Machine learning techniques to predict the effectiveness of music therapy: A randomized controlled trial

Alfredo Raglio, Marcello Imbriani, Chiara Imbriani, Paola Baiardi, Sara Manzoni, Marta Gianotti, Mauro Castelli, Leonardo Vanneschi, Francisco Vico, Luca Manzoni

Research output: Contribution to journalArticle

Abstract

Background: The literature shows the effectiveness of music listening, but which factors and what types of music produce therapeutic effects, as well as how music therapists can select music, remain unclear. Here, we present a study to establish the main predictive factors of music listening's relaxation effects using machine learning methods. Methods: Three hundred and twenty healthy participants were evenly distributed by age, education level, presence of musical training, and sex. Each of them listened to music for nine minutes (either to their preferred music or to algorithmically generated music). Relaxation levels were recorded using a visual analogue scale (VAS) before and after the listening experience. The participants were then divided into three classes: increase, decrease, or no change in relaxation. A decision tree was generated to predict the effect of music listening on relaxation. Results: A decision tree with an overall accuracy of 0.79 was produced. An analysis of the structure of the decision tree yielded some inferences as to the most important factors in predicting the effect of music listening, particularly the initial relaxation level, the combination of education and musical training, age, and music listening frequency. Conclusions: The resulting decision tree and analysis of this interpretable model makes it possible to find predictive factors that influence therapeutic music listening outcomes. The strong subjectivity of therapeutic music listening suggests the use of machine learning techniques as an important and innovative approach to supporting music therapy practice.

Original languageEnglish
Article number105160
JournalComputer Methods and Programs in Biomedicine
Volume185
DOIs
Publication statusPublished - 1 Mar 2020

Fingerprint

Music Therapy
Music
Decision trees
Learning systems
Randomized Controlled Trials
Decision Trees
Education
Decision theory
Machine Learning
Decision Support Techniques
Therapeutic Uses
Visual Analog Scale

Keywords

  • Decision tree methods
  • Machine learning techniques
  • Medicine
  • Therapeutic music listening
  • Therapeutic predictivity

Cite this

Raglio, Alfredo ; Imbriani, Marcello ; Imbriani, Chiara ; Baiardi, Paola ; Manzoni, Sara ; Gianotti, Marta ; Castelli, Mauro ; Vanneschi, Leonardo ; Vico, Francisco ; Manzoni, Luca. / Machine learning techniques to predict the effectiveness of music therapy : A randomized controlled trial. In: Computer Methods and Programs in Biomedicine. 2020 ; Vol. 185.
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abstract = "Background: The literature shows the effectiveness of music listening, but which factors and what types of music produce therapeutic effects, as well as how music therapists can select music, remain unclear. Here, we present a study to establish the main predictive factors of music listening's relaxation effects using machine learning methods. Methods: Three hundred and twenty healthy participants were evenly distributed by age, education level, presence of musical training, and sex. Each of them listened to music for nine minutes (either to their preferred music or to algorithmically generated music). Relaxation levels were recorded using a visual analogue scale (VAS) before and after the listening experience. The participants were then divided into three classes: increase, decrease, or no change in relaxation. A decision tree was generated to predict the effect of music listening on relaxation. Results: A decision tree with an overall accuracy of 0.79 was produced. An analysis of the structure of the decision tree yielded some inferences as to the most important factors in predicting the effect of music listening, particularly the initial relaxation level, the combination of education and musical training, age, and music listening frequency. Conclusions: The resulting decision tree and analysis of this interpretable model makes it possible to find predictive factors that influence therapeutic music listening outcomes. The strong subjectivity of therapeutic music listening suggests the use of machine learning techniques as an important and innovative approach to supporting music therapy practice.",
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Machine learning techniques to predict the effectiveness of music therapy : A randomized controlled trial. / Raglio, Alfredo; Imbriani, Marcello; Imbriani, Chiara; Baiardi, Paola; Manzoni, Sara; Gianotti, Marta; Castelli, Mauro; Vanneschi, Leonardo; Vico, Francisco; Manzoni, Luca.

In: Computer Methods and Programs in Biomedicine, Vol. 185, 105160, 01.03.2020.

Research output: Contribution to journalArticle

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T2 - A randomized controlled trial

AU - Raglio, Alfredo

AU - Imbriani, Marcello

AU - Imbriani, Chiara

AU - Baiardi, Paola

AU - Manzoni, Sara

AU - Gianotti, Marta

AU - Castelli, Mauro

AU - Vanneschi, Leonardo

AU - Vico, Francisco

AU - Manzoni, Luca

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