Recommender Systems for Medicine and Music (Studies in Computational Intelligence, 946)

Preface
Acknowledgements
Contents
Contributors
Recommendation Systems in Healthcare
	1 Introduction to Recommender Systems
	2 Recommender System Classification
		2.1 Collaborative Filtering
		2.2 Content-Based Recommender Systems
		2.3 Knowledge-Based Recommender Systems
		2.4 Group Recommender Systems
		2.5 Hybrid Recommender Systems
	References
Recommender Systems in Healthcare: A Socio-Technical Systems Approach
	1 Introduction
	2 Recommender Systems
		2.1 Technological and Social Issues in Recommender Systems
		2.2 Applications of Recommender Systems in Healthcare
	3 Socio-Technical Systems and Their Design
		3.1 Healthcare System as a Socio-Technical System
		3.2 Autonomous Agents in a Socio-Technical System
		3.3 Workload Analysis in a Socio-Technical System
	4 Design Considerations for RS in Healthcare: A Socio-Technical Approach
		4.1 Technical Agents: Regulations in Healthcare
		4.2 Human Agents: Empowerment, Patient- and Relationship-Centered Care
		4.3 Human Agents: Human-Driven Care
	5 Discussion
	References
Computer Methods for Localization  of the Subthalamic Nucleus During Deep Brain Stimulation Surgeries  for Treatment of Parkinson Disease
	1 Description of the Problem
	2 Characteristics of Microelectrode Recorded Signal
		2.1 Action Potentials
		2.2 Background Activity
	3 Attributes
		3.1 Spike Activity-Based Attributes
		3.2 Preprocessing for Background Activity-Based Attributes
		3.3 Background Activity-Based Attributes
		3.4 Moving Average-Based Attributes
		3.5 Temporal Attributes
		3.6 Narrow Frequency Band-Based Attributes
	4 Evaluation and Interpretation
		4.1 Spike Activity-Based Attributes
		4.2 Narrow Frequency Band-Based Attributes
		4.3 Attributes Based-On and Derived-From Background Activity
	5 Conclusions
	References
Stutter Detection and Remediation  in Speech
	1 Introduction and Background
	2 Stutter Detection System
		2.1 Identifying Potentially-Undesired Audio Segments
		2.2 Labeling Potentially Undesired Segments
		2.3 Constructing the Classifiers
	3 Experiments and Results
	4 Crowdsourcing Stutter Labels Web-Platform
		4.1 System Overview
		4.2 Creating a Crowdsourcing Study
		4.3 Logic Behind Segment-Worker Label Assignment
	5 Conclusion
	References
Personalizing Patients to Enable Shared Decision Making
	1 Introduction
	2 Healthcare Cost and Utilization Project (H-CUP)
	3 Predicting Medical Outcomes
	4 Treatment Plan Visualization
	5 Personalizing Treatment Plan
	6 Conclusion
	References
An LSTM-based Approach for Insulin and Carbohydrate Recommendations in Type 1 Diabetes Self-Management
	1 Introduction and Motivation
	2 Three Recommendation Scenarios
	3 Baseline Models and Neural Architectures
	4 Using the OhioT1DM Dataset for Recommendation Examples
		4.1 From Meals and Bolus Events to Recommendation Examples
	5 Experimental Methodology and Results
		5.1 Experimental Results
	6 Conclusion
	References
Music Recommendation Systems: A Survey
	1 Introduction
	2 Music Recommendation Systems
	3 Personalization of Music Recommendation Systems
		3.1 Emotions
		3.2 Personality
		3.3 Social Context
		3.4 New Interfaces
		3.5 Automatic Music Generation
	4 Summary
	References
Repeated Listens in the Music Discovery Process
	1 The Long-Tail Problem
	2 Causes of the Long-Tail Problem
	3 Potential Solutions to the Long-Tail Problem
