Explainable Artificial Intelligence in Medical Decision Support Systems

Cover
Contents
About the editors
Preface
Acknowledgments
1 Explainable artificial intelligence (XAI) in medical decision systems (MDSSs): healthcare systems perspective
	Abstract
	1.1 Introduction
	1.2 Overview of HMDSSs
		1.2.1 MDSSs in healthcare system
		1.2.2 Basis of HMDSS
		1.2.3 Characterizing and categorizing HMDSS
	1.3 Case study of XAI enabled with MDSSs in various infectious diseases
		1.3.1 SCD
		1.3.2 Diabetes mellitus (DM)
		1.3.3 Hypertensive retinopathy (HR)
		1.3.4 Carcinoma
		1.3.5 COVID-19 pandemic
	1.4 XAI research trends and open issues
		1.4.1 XAI perspective in healthcare
	1.5 Conclusion and future directions
	Acknowledgment
	References
2 Explainable artificial intelligence (XAI) in  medical decision support systems (MDSS): applicability, prospects, legal implications,  and challenges
	Abstract
	2.1 Introduction
		2.1.1 Chapter organization
	2.2 MDSS overview in healthcare systems
		2.2.1 Importance and prospects of MDSSs
		2.2.2 The challenges and pitfalls of MDSS
	2.3 AI in MDSS
		2.3.1 The basis of AI in healthcare systems
		2.3.2 The role of AI in MDSS
		2.3.3 Related work of AI in MDSS
		2.3.4 AI weakness in healthcare system
	2.4 XAI
		2.4.1 The basis of XAI
		2.4.2 The role of XAI in MDSSs
	2.5 Ethical effects and implications
		2.5.1 XAI weaknesses in medicine
	2.6 Conclusion and future directions
	Acknowledgment
	References
3 Explainable Artificial Intelligence-based framework for medical decision support systems
	Abstract
	3.1 Introduction
		3.1.1 Key contributions of the chapter
		3.1.2 Chapter organization
	3.2 Applicability of XAI in MDSSs
	3.3 The challenges in the applicability of XAI in MDSSs
	3.4 The proposed DeepSHAP enabled with DNN framework
		3.4.1 The pre-processing stage
		3.4.2 The hyper-parameters and DNN
		3.4.3 The Shapley additive explainable (SHAP)
	3.5 Experimental design for cancer prediction
		3.5.1 The Wisconsin breast cancer (WBCD) dataset
		3.5.2 The performance evaluation metrics
	3.6 Experimental results
		3.6.1 The comparison of the proposed model with existing methods
		3.6.2 The local explanation results
	3.7 The future research direction of XAI in healthcare systems
	3.8 Conclusion and future scopes
	References
4 Prototype interface for detecting mental fatigue with EEG and XAI frameworks in Industry 4.0
	Abstract
	4.1 Introduction
		4.1.1 Measurement of mental fatigue
		4.1.2 EEG in mental fatigue
		4.1.3 Acquisition with brain–machine interface (BCI)
		4.1.4 EEGNET
	4.2 Related work
	4.3 Materials and methods
		4.3.1 Selection of computer equipment for mental fatigue detection
		4.3.2 Generation of the dataset for training
		4.3.3 Training of EEGNet
		4.3.4 Graphical interface and control of communication with the trained model
	4.4 Results and discussions
		4.4.1 Results
		4.4.2 Discussions of results
	4.5 Conclusions
	References
5 XAI for medical image segmentation in medical decision support systems
	Abstract
	5.1 Introduction
		5.1.1 Contributions of the current study
		5.1.2 Chapter organization
	5.2 Related work
		5.2.1 Concept of XAI
		5.2.2 Framework for XAI
		5.2.3 Explainability in healthcare
		5.2.4 DL concept and applications
		5.2.5 Computer vision tasks
		5.2.6 Convolutional neural networks (CNNs)
		5.2.7 Medical image segmentation
		5.2.8 Medical image segmentation techniques
		5.2.9 Medical imaging modality
		5.2.10 Summary of related works
	5.3 Analysis of the proposed system
		5.3.1 Analysis of algorithm for proposed system
		5.3.2 Advantages of the hybrid system
		5.3.3 Disadvantages of the system
		5.3.4 Justification of the system
	5.4 Conclusion
	References
6 XAI robot-assisted surgeries in future medical decision support systems
	Abstract
	6.1 Introduction
	6.2 Related work
		6.2.1 Current applications of AI in the healthcare systems
		6.2.2 Limitations of AI in the medical field
		6.2.3 XAI
		6.2.4 XAI in healthcare
		6.2.5 How explainability works—bridging the AI gap
		6.2.6 Benefits of XAI for the medical field
	6.3 Medical robots
		6.3.1 History of robotic surgery
