Explainable artificial intelligence approach towards classifying educational android app reviews using deep learning

Mobile application developers rely largely on user reviews for identifying issues in mobile applications and meeting the users' expectations. User reviews are unstructured, unorganized and very informal. Identifying and classifying issues by extracting required information from reviews is difficult due to a large number of reviews. To automate the process of classifying reviews many researchers have adopted machine learning approaches. Keeping in view, the rising demand for educational applications, especially during COVID-19, this research aims to automate Android application education reviews' classification and sentiment analysis using natural language processing and machine learning techniques. A baseline corpus comprising 13,000 records has been built by collecting reviews of more than 20 educational applications. The reviews were then manually labelled with respect to sentiment and issue types mentioned in each review. User reviews are classified into eight categories and various machine learning algorithms are applied to classify users' sentiments and issues of applications. The results demonstrate that our proposed framework achieved an accuracy of 97% for sentiment identification and an accuracy of 94% in classifying the most significant issues. Moreover, the interpretability of the model is verified by using the explainable artificial intelligence technique of local interpretable model-agnostic explanations. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

The role of booster vaccination in decreasing COVID-19 age-adjusted case fatality rate: Evidence from 32 countries.

Background: The global COVID-19 pandemic is still ongoing, and cross-country and cross-period variation in COVID-19 age-adjusted case fatality rates (CFRs) has not been clarified. Here, we aimed to identify the country-specific effects of booster vaccination and other features that may affect heterogeneity in age-adjusted CFRs with a worldwide scope, and to predict the benefit of increasing booster vaccination rate on future CFR. Method: Cross-temporal and cross-country variations in CFR were identified in 32 countries using the latest available database, with multi-feature (vaccination coverage, demographic characteristics, disease burden, behavioral risks, environmental risks, health services and trust) using Extreme Gradient Boosting (XGBoost) algorithm and SHapley Additive exPlanations (SHAP). After that, country-specific risk features that affect age-adjusted CFRs were identified. The benefit of booster on age-adjusted CFR was simulated by increasing booster vaccination by 1-30% in each country. Results: Overall COVID-19 age-adjusted CFRs across 32 countries ranged from 110 deaths per 100,000 cases to 5,112 deaths per 100,000 cases from February 4, 2020 to Jan 31, 2022, which were divided into countries with age-adjusted CFRs higher than the crude CFRs and countries with age-adjusted CFRs lower than the crude CFRs (n = 9 and n = 23) when compared with the crude CFR. The effect of booster vaccination on age-adjusted CFRs becomes more important from Alpha to Omicron period (importance scores: 0.03-0.23). The Omicron period model showed that the key risk factors for countries with higher age-adjusted CFR than crude CFR are low GDP per capita and low booster vaccination rates, while the key risk factors for countries with higher age-adjusted CFR than crude CFR were high dietary risks and low physical activity. Increasing booster vaccination rates by 7% would reduce CFRs in all countries with age-adjusted CFRs higher than the crude CFRs. Conclusion: Booster vaccination still plays an important role in reducing age-adjusted CFRs, while there are multidimensional concurrent risk factors and precise joint intervention strategies and preparations based on country-specific risks are also essential.

A machine learning and explainable artificial intelligence triage-prediction system for COVID-19

