10. Recommender system in healthcare: an overview
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Dhrubasish Sarkar
Abstract
Today information technologies have led to a vast number of innovations and developments in almost every field of advancement. In this context, recommender systems (RS) have been setting milestones in the service industry. If we look at the web-based services, it can be said that RS has contributed majorly to increase the reachability of products and to provide a sea of options for potential customers. This RS can also be used as a tool to support decision-making by the decision-makers. With this advancement of RS in the service industry, healthcare systems also do not lag. Health RS (HRS) are becoming an important platform for providing healthcare services, which would cut down the hectic schedule of visiting the doctors and waiting for hours to get checked. In the healthcare industry, RS already play a very significant role in terms of supporting decision-making process about any individual’s health. Keeping the very limited availability of resources in mind and the need for HRS to make its way into the chapter of milestones and innovations, it is important to introduce a set of knowledge and information for the researchers that are interested in HRS studies and can make huge advancements in this domain. Using HRS to suggest the most probable and appropriate medicines after taking into consideration the history of the individual can be a sub-domain to highly think about. Hence, this paper provides a literature study of the HRS domain in general which includes the literature, innovations, purpose, and methods of HRS, along with the new concept of HRS being used for medication purposes.
Abstract
Today information technologies have led to a vast number of innovations and developments in almost every field of advancement. In this context, recommender systems (RS) have been setting milestones in the service industry. If we look at the web-based services, it can be said that RS has contributed majorly to increase the reachability of products and to provide a sea of options for potential customers. This RS can also be used as a tool to support decision-making by the decision-makers. With this advancement of RS in the service industry, healthcare systems also do not lag. Health RS (HRS) are becoming an important platform for providing healthcare services, which would cut down the hectic schedule of visiting the doctors and waiting for hours to get checked. In the healthcare industry, RS already play a very significant role in terms of supporting decision-making process about any individual’s health. Keeping the very limited availability of resources in mind and the need for HRS to make its way into the chapter of milestones and innovations, it is important to introduce a set of knowledge and information for the researchers that are interested in HRS studies and can make huge advancements in this domain. Using HRS to suggest the most probable and appropriate medicines after taking into consideration the history of the individual can be a sub-domain to highly think about. Hence, this paper provides a literature study of the HRS domain in general which includes the literature, innovations, purpose, and methods of HRS, along with the new concept of HRS being used for medication purposes.
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
