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Handbook of Artificial Intelligence in Healthcare : Vol 2: Practicalities and Prospects
| Handbook of Artificial Intelligence in Healthcare : Vol 2: Practicalities and Prospects |
| Autore | Lim Chee Peng |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2022 |
| Descrizione fisica | 1 online resource (429 pages) |
| Altri autori (Persone) |
ChenYen-Wei
VaidyaAshlesha MahorkarCharu JainLakhmi C |
| Collana | Intelligent Systems Reference Library |
| Soggetto genere / forma | Electronic books. |
| ISBN |
9783030836207
9783030836191 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Part I Practicalities of AI Methodologies in Healthcare -- 1 Intelligent Paradigms for Diagnosis, Prediction and Control in Healthcare Applications -- 1.1 Introduction -- 1.2 Relevant References -- 1.3 Medical Decision-Making Based on Artificial Neural Networks -- 1.3.1 Skin Diseases Diagnosis -- 1.3.2 Hepatitis C Predictions -- 1.3.3 Coronary Heart Disease Prediction -- 1.4 Medical Image Analysis Using Artificial Neural Networks -- 1.5 Artificial Neural Networks Versus Naïve Bayesian Classifier -- 1.5.1 Hepatitis B Predictions -- 1.5.2 Stroke Risk Prediction -- 1.6 Prosthetic Hand Myoelectric-Based Modeling and Control Using Evolving Fuzzy Models and Fuzzy Control -- 1.6.1 Evolving Fuzzy Modeling Results -- 1.6.2 Fuzzy Control Results -- 1.7 Conclusions -- References -- 2 Artificial Intelligence in Healthcare Practice: How to Tackle the "Human" Challenge -- 2.1 Introduction -- 2.2 AI in Healthcare -- 2.3 A "third Wheel" Effect -- 2.3.1 "Confusion of the Tongues" -- 2.3.2 Decision Paralysis and Risk of Delay -- 2.3.3 Role Ambiguity -- 2.4 An Interface for AI -- 2.5 Identifying Personnel to Work with AI -- 2.6 Recommendations -- 2.7 Conclusion -- References -- 3 A Statistical Analysis Handbook for Validating Artificial Intelligence Techniques Applied in Healthcare -- 3.1 Introduction -- 3.2 Hypothesis Testing -- 3.2.1 Contingency Tables or Cross-Tabulation -- 3.2.2 Odds Ratio -- 3.2.3 Pearson's χ2Test -- 3.3 Normality Tests -- 3.3.1 Kolmogorov-Smirnov Goodness of Fit (K-S) Test -- 3.3.2 Lilliefors Test -- 3.3.3 Shapiro Wilk W Test -- 3.4 Statistical Benchmarking Tests -- 3.4.1 T-test or Student's T-test -- 3.4.2 T-test for Two Independent Groups of Observations -- 3.4.3 Equality of Variances: Levene's Test -- 3.4.4 Equality of Variances: Bartlett's Test -- 3.4.5 Mann-Whitney Test or Mann-Whitney Wilcoxon Test.