COVID-19 is a respiratory disease caused by the SARS-CoV-2 contagion, severely disrupted the healthcare infrastructure. Various countries have developed COVID-19 vaccines that have effectively prevented the severe symptoms caused by the virus to a certain extent. However, a small section of people continues to perish. Artificial intelligence advances have revolutionized healthcare diagnosis and prognosis infrastructure. In this study, we predict the severity of COVID-19 using heterogenous Machine Learning and Deep Learning algorithms by considering clinical markers, vital signs, and other critical factors. This study extensively reviews various classifier architectures to predict the COVID-19 severity. We built and evaluated multiple pipelines entailing combinations of five state-of-the-art data-balancing techniques (Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic, Borderline SMOTE, SMOTE with Tomek links, and SMOTE with Edited Nearest Neighbor (ENN)) and twelve heterogeneous classifiers such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, Xgboost, Extratrees, Adaboost, Light GBM, Catboost, and 1-D Convolution Neural Network. The best-performing pipeline consists of Random Forest trained on Borderline SMOTE balanced data that produced the highest recall of 83%. We deployed Explainable Artificial Intelligence tools such as Shapley Additive Explanations and Local Interpretable Model-agnostic Explanations, ELI5, Qlattice, Anchor, and Feature Importance to demystify complex tree-based ensemble models. These tools provide valuable insights into the significance of critical features in the severity prediction of a COVID-19 patient. It was observed that changes in respiratory rate, blood pressure, lactate, and calcium values were the primary contributors to the increase in severity of a COVID-19 patient. This architecture aims to be an explainable decision-support triaging system for medical professionals in countries lacking advanced medical technology and infrastructure to reduce fatalities.

An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors.

Evaluating and understanding the financial impacts of COVID-19 has emerged as an urgent research agenda. Nevertheless, the impacts of government interventions on stock markets remain poorly understood. This study explores, for the first time, the impact of COVID-19 related government intervention policies on different stock market sectors using explainable machine learning-based prediction models. The empirical findings suggest that the LightGBM model provides excellent prediction accuracy while preserving computationally efficient and easy explainability of the model. We also find that COVID-19 government interventions are better predictors of stock market volatility than stock market returns. We further show that the observed effects of government intervention on the volatility and returns of ten stock market sectors are heterogeneous and asymmetrical. Our findings have important implications for policymakers and investors in terms of promoting balance and sustaining prosperity across industry sectors through government interventions.

Deep Learning Algorithms with LIME and Similarity Distance Analysis on COVID-19 Chest X-ray Dataset.

In the last few years, many types of research have been conducted on the most harmful pandemic, COVID-19. Machine learning approaches have been applied to investigate chest X-rays of COVID-19 patients in many respects. This study focuses on the deep learning algorithm from the standpoint of feature space and similarity analysis. Firstly, we utilized Local Interpretable Model-agnostic Explanations (LIME) to justify the necessity of the region of interest (ROI) process and further prepared ROI via U-Net segmentation that masked out non-lung areas of images to prevent the classifier from being distracted by irrelevant features. The experimental results were promising, with detection performance reaching an overall accuracy of 95.5%, a sensitivity of 98.4%, a precision of 94.7%, and an F1 score of 96.5% on the COVID-19 category. Secondly, we applied similarity analysis to identify outliers and further provided an objective confidence reference specific to the similarity distance to centers or boundaries of clusters while inferring. Finally, the experimental results suggested putting more effort into enhancing the low-accuracy subspace locally, which is identified by the similarity distance to the centers. The experimental results were promising, and based on those perspectives, our approach could be more flexible to deploy dedicated classifiers specific to different subspaces instead of one rigid end-to-end black box model for all feature space.

Promoting Students' Conceptual Change in Statics through Self-Explanation Strategy in a Remote Learning Context

CONTEXT Statics is a fundamental course in engineering, representing one of the main challenges for students to complete their engineering programs. Statics is also a prerequisite to different subsequent subjects where problem-solving and spatial visualization skills are essential. The traditional teaching of Statics is insufficient for students to achieve successful learning of Statics. PURPOSE OR GOAL This study explores how different conditions of worked-examples integrated into a spatial visualization tool named "Hapstatics" promote conceptual change in Statics. Students engaged in active exploration of these examples by self-explaining them. These activities took place in the context of remote education due to the COVID-19 pandemic. APPROACH OR METHODOLOGY/METHODS This study included 54 undergraduate engineering students enrolled in a Statics course distributed into three groups. Each group wrote self-explanations for each step in a correct, incomplete, or incorrect worked-example in the context of Statics equilibrium. The "Hapstatics" tool was used to support conceptual understanding of static equilibrium. Students completed a pretest and a posttest, and the researcher team used inferential statistics to identify possible changes in students' conceptual knowledge. ACTUAL OR ANTICIPATED OUTCOMES The results showed a considerable improvement on student conceptual understanding in the posttest for the incomplete worked-example condition. The complete and incorrect worked-example conditions did not show a statistically significant result. CONCLUSIONS/RECOMMENDATIONS/SUMMARY This study suggests that engaging students with a Statics interactive worked-example using a self-explanation strategy may promote conceptual change. We recommend working in strengthening their spatial visualization skills in learning Statics throught the use of spatial visualization tools. Copyright © Jose L. De La Hoz, Camilo Vieira, Alfredo J. Ojeda, Gabriel Garcia-Yepes, 2021.