		6.3.2 Current and future use of medical robots and devices
		6.3.3 Robotic surgery and AI
		6.3.4 Current application of AI in robotic surgery
		6.3.5 Current application of AI in emerging robotic systems
		6.3.6 XAI robot-assisted surgeries for MDSS
		6.3.7 Current limitations of XAI and robotic surgery for MDSS
	6.4 Explanation methods
		6.4.1 Explanation methods in robotics
		6.4.2 SHAPs
		6.4.3 Layer-wise relevance propagation
		6.4.4 LIMEs
	6.5 Conclusion
	Acknowledgment
	References
7 Prediction of erythemato squamous-disease using ensemble learning framework
	Abstract
	7.1 Introduction
	7.2 Related literature review
	7.3 Materials and methods
		7.3.1 Data collection
		7.3.2 Dataset analysis
		7.3.3 Feature selection
		7.3.4 Multi-filter-based feature selection approach
		7.3.5 Multi-embedded-based feature selection approach
		7.3.6 An ensemble multi-feature selection (EMFME-FS) approach
		7.3.7 Machine learning classifiers
		7.3.8 Ensemble methods
	7.4 Experimental results and discussion
	7.5 Conclusion
	References
8 Security-based explainable artificial intelligence (XAI) in healthcare system
	Abstract
	8.1 Introduction
		8.1.1 XAI
		8.1.2 Model-based explanation
		8.1.3 Post-hoc XAI
		8.1.4 Model-specific explanation
		8.1.5 Model-agnostic explanation
		8.1.6 Global explanation
		8.1.7 Local explanation
	8.2 Literature review
		8.2.1 XAI and AI
		8.2.2 Explanation meaningfulness and veracity
		8.2.3 ML in healthcare
		8.2.4 Intelligibility and explainable systems research in HCI
	8.3 Methodology
		8.3.1 Explainable video action recognition system
		8.3.2 TL
		8.3.3 Model architecture
		8.3.4 Freeze model
		8.3.5 Fine-tune model
		8.3.6 Pre-trained model followed by a new classifier
		8.3.7 Pre-trained CNNs implementation
	8.4 Experimental result
		8.4.1 Human action dataset
		8.4.2 ResNet50 visual explanations
		8.4.3 VGG16 visual explanations
		8.4.4 VGG19 visual explanations
		8.4.5 Final discussion
	8.5 Conclusion and future scope
	Acknowledgment
	References
9 Explainable dimensionality reduction model with deep learning for diagnosing hypertensive retinopathy
	Abstract
	9.1 Introduction
	9.2 Overview and related works
		9.2.1 Hypertension
		9.2.2 Machine learning
		9.2.3 Related works
	9.3 Materials and methods
		9.3.1 Data description
		9.3.2 Data preprocessing:
	9.4 Results and discussions
		9.4.1 Importing the dataset
		9.4.2 Resizing and converting the images to array
		9.4.3 Data splitting
		9.4.4 Pre-processing the data with LDA
		9.4.5 Training the ANN model with and without LDA
		9.4.6 Plotting the scattered plot and confusion matrix for the ANN model with and without LDA
		9.4.7 Comparison with previous works
	9.5 Conclusions
	References
10 Understanding cancer patients with diagnostically influential factors using high-dimensional data embedding
	Abstract
	10.1 Introduction
	10.2 Literature review
	10.3 Dimensionality reduction methods
		10.3.1 Projection
		10.3.2 Manifold learning
		10.3.3 PCA
		10.3.4 t-SNE
		10.3.5 SDD
	10.4 Methodology
		10.4.1 Procedure
		10.4.2 Data used
		10.4.3 Performance assessment metrics
	10.5 Experiments
	10.6 Discussion of results
	10.7 Concluding remarks and future work
	References
	Appendix
11 Explainable neural networks in diabetes mellitus prediction
	Abstract
	11.1 Introduction
	11.2 Related work
	11.3 Methodology
		11.3.1 Key implementation requirements and strategies for xDNNs
		11.3.2 DNN architecture
		11.3.3 Activation function
		11.3.4 Procedures for xDNN model implementation
		11.3.5 Model parameters and hyper-parameters
		11.3.6 Evaluation and explainability metrics
	11.4 Results and discussion
		11.4.1 Results for DNN models
		11.4.2 Results for neural network models
	11.5 Conclusion and future scope
	Acknowledgment
	References
12 A KNN and ANN model for predicting heart diseases
	Abstract
	12.1 Introduction
	12.2 Overview of the literature
		12.2.1 Heart diseases
		12.2.2 Machine learning
		12.2.3 Related work
	12.3 Materials and methods
		12.3.1 Standard scalar
		12.3.2 ANNs
		12.3.3 K-nearest neighbor
		12.3.4 Performance metrics
	12.4 Results and discussions
		12.4.1 Comparison with previous work
