| 000 | 07757nam a22004573i 4500 | ||
|---|---|---|---|
| 001 | EBC6737887 | ||
| 003 | MiAaPQ | ||
| 005 | 20220524155048.0 | ||
| 006 | m o d | | ||
| 007 | cr cnu|||||||| | ||
| 008 | 220504s2021 xx o ||||0 eng d | ||
| 020 |
_a9781000485400 _q(electronic bk.) |
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| 020 | _z9781032057019 | ||
| 035 | _a(MiAaPQ)EBC6737887 | ||
| 035 | _a(Au-PeEL)EBL6737887 | ||
| 035 | _a(OCoLC)1272989878 | ||
| 040 |
_aMiAaPQ _beng _erda _epn _cMiAaPQ _dMiAaPQ |
||
| 082 | 0 | _a610.285 | |
| 100 | 1 | _aDey, Nilanjan. | |
| 245 | 1 | 0 | _aDigital Future of Healthcare. |
| 264 | 1 |
_aMilton : _bTaylor & Francis Group, _c2021. |
|
| 264 | 4 | _c�2022. | |
| 300 | _a1 online resource (219 pages) | ||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 505 | 0 | _aIntro -- Half Title -- Title Page -- Copyright Page -- Contents -- Preface -- Editors -- Contributors -- 1. Introduction to Digital Future of Healthcare -- 1.1 Introduction -- 1.2 Misconceptions of Digital Healthcare -- 1.2.1 Misconception 1: People Do Not Want to Adopt Automated Healthcare Services -- 1.2.2 Misconception 2: Mainly Young Adults Choose Digital Services -- 1.2.3 Misconception 3: Mobile Health or M-health Is a Game-changer -- 1.2.4 Misconception 4: Patients Are Searching for New Applications and Features -- 1.3 The State of Digital Change of Healthcare in 2021 -- 1.3.1 The Growth in On-Demand Healthcare (Why Patients Need Healthcare on Their Schedule) -- 1.3.2 The Significance of Big Data in the Health Industry -- 1.3.3 The Wonder and Care of Patients with Artificial Intelligence -- 1.3.4 Development of Wearable Healthcare Devices -- 1.3.5 Blockchain and the Future of Improved Electronic Medical Records -- 1.4 Advantages of Digital Healthcare -- 1.5 Challenges in Digital Healthcare -- 1.6 Summary -- References -- 2. A Content-Based Image Retrieval System for Diagnosis and Detection of Skin Cancer Using Self-Organizing Feature Maps -- 2.1 Introduction -- 2.2 Research Highlights -- 2.3 Review on Skin Cancer -- 2.4 Methodology -- 2.4.1 Computation of Local Binary Pattern (LBP) -- 2.4.1.1 Importance of LBP -- 2.4.2 Derivation for LBP -- 2.4.3 Algorithm for LBP -- 2.5 Importance and Algorithm for Computation of Variance -- 2.6 Algorithm for DFT Computation -- 2.7 Detection of Skin Lesion Using Self-Organizing Maps (SOM) -- 2.8 Similarity Measures Used for Training the SOM -- 2.9 Performance Evaluation -- 2.10 Results and Discussion -- 2.11 Conclusion -- References -- 3. Innovative Wearable Device Technology for Biomedical Applications -- 3.1 Introduction -- 3.2 Wearable Devices in Vital Signs Monitoring -- 3.2.1 Heart Rate. | |
| 505 | 8 | _a3.2.2 Respiration Rate -- 3.2.3 Body Temperature -- 3.2.4 Blood Pressure -- 3.2.5 Blood Glucose -- 3.2.6 Blood Oxygen Saturation -- 3.2.7 Motion Evaluation -- 3.2.8 Other Biomedical Applications of Wearable Devices -- 3.3 Few examples of Innovative Wearable Healthcare Devices Currently Under Development -- 3.3.1 RespiroGear - Respiratory Rate Controller -- 3.3.1.1 Background -- 3.3.1.2 Novelty -- 3.3.1.3 Core Technology -- 3.3.2 CardioMate - Heart Rate and Activity Monitoring for Disease Diagnosis -- 3.3.2.1 Background -- 3.3.2.2 Novelty -- 3.3.2.3 Core Technology -- 3.4 Key Factors Influencing the Growth of Healthcare Device Sector, Particularly in the India Context -- 3.5 Challenges and Way Forward of Wearable Devices in Healthcare Technology -- References -- 4. Advancements in Digital Computation: Issues and Opportunities in Healthcare Services -- 4.1 Introduction -- 4.2 Literature Survey -- 4.3 Proposed Model -- 4.3.1 Overview of the Framework -- 4.3.2 Perception Layer -- 4.3.3 Communication Layer -- 4.3.4 Computing Model -- 4.3.4.1 Classification Model -- 4.4 Case Study: COVID-19 Pandemic -- 4.5 Limitations and Future Scope -- References -- 5. Epileptic Aura Detection to Rescue the Epilepsy Patient Through Wireless Body Area Sensor Network -- 5.1 Introduction -- 5.1.1 Epilepsy Attack -- 5.1.2 Body Area Network -- 5.1.2.1 Technologies Associated with BAN -- 5.2 Related Work -- 5.3 Our Approach -- 5.3.1 Proposed Model 1 - Close Circuit Seizure Detector -- 5.3.1.1 Initial Setup -- 5.3.2 Proposed Model 2 - Neck Choker -- 5.3.2.1 Components of EEG Module -- 5.3.3 Proposed Model 3 - Seizure Detection with Heart Beat Sensor -- 5.3.3.1 Procedure of Calculation of Pulse Rate -- 5.3.3.2 Normal Pulse Rate -- 5.4 Discussion -- 5.5 Conclusion -- References. | |