Antecedents and intervention mechanisms of institutional distrust of P2P accommodations during COVID-19 in China

PurposeCOVID-19 affects the peer-to-peer (P2P) accommodation industry. With regard to prospect theory, individuals' negative emotions, such as institutional distrust, are easily evoked and impede consumption intention in an environment of permeating uncertainty and risks. While existing research indicates the negative effects of institutional distrust, scant research has explored its antecedents and intervention mechanisms. This study thus aims to unveil the influencing factors and explore mitigating mechanisms of customers' institutional distrust of P2P accommodations. Design/methodology/approachOnline reviews data were used to identify the underlying critical issues. The authors developed a model to depict how institutional distrust is formed under the boundary condition of subjective norm by the results. The model was verified using a questionnaire survey. Finally, in-depth semi-structured interviews were conducted to ensure its robustness. FindingsThe external environment and internal platform effectiveness are two critical aspects affecting institutional distrust of P2P accommodations. The external environment influences institutional distrust through perceived threat, explaining the formation mechanism of customers' institutional distrust through customers' internal psychology. Furthermore, the authors found subjective norm moderating the effect of perceived threat on customers' institutional distrust. Research limitations/implicationsThis is one of the first studies, to the best of the authors' knowledge, to explore institutional distrust of P2P accommodations after COVID-19. The finding contributes to studies on P2P accommodation by uncovering the contingent role of subjective norm in influencing customers' institutional distrust. Originality/valueThis is a pioneering study that explores the antecedents and mitigating mechanisms of institutional distrust of P2P accommodations during the new normal of COVID-19.

Transferring AI Explainability to User-Centered Explanations of Complex COVID-19 Information

This paper presents a user-centered approach to translating techniques and insights from AI explainability research to developing effective explanations of complex issues in other fields, on the example of COVID-19. We show how the problem of AI explainability and the explainability problem in the COVID-19 pandemic are related: as two specific instances of a more general explainability problem, occurring when people face in-transparent, complex systems and processes whose functioning is not readily observable and understandable to them ("black boxes”). Accordingly, we discuss how we applied an interdisciplinary, user-centered approach based on Design Thinking to develop a prototype of a user-centered explanation for a complex issue regarding people's perception of COVID-19 vaccine development. The developed prototype demonstrates how AI explainability techniques can be adapted and integrated with methods from communication science, visualization and HCI to be applied to this context. We also discuss results from a first evaluation in a user study with 88 participants and outline future work. The results indicate that it is possible to effectively apply methods and insights from explainable AI to explainability problems in other fields and support the suitability of our conceptual framework to inform that. In addition, we show how the lessons learned in the process provide new insights for informing further work on user-centered approaches to explainable AI itself. © 2022, The Author(s).

Does the COVID-19 Pandemic have Implications for Machine Ethics?

Algorithms have advanced in status from supporting human decision-making to making decisions for themselves. The fundamental issue here is the relationship between Big Data and algorithms, or how algorithms empower data with direction and purpose. In this paper, I provide a conceptual framework for analyzing and improving ethical decision-making in Human-AI interaction. On the one hand, I examine the challenges and the limitations facing the field of Machine Ethics and Explainability in its aim to provide and justify ethical decisions. On the other hand, I propose connecting counterfactual explanations with the emotion of regret, as requirements for improving ethical decision-making in novel situations and under uncertainty. To test whether this conceptual framework has empirical value, I analyze the COVID-19 epidemic in terms of "what might have been” to answer the following question: could some of the unintended consequences of this health crisis have been avoided if the available data had been used differently before the crisis happened and as it unfolded? © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Analysis of changes in geographical factors affecting sales in commercial alleys after COVID-19 using machine learning techniques.

