Computational Intelligence and Healthcare Informatics. Группа авторов
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Limitations Acknowledgments Abbreviations References
8 Part IV: Prospective of Computational Intelligence in Healthcare 19 Conceptualizing Tomorrow’s Healthcare Through Digitization 19.1 Introduction 19.2 Importance of IoMT in Healthcare 19.3 Case Study I: An Integrated Telemedicine Platform in Wake of the COVID-19 Crisis 19.4 Case Study II: A Smart Sleep Detection System to Track the Sleeping Pattern in Patients Suffering From Sleep Apnea 19.5 Future of Smart Healthcare 19.6 Conclusion References 20 Domain Adaptation of Parts of Speech Annotators in Hindi Biomedical Corpus: An NLP Approach 20.1 Introduction 20.2 Review of Related Literature 20.3 Scope and Objectives 20.4 Methodological Design 20.5 Evaluation 20.6 Issues 20.7 Conclusion and Future Work Acknowledgements References 21 Application of Natural Language Processing in Healthcare 21.1 Introduction 21.2 Evolution of Natural Language Processing 21.3 Outline of NLP in Medical Management 21.4 Levels of Natural Language Processing in Healthcare 21.5 Opportunities and Challenges From a Clinical Perspective 21.6 Openings and Difficulties From a Natural Language Processing Point of View 21.7 Actionable Guidance and Directions for the Future 21.8 Conclusion References
9 Index
List of Figures
1 Chapter 1Figure 1.1 Machine learning and big data analysis in healthcare.Figure 1.2 Application of ML in healthcare.Figure 1.3 The types of machine learning algorithm.Figure 1.4 Sources of big data in healthcare.Figure 1.5 Applications of big data in healthcare.
2 Chapter 2Figure 2.1 Broad view of existing research.Figure 2.2 Types of chest pathologies.
3 Chapter 3Figure 3.1 (a) This what the normal data looks like. (b) “Big p and small n” pro...Figure 3.2 Feature selection process.Figure 3.3 Taxonomy of feature selection.Figure 3.4 Taxonomy of deep learning models.
4 Chapter 4Figure 4.1 Overview of the processes involved in building ML models.Figure 4.2 Machine Learning vs. Deep Learning workflow process.Figure 4.3 Overview of an Artificial Neural Network.Figure 4.4 The description of the raw dataset showing the first five rows.Figure 4.5 The code snippet showing the data processing of the attributes such a...Figure 4.6 The description of the cleaned and processed dataset showing the firs...Figure 4.7 Boxplot showing number of disease occurrences.Figure 4.8 Ten highest reported diseases.Figure 4.9 Ten highest reported symptoms.Figure 4.10 The code snippet of the label encoding and one hot encoding process ...Figure 4.11 List of the implemented algorithms while building the model. MLP pro...Figure 4.12 Work architecture of the proposed solution.Figure 4.13 The web application to predict disease based on symptoms.Figure 4.14 The user can input multiple symptoms at a time and get accurate pred...
5 Chapter 5Figure 5.1 PCG signal in time-frequency representation before and after filtered...Figure 5.2 (a) Standard spectrogram for normal sample. (b) for standard spectrog...Figure 5.3 (a) Mel-spectrogram for normal sample. (b) Mel-spectrogram for abnorm...Figure 5.4 (a) IIR-CQT spectrogram for normal sample. (b) IIR-CQT spectrogram fo...Figure 5.5 Kirschmask in eight different directions [12].Figure 5.6 Algorithm for the proposed heart sound classification system.Figure 5.7 Confusion matrix for dense CLBP method.
6 Chapter 6Figure 6.1 Process of proposed model.Figure 6.2 Performance on the Yeast data set.Figure 6.3 Performance on the Scene data set.Figure 6.4 Performance of the Emotion data set.Figure 6.5 Performance if the Enron data set.Figure 6.6 Performance if the Medical data set.
7 Chapter 7Figure 7.1 An Intelligent Computational Framework for diabetes disease predictio...Figure 7.2 Performance of all eight classification hypotheses on PIDD using 5-fo...Figure 7.3 F1-score and MCC of all eight classification hypotheses on PIDD apply...Figure 7.4 Precision and AUC of all eight classification hypotheses on PIDD usin...Figure 7.5 ROC curves of all used classification hypotheses applying 5-fold CV.Figure 7.6 Performance of all eight classification hypotheses on PIDD using 7-fo...Figure 7.7 F1-score and MCC of all eight classification hypotheses on PIDD using...Figure 7.8 Precision and AUC of all eight classification hypotheses on PIDD usin...Figure 7.9 ROC curves of all used classification hypotheses using 7-fold CV.Figure 7.10 Performance of all eight classification hypotheses on PIDD using 10-...Figure 7.11 F1-score and MCC of all eight classification hypotheses on PIDD usin...Figure 7.12 Precision and AUC of all eight classification hypotheses on PIDD usi...Figure 7.13 ROC curves of all eight classification hypotheses on PIDD using 10-f...
8 Chapter 8Figure 8.1 General schematic diagram of the proposed method for predicting heart...Figure 8.2 Process of building heart disease prediction model.Figure 8.3 Proposed classifier workflow.Figure 8.4 Random forest classifier workflow.Figure 8.5 Comparison of three ensemble classifier accuracy before and after gri...
9 Chapter 9Figure 9.1 Distribution of post-surgical