| 505 | 8 | _a6. Prediction of Users' Performance in Surgical Augmented Reality Simulation-Based Training Using Machine Learning Techniques -- 6.1 Introduction -- 6.2 Background and Related Work -- 6.3 Methodology -- 6.3.1 Apparatus -- 6.3.2 Pilot Study -- 6.3.3 Human Evaluation Technique -- 6.3.4 Machine Learning Classifiers (MLCs) -- 6.3.5 Performance Measures -- 6.4 Findings -- 6.5 Discussion -- 6.6 Conclusion and Future Work -- Note -- References -- 7. An Ensemble Approach for Argument Mining on Medical Reviews -- 7.1 Introduction -- 7.1.1 Medical Reviews -- 7.1.2 Challenges in Handling Medical Reviews -- 7.2 Motivation -- 7.3 Related Work -- 7.4 Methodology -- 7.4.1 Proposed Methodology -- 7.4.2 Data Collection and Preprocessing -- 7.4.3 Feature Extraction -- 9.4.4 Relationship Extraction -- 7.4.5 Ensemble Methods -- 7.5 Results and Analysis -- 7.5.1 Results -- 7.5.2 Discussion -- 7.5.3 Website -- 7.5.4 Argument Graphs and User Interface -- 7.6 Conclusion -- References -- 8. Augmented Reality Systems and Haptic Devices for Needle Insertion Medical Training -- 8.1 Introduction -- 8.2 Augmented Reality (AR) -- 8.3 Haptic Devices -- 8.4 AR Systems with Haptic Devices -- 8.5 Analysis of the Studies -- 8.6 Challenges, Trends, and Opportunities -- 8.7 Conclusion -- References -- 9. Current Strategies and Future Perspectives of Autoimmune Disorder -- 9.1 Introduction: Background and Driving Forces -- 9.2 Autoimmune Disorders -- 9.3 Protocol Used During Docking Process -- 9.4 Analysis of Interaction Result Obtained from Molecular Docking -- 9.5 Summary and Conclusions -- References -- 10. Aspects of Improvement of Digital Healthcare Systems Through Digital Transformation -- 10.1 Introduction: Digital Transformation in Healthcare System -- 10.2 Challenges of Digital Transformation in Healthcare -- 10.3 Computer-Aided Drug Design with H2N2 Influenza A Virus. | |
| 505 | 8 | _a10.4 Analysis of Molecular Interaction -- 10.5 Summary and Conclusions -- References -- 11. Automated Detection of COVID-19 Lesion in Lung CT Slices with VGG-UNet and Handcrafted Features -- 11.1 Introduction -- 11.2 Earlier Research -- 11.3 Methodology -- 11.3.1 Image Database -- 11.3.2 Segmentation with VGG-UNet -- 11.3.3 Feature Extraction -- 11.3.4 Feature Selection with Bat Algorithm -- 11.3.5 Classifier Implementation -- 11.3.5.1 Decision-Tree -- 11.3.5.2 Random-Forest -- 11.3.5.3 K-Nearest Neighbor -- 11.3.5.4 SVM-RBF -- 11.3.6 Validation of the Proposed System -- 11.4 Results and Discussions -- 11.5 Conclusion -- References -- Index. | |
| 588 | _aDescription based on publisher supplied metadata and other sources. | ||
| 590 | _aElectronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2022. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. | ||
| 655 | 4 | _aElectronic books. | |
| 700 | 1 | _aDas, Nabanita. | |
| 700 | 1 | _aChaki, Jyotismita. | |
| 776 | 0 | 8 |
_iPrint version: _aDey, Nilanjan _tDigital Future of Healthcare _dMilton : Taylor & Francis Group,c2021 _z9781032057019 |
| 797 | 2 | _aProQuest (Firm) | |
| 850 | _aVJRL-AP | ||
| 856 | 4 | 0 |
_uhttps://ebookcentral.proquest.com/lib/vajira-ebooks/detail.action?docID=6737887 _zClick to View |
| 942 |
_2nlm _cEBK |
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_c36172 _d36172 |
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