Social restrictions, such as social distancing and self-isolation, imposed owing to the coronavirus disease-19 (COVID-19) pandemic have resulted in a decreased demand of commodities and manufactured products. However, the factors influencing sales in commercial districts in the pre- and post-COVID-19 periods have not yet been fully understood. Thus, this study uses machine learning techniques to identify the changes in important geographical factors among both periods that have affected sales in commercial alleys. It was discovered that, in the post-COVID-19 period, the number of pharmacies, age groups of the working population, average monthly income, and number of families living in apartments priced higher than $600k in the catchment areas had relatively high importance after COVID-19 in the prediction of a high level of sales. Moreover, the percentage of deciduous forests appeared to be a important factor in the post-COVID-19 period. As the average monthly income and worker population in their 60s and numbers of pharmacies and banks increased after the pandemic, sales in commercial alleys also increased. The survival of commercial alleys has become a critical social problem in the post-COVID-19 era; therefore, this study is meaningful in that it suggests a policy direction that could contribute to the revitalization of commercial alley sales in the future and boost the local economy.

Molecular modeling of C1-inhibitor as SARS-CoV-2 target identified from the immune signatures of multiple tissues: An integrated bioinformatics study.

The expeditious transmission of the severe acute respiratory coronavirus 2 (SARS-CoV-2), a strain of COVID-19, crumbled the global economic strength and caused a veritable collapse in health infrastructure. The molecular modeling of the novel coronavirus research sounds promising and equips more evidence about the pragmatic therapeutic options. This article proposes a machine-learning framework for identifying potential COVID-19 transcriptomic signatures. The transcriptomics data contains immune-related genes collected from multiple tissues (blood, nasal, and buccal) with accession number: GSE183071. Extensive bioinformatics work was carried out to identify the potential candidate markers, including differential expression analysis, protein interactions, gene ontology, and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment studies. The overlapping investigation found SERPING1, the gene that encodes a glycosylated plasma protein C1-INH, in all three datasets. Furthermore, the immuno-informatics study was conducted on the C1-INH protein. 5DU3, the protein identifier of C1-INH, was fetched to identify the antigenicity, major histocompatibility (MHC) Class I and II binding epitopes, allergenicity, toxicity, and immunogenicity. The screening of peptides satisfying the vaccine-design criteria based on the metrics mentioned above is performed. The drug-gene interaction study reported that Rhucin is strongly associated with SERPING1. HSIC-Lasso (Hilbert-Schmidt independence criterion-least absolute shrinkage and selection operator), a model-free biomarker selection technique, was employed to identify the genes having a nonlinear relationship with the target class. The gene subset is trained with supervised machine learning models by a leave-one-out cross-validation method. Explainable artificial intelligence techniques perform the model interpretation analysis.

ReLAX: Reinforcement Learning Agent Explainer for Arbitrary Predictive Models

Counterfactual examples (CFs) are one of the most popular methods for attaching post-hoc explanations to machine learning (ML) models. However, existing CF generation methods either exploit the internals of specific models or depend on each sample's neighborhood, thus they are hard to generalize for complex models and inefficient for large datasets. This work aims to overcome these limitations and introduces ReLAX, a model-agnostic algorithm to generate optimal counterfactual explanations. Specifically, we formulate the problem of crafting CFs as a sequential decision-making task and then find the optimal CFs via deep reinforcement learning (DRL) with discrete-continuous hybrid action space. Extensive experiments conducted on several tabular datasets have shown that ReLAX outperforms existing CF generation baselines, as it produces sparser counterfactuals, is more scalable to complex target models to explain, and generalizes to both classification and regression tasks. Finally, to demonstrate the usefulness of our method in a real-world use case, we leverage CFs generated by ReLAX to suggest actions that a country should take to reduce the risk of mortality due to COVID-19. Interestingly enough, the actions recommended by our method correspond to the strategies that many countries have actually implemented to counter the COVID-19 pandemic. © 2022 ACM.