3.4.6 One Way ANOVA -- 3.4.7 Tukey's Honest Significant Difference Test -- 3.5 Conclusions -- References -- 4 Designing Meaningful, Beneficial and Positive Human Robot Interactions with Older Adults for Increased Wellbeing During Care Activities -- 4.1 Introduction -- 4.2 Social Robotics -- 4.2.1 The Nao Robot -- 4.2.2 The Need for Meaningful Activities and a Holistic Approach -- 4.3 Method: Learning from HCI Approaches for Exploring Social HRI -- 4.3.1 Situated Action -- 4.3.2 Participatory Design and Mutual Learning -- 4.3.3 Technology Probes -- 4.3.4 Motivational Goal Models and Technology Probes -- 4.3.5 Understanding Emotions -- 4.3.6 Iterative Visits in the Field and Data Collection -- 4.4 Four Case Studies Using the Nao in the Field -- 4.4.1 Preparing Considerations -- 4.4.2 Interaction stages -- 4.4.3 Overview -- 4.4.4 Case Study 1: Active Ageing Knitting Group -- 4.4.5 Case Study 2: Dementia Respite Care as Part of the Active Ageing Program -- 4.4.6 Case Study 3: Men's Shed -- 4.4.7 Case Study 4: Residential Care -- 4.5 Discussion -- 4.5.1 Creating a Basis Through Humor and Turning Initial Negative Emotions into Positive -- 4.5.2 Increasing Wellbeing Through Activity and Application of Skills -- 4.5.3 Situated AI for Human Robot Interactions -- 4.5.4 Designing Social Interactions -- 4.6 Conclusions -- References -- 5 Wearable Accelerometers in Cancer Patients -- 5.1 Introduction -- 5.2 The Cancer Patient and Outcome Measures -- 5.2.1 Measuring Physical Activity -- 5.2.2 Measuring Physical Activity in the Cancer Patient -- 5.3 Harnessing Wearable Technology in Oncology -- 5.3.1 What Can Wearable Technology Be Used to Measure in Oncology, and Why Are These Parameters Relevant? -- 5.4 Accelerometers -- 5.4.1 Challenges with Wearable Accelerometer Data -- 5.5 Real-World Experience of Running a Digital Health Study -- 5.5.1 Device Considerations. 5.5.2 Successes and Challenges of Running a Real-World Wearable Accelerometer Study -- 5.6 Clinical Studies in Cancer Patients Using Wearable Accelerometers -- 5.7 Ethical Issues with Wearable Accelerometer Data -- 5.7.1 Data Privacy and Security -- 5.7.2 Data Ownership -- 5.7.3 Insurance Premiums -- 5.8 Conclusion -- References -- 6 Online Application of a Home-Administered Parent-Mediated Program for Children with ASD -- 6.1 Introduction -- 6.2 Conceptual Framework and Aims of the Program -- 6.2.1 Behavioral Model of Communicative Failure -- 6.2.2 Structure of the Program -- 6.2.3 Technical Description and Parameters of the Program -- 6.2.4 Technical Specifications of the System -- 6.3 Pilot Testing of the Program-Qualitative Analysis -- 6.4 Conclusions -- References -- 7 Explainable AI, But Explainable to Whom? An Exploratory Case Study of xAI in Healthcare -- 7.1 Introduction -- 7.2 Related Work -- 7.2.1 Adoption and Use of AI in Healthcare -- 7.2.2 Drivers for xAI -- 7.2.3 Emergence of xAI -- 7.2.4 AI and xAI in the Fight Against the COVID-19 Pandemic -- 7.3 Method -- 7.3.1 Data Collection -- 7.3.2 Data Analysis -- 7.4 Case Setting -- 7.5 Findings -- 7.5.1 Development Team -- 7.5.2 Subject Matter Expert -- 7.5.3 Decision-Makers -- 7.5.4 Audience -- 7.6 Discussion and Concluding Remarks -- Appendix 1-Technical Aspects of LungX -- References -- 8 Pandemic Spreading in Italy and Regional Policies: An Approach with Self-organizing Maps -- 8.1 Introduction -- 8.2 Related Literature -- 8.3 Data and Research Questions -- 8.4 Methodology -- 8.5 Analysis -- 8.6 Analysis -- References -- 9 Biases in Assigning Emotions in Patients Due to Multicultural Issues -- 9.1 Introduction -- 9.2 The Non-Universality of Emotions -- 9.3 Emotions in Medical Contexts -- 9.4 Machine Learning, Data, Emotions, and Diagnosis -- 9.4.1 What is Affective Computing?. 