu

et al. 2023

IEEE Rev. Biomed. Eng.

View full text Add to dashboard Cite

show abstract

“…LIME is a technique for explaining black-box models, or models whose inner logic is obscure and difficult to comprehend [47]. LIME adjusts the feature values for a single data sample and monitors its impact on the output.…”

Section: Local Interpretable Model-agnostic Explanations (Lime)mentioning

confidence: 99%

Comprehensible Machine-Learning-Based Models for the Pre-Emptive Diagnosis of Multiple Sclerosis Using Clinical Data: A Retrospective Study in the Eastern Province of Saudi Arabia

Olatunji

Alsheikh

Alnajrani

et al. 2023

IJERPH

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Multiple Sclerosis (MS) is characterized by chronic deterioration of the nervous system, mainly the brain and the spinal cord. An individual with MS develops the condition when the immune system begins attacking nerve fibers and the myelin sheathing that covers them, affecting the communication between the brain and the rest of the body and eventually causing permanent damage to the nerve. Patients with MS (pwMS) might experience different symptoms depending on which nerve was damaged and how much damage it has sustained. Currently, there is no cure for MS; however, there are clinical guidelines that help control the disease and its accompanying symptoms. Additionally, no specific laboratory biomarker can precisely identify the presence of MS, leaving specialists with a differential diagnosis that relies on ruling out other possible diseases with similar symptoms. Since the emergence of Machine Learning (ML) in the healthcare industry, it has become an effective tool for uncovering hidden patterns that aid in diagnosing several ailments. Several studies have been conducted to diagnose MS using ML and Deep Learning (DL) models trained using MRI images, achieving promising results. However, complex and expensive diagnostic tools are needed to collect and examine imaging data. Thus, the intention of this study is to implement a cost-effective, clinical data-driven model that is capable of diagnosing pwMS. The dataset was obtained from King Fahad Specialty Hospital (KFSH) in Dammam, Saudi Arabia. Several ML algorithms were compared, namely Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Extra Trees (ET). The results indicated that the ET model outpaced the rest with an accuracy of 94.74%, recall of 97.26%, and precision of 94.67%.

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“…LIME is a popular interpretation tool that learns a new interpretable model that can better explain a less interpretable model. Numerous studies have successfully applied LIME to provide interpretation of complex models, including in biomedicine [ 195 , 196 ]. Another popular interpretation method is DeepLIFT [ 197 ], which calculates the contribution of neurons in a trained neural network by evaluating the difference in activation from a chosen representative reference.…”

Section: Major Challenges For Clinical Utility Of Complex and Data-dr...mentioning

confidence: 99%

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications

Patterson

Elbasir

Tian

et al. 2023

Cancers

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Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning.

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A LIME-Based Explainable Machine Learning Model for Predicting the Severity Level of COVID-19 Diagnosed Patients

Cited by 31 publications

References 34 publications

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

Comprehensible Machine-Learning-Based Models for the Pre-Emptive Diagnosis of Multiple Sclerosis Using Clinical Data: A Retrospective Study in the Eastern Province of Saudi Arabia

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications

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