Machine learning and comorbidity network analysis for hospitalized patients with COVID-19 in a city in Southern Brazil.

The large amount of data generated during the COVID-19 pandemic requires advanced tools for the long-term prediction of risk factors associated with COVID-19 mortality with higher accuracy. Machine learning (ML) methods directly address this topic and are essential tools to guide public health interventions. Here, we used ML to investigate the importance of demographic and clinical variables on COVID-19 mortality. We also analyzed how comorbidity networks are structured according to age groups. We conducted a retrospective study of COVID-19 mortality with hospitalized patients from Londrina, Parana, Brazil, registered in the database for severe acute respiratory infections (SIVEP-Gripe), from January 2021 to February 2022. We tested four ML models to predict the COVID-19 outcome: Logistic Regression, Support Vector Machine, Random Forest, and XGBoost. We also constructed a comorbidity network to investigate the impact of co-occurring comorbidities on COVID-19 mortality. Our study comprised 8358 hospitalized patients, of whom 2792 (33.40%) died. The XGBoost model achieved excellent performance (ROC-AUC = 0.90). Both permutation method and SHAP values highlighted the importance of age, ventilatory support status, and intensive care unit admission as key features in predicting COVID-19 outcomes. The comorbidity networks for old deceased patients are denser than those for young patients. In addition, the co-occurrence of heart disease and diabetes may be the most important combination to predict COVID-19 mortality, regardless of age and sex. This work presents a valuable combination of machine learning and comorbidity network analysis to predict COVID-19 outcomes. Reliable evidence on this topic is crucial for guiding the post-pandemic response and assisting in COVID-19 care planning and provision.

Is self‐explanation better than explaining to a fictitious student when learning from video lectures?

Generating written explanations is a popular learning strategy in an online learning environment. Students can explain to themselves (ie, self‐explanations) or a peer‐student (ie, instructional explanations). However, for improving learning from video lectures, it is unclear whether writing self‐explanations is more beneficial than writing instructional explanations, and whether writing both types of explanation is more beneficial than writing only one type. We compared the learning‐related outcomes of students who wrote explanations under one of four conditions: self‐explanation (n = 30), instructional explanation (n = 30), self‐explanation then instructional explanation (n = 30) and instructional explanation then self‐explanation (n = 30). We assessed the participants' external and internal attention, explanation quality, and immediate and delayed learning performance. Students in the conditions that included self‐explanations showed higher internal attention, as well as better immediate and delayed performance than those in the instructional explanations condition. In addition, students in the two combined conditions showed a higher level of organization and elaboration than those in the instructional explanations condition. These results suggest that students should write explanations to themselves while learning from video lectures. Practitioner notesWhat is already known about this topic Generating explanations is a beneficial learning strategy.It is unclear whether explaining to oneself (self‐explanations) is more beneficial than explaining to a peer (instructional explanations).The benefits of writing instructional explanations on learning performance were not consistently found across diverse areas.What this paper adds Self‐explanations, both in oral and written form, were more effective for learning performance than instructional explanations.Students in the conditions that included both self‐explanations and instructional explanations demonstrated a higher level of organization and elaboration than those in the instructional explanation condition.When compared to the self‐explanations condition, additional instructional explanations had no effect on learning performance or internal attention.Implications for practice and/or policy Self‐explanations was an excellent approach for learning from video lectures.Students should write explanations to themselves while learning from video lectures. [ FROM AUTHOR] Copyright of British Journal of Educational Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Reptile: Aggregation-level Explanations for Hierarchical Data