	4 Effect of Repeated Listens on Memory and Liking
	5 Music Familiarity and Its Influence on Listening Behavior
	6 Aspects of Familiarity
	7 Familiarity and Emotions
	8 How Familiarity Influences Listening Behavior
	9 Music Listening Study
	10 Experiment Methodology
	11 Results and Discussion
	12 Conclusions and Future Work
	References
What Songs We Listen to Together: Automatic Music Selection for Groups
	1 Introduction
	2 Issues in Group Music Selection
	3 Existing Works
		3.1 MusicFX ch9McCarthyCSCW98
		3.2 Flytrap ch9CrossenIUI2002
		3.3 GroupFun ch9PopescuCHI2012
		3.4 BlueMusic ch9MahatoCHI2008
		3.5 Other Works
	4 Case Study: A Bluetooth-Based Music Selection Application
		4.1 Concept
		4.2 System Design
		4.3 Implementation
		4.4 Evaluation of Preferences for Owned Songs
		4.5 Evaluation of Preferences for Unowned Songs
	5 Discussion
	6 Conclusion
	References
Body Data for Music Information Retrieval Tasks
	1 Music and the Body
	2 Music Content Retrieval
		2.1 Audio Content-Based Music Retrieval
		2.2 Content Retrieval Using Melody
	3 Body Data
		3.1 Body Data and Music Creation
		3.2 Body Data for Music Retrieval
	4 Search and Retrieval Algorithms
		4.1 Multimodal Retrieval
		4.2 EEG Data for Music Information Retrieval
		4.3 Motion Capture
	5 Methods for Multimodal Retrieval
		5.1 Time Series Analysis
		5.2 Canonical Correlation Analysis
	6 Structures of Multimodal Retrieval
		6.1 Granularity and Specificity
		6.2 Intermediate Domain Representation
	7 Conclusions
	References
Music Recommendation Based  on Emotion Tracking of Musical Performances
	1 Introduction
	2 Related Work
	3 Preparatory Activities for Finding Similarities
		3.1 Annotation of Music Data for Regressor Training
		3.2 Training Regressors for Emotion Prediction
		3.3 Automatic Alignment of Different Performances
	4 Results of Emotion Tracking
		4.1 Arousal and Valence Trajectories
		4.2 Visual Emotion Tracking on the A-V Plane
		4.3 Visual Emotion Tracking on the A-V Plane After Ranking
	5 Similarity Findings
		5.1 Metrics of Similarity
		5.2 Joining Arousal and Valence Sequences
		5.3 Results of Calculating Similarity Matrices
	6 Evaluation
		6.1 Ground Truth Similarity Matrix
		6.2 Evaluation Results
	7 Conclusions
	References
Music and Healthcare Recommendation Systems
	1 Introduction
	2 Healthcare Recommendation Systems
	3 Systems for Recommending Music in Medicine
	4 The Influence of Music on Health
	5 Summary
	References
Emotion-Based Music Recommender System for Tinnitus Patients (EMOTIN)
	1 Introduction
		1.1 Tinnitus
		1.2 Tinnitus Retraining Therapy
		1.3 Music Therapy
	2 Background
		2.1 RECTIN—Recommender for Tinnitus
		2.2 eTRT—Electronic Tinnitus Retraining Therapy
	3 Methods
		3.1 Music Recommendations
		3.2 Music Therapy Protocol
		3.3 Detecting Affective States
		3.4 Emotion Model
		3.5 Music Emotion Recognition
		3.6 Audio Notching
		3.7 Emotion-Based Music Recommender Model
		3.8 Recommendation Algorithm
	4 Results
		4.1 Audio Feature Selection for Music Emotion Recognition
		4.2 Regression Models for Music Emotion Recognition
	5 Discussion
	References
A Model of Typhlo Music Therapy  in Educational and Rehabilitation Work with Visually Impaired Persons
	1 Introduction
	2 Music—The Most Accessible of All the Fine Arts
	3 The Concept of Typhlo Music Therapy
	4 Typhlo Music Therapeutic Procedure
	5 Acoustic Material
	6 Music Therapy Sessions
	7 Conclusions
	8 Notes on the Authors
	References