Chapter 12 An overview of recommender systems in the healthcare domain: significant contributions, challenges, and future scope
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Anupama Angadi
Abstract
In the age of the web, we become aware that the use of recommender systems (RSs) is versatile. The abundant data produced by smart devices on the web originate uncertainty for medical treatment to pick a preferred suggestion. Symptomoriented guidelines are a noble way to direct patients to notice the right medical tests and drugs. The RS aims to tailor search results, past browsing history, and searching patterns to guess what patients might look for in medical facilities soon. For instance, a client searching for signs and symptoms of B12 deficiency might recommend the best food sources, medicines, and healthcare officials. Unlike current literature in the health domain, our work offers insights into recommendation scenarios and approaches. Two major RSs exist either collaborative or content filtering. The essential of the RS exists in determining similar patients (or symptoms). We outlined the introduction, prior works emphasizing recommender filters, and their strengths and weaknesses. Later, we categorize types therein and briefly discuss similarity metrics applied to filter neighborhoods, and the choice of estimation metrics for evaluating the RS is discussed.
Abstract
In the age of the web, we become aware that the use of recommender systems (RSs) is versatile. The abundant data produced by smart devices on the web originate uncertainty for medical treatment to pick a preferred suggestion. Symptomoriented guidelines are a noble way to direct patients to notice the right medical tests and drugs. The RS aims to tailor search results, past browsing history, and searching patterns to guess what patients might look for in medical facilities soon. For instance, a client searching for signs and symptoms of B12 deficiency might recommend the best food sources, medicines, and healthcare officials. Unlike current literature in the health domain, our work offers insights into recommendation scenarios and approaches. Two major RSs exist either collaborative or content filtering. The essential of the RS exists in determining similar patients (or symptoms). We outlined the introduction, prior works emphasizing recommender filters, and their strengths and weaknesses. Later, we categorize types therein and briefly discuss similarity metrics applied to filter neighborhoods, and the choice of estimation metrics for evaluating the RS is discussed.
Chapters in this book
- Frontmatter I
- About the book V
- Preface VII
- Foreword IX
- Contents XI
- List of contributors XV
- Chapter 1 The impact of blockchain technology on the healthcare system 1
- Chapter 2 The role of metaverse in transforming healthcare: blockchain approach 33
- Chapter 3 Blockchain-empowered metaverse healthcare systems and applications 61
- Chapter 4 Role of artificial intelligence in disease diagnosis 89
- Chapter 5 Machine learning for twinning the human body 105
- Chapter 6 Improving patient care and healthcare management using bigdata analytics presents several research challenges 131
- Chapter 7 An emerging trends of bioinformatics and big data analytics in healthcare 159
- Chapter 8 Digital twins in medicine: leveraging machine learning for real-time diagnosis and treatment 189
- Chapter 9 Nanorobots in healthcare 209
- Chapter 10 Semantic-based approach for medical cyber-physical system (MCPS) with biometric authentication for secured privacy 237
- Chapter 11 Integration of cognitive computing and AI for smart healthcare 267
- Chapter 12 An overview of recommender systems in the healthcare domain: significant contributions, challenges, and future scope 293
- Chapter 13 Advancements and challenges of using natural language processing in the healthcare sector 317
- Chapter 14 Intraocular pressure monitoring system for glaucoma patients using IoT and machine learning 343
- Chapter 15 A machine learning approach to voice analysis in Parkinson’s disease diagnosis 365
- Index 375
Chapters in this book
- Frontmatter I
- About the book V
- Preface VII
- Foreword IX
- Contents XI
- List of contributors XV
- Chapter 1 The impact of blockchain technology on the healthcare system 1
- Chapter 2 The role of metaverse in transforming healthcare: blockchain approach 33
- Chapter 3 Blockchain-empowered metaverse healthcare systems and applications 61
- Chapter 4 Role of artificial intelligence in disease diagnosis 89
- Chapter 5 Machine learning for twinning the human body 105
- Chapter 6 Improving patient care and healthcare management using bigdata analytics presents several research challenges 131
- Chapter 7 An emerging trends of bioinformatics and big data analytics in healthcare 159
- Chapter 8 Digital twins in medicine: leveraging machine learning for real-time diagnosis and treatment 189
- Chapter 9 Nanorobots in healthcare 209
- Chapter 10 Semantic-based approach for medical cyber-physical system (MCPS) with biometric authentication for secured privacy 237
- Chapter 11 Integration of cognitive computing and AI for smart healthcare 267
- Chapter 12 An overview of recommender systems in the healthcare domain: significant contributions, challenges, and future scope 293
- Chapter 13 Advancements and challenges of using natural language processing in the healthcare sector 317
- Chapter 14 Intraocular pressure monitoring system for glaucoma patients using IoT and machine learning 343
- Chapter 15 A machine learning approach to voice analysis in Parkinson’s disease diagnosis 365
- Index 375