	12.5 Conclusions
	References
13 Artificial Intelligence-enabled Internet of Medical Things for COVID-19 pandemic data management
	Abstract
	13.1 Introduction
	13.2 Related work
	13.3 IoMT for COVID-19 pandemic data management
		13.3.1 Architecture of IoMT
		13.3.2 Applications of the IoMT in COVID-19 data management
	13.4 Reducing the workload of the medical industry
		13.4.1 Applications of AI-enabled IoMT
		13.4.2 Applications of AI-enabled IoMT for drug repurposing
	13.5 Privacy-aware energy-efficient framework using AIoMT for COVID-19
	13.6 Open research issues
		13.6.1 Security and privacy
		13.6.2 Energy efficiency
		13.6.3 Integration of emotion-aware abilities
		13.6.4 Interoperability
		13.6.5 AI in IoMT
		13.6.6 Ethical issues
	13.7 Conclusion
	Acknowledgment
	References
14 A deep neural network for the identification of lead molecules in antibiotics discovery
	Abstract
	14.1 Introduction
		14.1.1 DNN and its architecture
		14.1.2 Lead identification techniques
	14.2 Literature review
	14.3 Materials and methods
		14.3.1 Dataset preparation and preprocessing
		14.3.2 Model development
		14.3.3 Model evaluation
	14.4 Results and discussion
	14.5 Conclusion
	References
15 Statistical test with differential privacy for medical decision support systems
	Abstract
	15.1 Introduction
	15.2 Related work
		15.2.1 Chi-squared hypothesis test
		15.2.2 Privacy model
		15.2.3 ε-Differentially private Chi-squared test
		15.2.4 Adversarial model
		15.2.5 Other privacy models
	15.3 Proposed algorithm
		15.3.1 Outline
		15.3.2 Global sensitivity of Chi-squared value
		15.3.3 Differentially private hypothesis testing
		15.3.4 Complexity analysis
	15.4 Evaluation
		15.4.1 Significance results
		15.4.2 Power results
		15.4.3 Results of real datasets
	15.5 Discussion
	15.6 Conclusion
	Acknowledgment
	References
16 Automated decision support system for diagnosing sleep diseases using machine intelligence techniques
	Abstract
	16.1 Introduction
	16.2 Related work
	16.3 Experimental dataset
	16.4 Proposed automatic sleep stage detection method
	16.5 Classification
		16.5.1 SVM
		16.5.2 Random Forest (RF)
		16.5.3 Gradient boosting decision tree (GBDT)
		16.5.4 eXtreme gradient boosting (XGBoost)
		16.5.5 Stacking ensembling learning
	16.6 Experimental discussion
		16.6.1 Feature screening results
		16.6.2 Sleep staging performance with ISRUC-Sleep subgroup-I dataset
		16.6.3 Automated decision on sleep staging using the ensemble learning stacking algorithm
	16.7 Conclusion
	References
17 XAI methods for precision medicine in medical decision support systems
	Abstract
	17.1 Introduction
		17.1.1 Contributions of the current study
		17.1.2 Chapter organization
	17.2 Related works
		17.2.1 Measurement of XAI in precision medicine
		17.2.2 Concept of explainability and interpretability
	17.3 Explainable models in MDSS: opportunities and challenges
	17.4 Conclusion
	References
18 The psychology of explanation in medical decision support systems
	Abstract
	18.1 Introduction
		18.1.1 Categories of AI
		18.1.2 Artificial narrow intelligence
		18.1.3 Artificial broad intelligence
		18.1.4 Artificial general intelligence
		18.1.5 Artificial super-intelligence
	18.2 Recent development of XAI in MDSS
		18.2.1 AI in clinical practice
		18.2.2 AI in biomedical research
		18.2.3 AI for public and global health
		18.2.4 AI in healthcare administration
	18.3 Potential benefits of XAI in MDSS
		18.3.1 Radiology
		18.3.2 Early diagnosis
		18.3.3 Emergency medicine
		18.3.4 Risk prediction
		18.3.5 Chatbots
		18.3.6 Virtual nursing assistance
		18.3.7 Precision medicine
		18.3.8 Administrative workflow assistance
	18.4 Key challenges of XAI in MDSS
		18.4.1 Patient harm due to AI errors
		18.4.2 Misuse of medical AI tools
		18.4.3 Risk of bias in medical AI
		18.4.4 Lack of transparency
		18.4.5 Privacy and security issues
	18.5 The future of XAI in MDSS
	18.6 The research trend of XAI in MDSS
	18.7 The future directions and recommendations
	18.8 Conclusions and future scope
	Acknowledgment
	References
Index
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