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Recommender Systems for Medicine and Music

Overview of attention for book
Cover of 'Recommender Systems for Medicine and Music'

Table of Contents

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    Book Overview
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    Chapter 1 Recommendation Systems in Healthcare
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    Chapter 2 Recommender Systems in Healthcare: A Socio-Technical Systems Approach
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    Chapter 3 Computer Methods for Localization of the Subthalamic Nucleus During Deep Brain Stimulation Surgeries for Treatment of Parkinson Disease
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    Chapter 4 Stutter Detection and Remediation in Speech
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    Chapter 5 Personalizing Patients to Enable Shared Decision Making
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    Chapter 6 An LSTM-based Approach for Insulin and Carbohydrate Recommendations in Type 1 Diabetes Self-Management
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    Chapter 7 Music Recommendation Systems: A Survey
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    Chapter 8 Repeated Listens in the Music Discovery Process
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    Chapter 9 What Songs We Listen to Together: Automatic Music Selection for Groups
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    Chapter 10 Body Data for Music Information Retrieval Tasks
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    Chapter 11 Music Recommendation Based on Emotion Tracking of Musical Performances
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    Chapter 12 Music and Healthcare Recommendation Systems
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    Chapter 13 Emotion-Based Music Recommender System for Tinnitus Patients (EMOTIN)
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    Chapter 14 A Model of Typhlo Music Therapy in Educational and Rehabilitation Work with Visually Impaired Persons
Attention for Chapter 5: Personalizing Patients to Enable Shared Decision Making
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Citations

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Chapter title
Personalizing Patients to Enable Shared Decision Making
Chapter number 5
Book title
Recommender Systems for Medicine and Music
Published by
Springer, Cham, April 2021
DOI 10.1007/978-3-030-66450-3_5
Book ISBNs
978-3-03-066448-0, 978-3-03-066450-3
Authors

Mamoun T. Mardini, Amal Hashky, Zbigniew W. Raś, Mardini, Mamoun T., Hashky, Amal, Raś, Zbigniew W.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 33%
Unknown 2 67%
Readers by discipline Count As %
Computer Science 1 33%
Unknown 2 67%

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