Users often can see from overview-level statistics that some results look "off", but are rarely able to characterize even the type of error. Reptile is an iterative human-in-the-loop explanation and cleaning system for errors in hierarchical data. Users specify an anomalous distributive aggregation result (a complaint), and Reptile recommends drill-down operations to help the user "zoom-in" on the underlying errors. Unlike prior explanation systems that intervene on raw records, Reptile intervenes by learning a group's expected statistics, and ranks drill-down sub-groups by how much the intervention fixes the complaint. This group-level formulation supports a wide range of error types (missing, duplicates, value errors) and uniquely leverages the distributive properties of the user complaint. Further, the learning-based intervention lets users provide domain expertise that Reptile learns from. In each drill-down iteration, Reptile must train a large number of predictive models. We thus extend factorised learning from countjoin queries to aggregation-join queries, and develop a suite of optimizations that leverage the data's hierarchical structure. These optimizations reduce runtimes by >6x compared to a Lapack-based implementation. When applied to real-world Covid-19 and African farmer survey data, Reptile correctly identifies 21/30 (vs 2 using existing explanation approaches) and 20/22 errors. Reptile has been deployed in Ethiopia and Zambia, and used to clean nationwide farmer survey data;the clean data has been used to design national drought insurance policies.

LISA : Enhance the explainability of medical images unifying current XAI techniques

This work proposed a unified approach to increase the explainability of the predictions made by Convolution Neural Networks (CNNs) on medical images using currently available Explainable Artificial Intelligent (XAI) techniques. This method in-cooperates multiple techniques such as LISA aka Local Interpretable Model Agnostic Explanations (LIME), integrated gradients, Anchors and Shapley Additive Explanations (SHAP) which is Shapley values-based approach to provide explanations for the predictions provided by Blackbox models. This unified method increases the confidence in the black-box model's decision to be employed in crucial applications under the supervision of human specialists. In this work, a Chest X-ray (CXR) classification model for identifying Covid-19 patients is trained using transfer learning to illustrate the applicability of XAI techniques and the unified method (LISA) to explain model predictions. To derive predictions, an image-net based Inception V2 model is utilized as the transfer learning model. © 2022 IEEE.

Examining children's questions and parents' responses about COVID-19 pandemic in Turkey.

COVID-19 pandemic has had a significant impact on both adults' and children's everyday lives. Conversations about biological processes such as viruses, illness, and health have started to occur more frequently in daily interactions. Although there are many guidelines for parents about how to talk to their children about the coronavirus, only a few studies have examined what children are curious about the coronavirus and how they make sense of the changes in their everyday lives. This study addresses this need by examining children's questions and parents' responses about the COVID-19 Pandemic in the Turkish sociocultural context. Using an online survey, we asked 184 parents of 3- to 12-year-olds to report their children's questions about coronavirus and their answers to these questions. We analyzed children's questions and parents' responses using qualitative and quantitative analyses (Menendez et al., 2021). Children's questions were mainly about the nature of the virus (34%), followed by lifestyle changes (20%). Older children were more likely to ask about school/work and less likely to ask about lifestyle changes than younger children. Parents responded to children's questions by providing realistic explanations (48%) and reassurance (20%). Only 18% of children's questions were explanation-seeking "why" and "how" questions. Parents were more likely to provide explanations if children's questions were explanation-seeking. Family activities such as playing games and cooking were the most common coping strategies reported by parents (69.2%). The findings have important implications for children's learning about the coronavirus and how adults can support children's learning and help them develop coping strategies in different sociocultural contexts. Supplementary Information: The online version contains supplementary material available at 10.1007/s12144-022-03331-4.

The Pitfalls of Mining for QuantiFERON Gold in Severely Ill Patients With COVID-19.

