4. Computational intelligence approach to address the language barrier in healthcare
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Shweta Sinha
Abstract
In this aeon of globalization and economic growth, the fixed geographic boundary of any country/continent does not confine the mobility of people. Education and healthcare are two service sectors that have seen the major changes in this respect. But, the language diversity across the globe works as an obtrusion for the smooth transition from one part of the globe to the other. The challenges due to linguistic diversity possess more severe difficulties in the healthcare sector. Certainly, the miscommunications of any form in this sector can have far-reaching consequences that may turn out to be irreversible. These difficulties get more scaled up when the patient moves from one part of the world to the other, where very few people speak or understand his/her language. Solutions in terms of translation of one language speech to another language speech can help overcome these difficulties. Automatic speech-to-speech (S2S) translation can make the communication seamless that can expand the horizon of the healthcare sector. This chapter discusses the advancements in natural language processing, the chief focus being the spoken aspect of the language during communication. The chapter discusses the stringing together of three major techniques: automatic speech recognition, automated translation by machine and conversion of text into spoken utterance, that is, text to speech for seamless communication in healthcare services. Besides this, the technological developments and implementation of the challenges at each step is identified and briefly discussed. The performance of the S2S system is evaluated in the healthcare domain.
Abstract
In this aeon of globalization and economic growth, the fixed geographic boundary of any country/continent does not confine the mobility of people. Education and healthcare are two service sectors that have seen the major changes in this respect. But, the language diversity across the globe works as an obtrusion for the smooth transition from one part of the globe to the other. The challenges due to linguistic diversity possess more severe difficulties in the healthcare sector. Certainly, the miscommunications of any form in this sector can have far-reaching consequences that may turn out to be irreversible. These difficulties get more scaled up when the patient moves from one part of the world to the other, where very few people speak or understand his/her language. Solutions in terms of translation of one language speech to another language speech can help overcome these difficulties. Automatic speech-to-speech (S2S) translation can make the communication seamless that can expand the horizon of the healthcare sector. This chapter discusses the advancements in natural language processing, the chief focus being the spoken aspect of the language during communication. The chapter discusses the stringing together of three major techniques: automatic speech recognition, automated translation by machine and conversion of text into spoken utterance, that is, text to speech for seamless communication in healthcare services. Besides this, the technological developments and implementation of the challenges at each step is identified and briefly discussed. The performance of the S2S system is evaluated in the healthcare domain.
Chapters in this book
- Frontmatter I
- Preface VII
- Contents XI
- List of contributors XIII
- 1. A review of bone tissue engineering for the application of artificial intelligence in cellular adhesion prediction 1
- 2. Implementation and classification of machine learning algorithms in healthcare informatics: approaches, challenges, and future scope 21
- 3. Cardiac arrhythmia recognition using Stockwell transform and ABC-optimized twin SVM 35
- 4. Computational intelligence approach to address the language barrier in healthcare 53
- 5. Recent advancement of machine learning and deep learning in the field of healthcare system 77
- 6. Predicting psychological disorders using machine learning 99
- 7. Automatic analysis of cardiovascular diseases using EMD and support vector machines 131
- 8. Machine learning approach for exploring computational intelligence 153
- 9. Classification of various image fusion algorithms and their performance evaluation metrics 179
- 10. Recommender system in healthcare: an overview 199
- 11. Dense CNN approach for medical diagnosis 217
- 12. Impact of sentiment analysis tools to improve patients’ life in critical diseases 239
- 13. A fuzzy entropy-based multilevel image thresholding using neural network optimization algorithm 253
- 14. Machine learning in healthcare 277
- 15. Computational health informatics using evolutionary-based feature selection 309
- Index 329
Chapters in this book
- Frontmatter I
- Preface VII
- Contents XI
- List of contributors XIII
- 1. A review of bone tissue engineering for the application of artificial intelligence in cellular adhesion prediction 1
- 2. Implementation and classification of machine learning algorithms in healthcare informatics: approaches, challenges, and future scope 21
- 3. Cardiac arrhythmia recognition using Stockwell transform and ABC-optimized twin SVM 35
- 4. Computational intelligence approach to address the language barrier in healthcare 53
- 5. Recent advancement of machine learning and deep learning in the field of healthcare system 77
- 6. Predicting psychological disorders using machine learning 99
- 7. Automatic analysis of cardiovascular diseases using EMD and support vector machines 131
- 8. Machine learning approach for exploring computational intelligence 153
- 9. Classification of various image fusion algorithms and their performance evaluation metrics 179
- 10. Recommender system in healthcare: an overview 199
- 11. Dense CNN approach for medical diagnosis 217
- 12. Impact of sentiment analysis tools to improve patients’ life in critical diseases 239
- 13. A fuzzy entropy-based multilevel image thresholding using neural network optimization algorithm 253
- 14. Machine learning in healthcare 277
- 15. Computational health informatics using evolutionary-based feature selection 309
- Index 329