9.4.2 Data for Automatic Emotion Detection -- 9.4.3 Developing the Algorithm -- 9.5 Correcting Data Biases in Medical Diagnosis -- 9.6 Conclusions -- References -- Part II Prospects of AI Methodologies in Healthcare -- 10 Artificial Intelligence in Healthcare: Directions of Standardization -- 10.1 Introduction -- 10.2 Definition of Artificial Intelligence (AI) -- 10.3 History -- 10.4 AI Features and Development -- 10.5 Problems and Challenges -- 10.6 AI Systems in Healthcare -- 10.7 Quality and Safety of AI -- 10.8 Standardization of AI in Healthcare -- 10.9 Conclusion -- References -- 11 Development of Artificial Intelligence in Healthcare in Russia -- 11.1 Introduction -- 11.1.1 National Strategy for AI in Healthcare of the Russian Federation -- 11.1.2 The Work of Government Agencies and the Expert Community on the Development of AI in Healthcare -- 11.2 AI Regulations in Healthcare of the Russian Federation -- 11.2.1 Basic Principles of Regulations in Healthcare -- 11.2.2 Technical and Clinical Trials of Software as a Medical Device Created with the Application of AI Technologies -- 11.2.3 State Registration of Software as a Medical Device Created with the Application of AI Technologies -- 11.2.4 Post-registration Monitoring of Software as a Medicaldevice -- 11.3 Technical Regulations of Artificial Intelligence in the Russian Federation -- 11.4 Practical Experience of Artificial Intelligence in Healthcare of the Russian Federation -- 11.5 Chapter Summary -- References -- 12 Robotics in Healthcare -- 12.1 Introduction -- 12.2 Surgical Robots -- 12.2.1 Computer-Assisted Surgery -- 12.2.2 Mechanical Design and Control -- 12.2.3 Application -- 12.3 Rehabilitation Robots -- 12.3.1 Contact Therapy Robots -- 12.3.2 Assistive Robotics -- 12.3.3 Non-Contact Therapy Robots and Socially Assistive Robotics -- 12.4 Non-Medical Robots -- 12.5 Challenges. 12.6 Conclusion -- References -- 13 Smart Healthcare, IoT and Machine Learning: A Complete Survey -- 13.1 Introduction -- 13.2 Architecture and Pipeline -- 13.2.1 Research Questions and Methodology Adopted -- 13.3 The General Picture of Levels -- 13.3.1 Architectures for the Local Integration Level-The Edge Level -- 13.3.2 Task Allocation and Resource Management-The Fog Level -- 13.3.3 Global Integration of Tasks and Resources-The Cloud Level -- 13.3.4 Algorithms and Data Analytics -- 13.3.5 Architectural Configurations -- 13.4 Data Pipeline and Data Storage -- 13.5 Conclusion -- References -- 14 Digital Business Models in the Healthcare Industry -- 14.1 Introduction -- 14.2 Role of the Healthcare Sector -- 14.3 Current Trends of Digitalization in Healthcare -- 14.4 Potential Benefits of Digital Business Models in the Healthcare Industry -- 14.4.1 Research Method -- 14.4.2 Industry-Dependent Determinants of Digitalization -- 14.4.3 Digital Technologies Along the Care Pathway -- 14.4.4 Challenges of Digitalization in Healthcare -- 14.4.5 Study Results -- 14.4.6 Interpretation -- 14.5 Conclusion -- 14.6 Outlook: The Role of AI in Healthcare -- References -- 15 Advances in XAI: Explanation Interfaces in Healthcare -- 15.1 Introduction -- 15.2 Related Work -- 15.3 Method -- 15.4 Findings -- 15.4.1 Prediction Tasks -- 15.4.2 Diagnosis Tasks -- 15.4.3 Automated Tasks -- 15.5 Conclusions -- References -- 16 Medical Knowledge Graphs in the Discovery of Future Research Collaborations -- 16.1 Introduction -- 16.2 Background Issues -- 16.2.1 Graph Measures and Indices -- 16.2.2 Graph-Based Text Representations -- 16.2.3 Graph-Based Feature Selection -- 16.2.4 Graph-Based Text Categorization -- 16.2.5 Graph-Based Link Prediction -- 16.3 The Proposed Framework -- 16.3.1 Graph-Based Text Representation -- 16.3.2 Graph-Based Feature Selection. 16.3.3 Graph-Based Text Categorization. |
| Record Nr. | UNINA-9910510540603321 |
Lim Chee Peng
|
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| Cham : , : Springer International Publishing AG, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Handbook of Artificial Intelligence in Healthcare : Vol. 1 - Advances and Applications
| Handbook of Artificial Intelligence in Healthcare : Vol. 1 - Advances and Applications |
| Autore | Lim Chee Peng |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2021 |
| Descrizione fisica | 1 online resource (463 pages) |
| Altri autori (Persone) |
VaidyaAshlesha
JainKiran MahorkarVirag U JainLakhmi C |
| Collana | Intelligent Systems Reference Library |
| Soggetto genere / forma | Electronic books. |
| ISBN | 3-030-79161-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Handbook of Artificial Intelligence in Healthcare |
| Record Nr. | UNINA-9910502623503321 |
|
Lim Chee Peng
|
||
| Cham : , : Springer International Publishing AG, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||