Objective: To assess the proportion of indeterminate QuantiFERON-TB Gold Plus (QFT-Plus) results in patients admitted for severe coronavirus disease 2019 (COVID-19) pneumonia and evaluate the factors associated with indeterminate QFT-Plus results. Patients and Methods: Data on COVID-19 admissions at Mayo Clinic in Florida were extracted between October 13, 2020, and September 20, 2021, and data from a prepandemic cohort were extracted between October 13, 2018, and September 20, 2019. A secondary analysis of the COVID-19 cohort was performed using gradient boosting modeling to generate variable importance and SHapley Additive exPlanations plots. Results: Our findings demonstrated more indeterminate QFT-Plus test results in patients hospitalized for severe COVID-19 infection than in patients without COVID-19 (139 of 495, 28.1%). The factors associated with indeterminate QFT-Plus test results included elevated levels of C-reactive protein, ferritin, lactate dehydrogenase and interleukin-6 and included lower levels of leukocyte, lymphocyte, and platelet counts. Conclusion: The patients with severe COVID-19 had a higher likelihood of indeterminate QFT-Plus results, which were associated with elevated levels of inflammatory markers consistent with severe infection. Interferon-gamma release assay screening tests are likely confounded by COVID-19 infection itself, limiting the screening ability for latent tuberculosis infection reactivation. Indeterminate QFT-Plus results may also require follow-up QFT-Plus testing after patient recovery from COVID-19, increasing the cost and complexity of medical decision making and management. Additional risk assessments may be needed in this patient population for screening for latent tuberculosis infection in patients with severe COVID-19.

The effect of machine learning explanations on user trust for automated diagnosis of COVID-19.

Recent years have seen deep neural networks (DNN) gain widespread acceptance for a range of computer vision tasks that include medical imaging. Motivated by their performance, multiple studies have focused on designing deep convolutional neural network architectures tailored to detect COVID-19 cases from chest computerized tomography (CT) images. However, a fundamental challenge of DNN models is their inability to explain the reasoning for a diagnosis. Explainability is essential for medical diagnosis, where understanding the reason for a decision is as important as the decision itself. A variety of algorithms have been proposed that generate explanations and strive to enhance users' trust in DNN models. Yet, the influence of the generated machine learning explanations on clinicians' trust for complex decision tasks in healthcare has not been understood. This study evaluates the quality of explanations generated for a deep learning model that detects COVID-19 based on CT images and examines the influence of the quality of these explanations on clinicians' trust. First, we collect radiologist-annotated explanations of the CT images for the diagnosis of COVID-19 to create the ground truth. We then compare ground truth explanations with machine learning explanations. Our evaluation shows that the explanations produced. by different algorithms were often correct (high precision) when compared to the radiologist annotated ground truth but a significant number of explanations were missed (significantly lower recall). We further conduct a controlled experiment to study the influence of machine learning explanations on clinicians' trust for the diagnosis of COVID-19. Our findings show that while the clinicians' trust in automated diagnosis increases with the explanations, their reliance on the diagnosis reduces as clinicians are less likely to rely on algorithms that are not close to human judgement. Clinicians want higher recall of the explanations for a better understanding of an automated diagnosis system.

ECG-iCOVIDNet: Interpretable AI model to identify changes in the ECG signals of post-COVID subjects.

OBJECTIVE: Studies showed that many COVID-19 survivors develop sub-clinical to clinical heart damage, even if subjects did not have underlying heart disease before COVID. Since Electrocardiogram (ECG) is a reliable technique for cardiovascular disease diagnosis, this study analyzes the 12-lead ECG recordings of healthy and post-COVID (COVID-recovered) subjects to ascertain ECG changes after suffering from COVID-19. METHOD: We propose a shallow 1-D convolutional neural network (CNN) deep learning architecture, namely ECG-iCOVIDNet, to distinguish ECG data of post-COVID subjects and healthy subjects. Further, we employed ShAP technique to interpret ECG segments that are highlighted by the CNN model for the classification of ECG recordings into healthy and post-COVID subjects. RESULTS: ECG data of 427 healthy and 105 post-COVID subjects were analyzed. Results show that the proposed ECG-iCOVIDNet model could classify the ECG recordings of healthy and post-COVID subjects better than the state-of-the-art deep learning models. The proposed model yields an F1-score of 100%. CONCLUSION: So far, we have not come across any other study with an in-depth ECG signal analysis of the COVID-recovered subjects. In this study, it is shown that the shallow ECG-iCOVIDNet CNN model performed good for distinguishing ECG signals of COVID-recovered subjects from those of healthy subjects. In line with the literature, this study confirms changes in the ECG signals of COVID-recovered patients that could be captured by the proposed CNN model. Successful deployment of such systems can help the doctors identify the changes in the ECG of the post-COVID subjects on time that can save many lives.