Machine learning techniques to predict the effectiveness of music therapy: A randomized controlled trial
Raglio A, Imbriani M, Imbriani C, Baiardi P, Manzoni S, Gianotti M, Castelli M, Vanneschi L, Vico F, Manzoni L
Computer methods and programs in biomedicine · 6 citas
Etiquetas de revisión
Financiación no declaradaDatos neutrales que nuestra revisión registró sobre cómo se hizo este estudio. Describen el método, nunca si nos gusta el resultado.
Cómo se estudió
- Diseño
- Ensayo controlado aleatorizado (indexado por PubMed)
- Estudiado en
- Personas
- Resultado principal
- Marcadores de salud y funcionamiento
Quién lo pagó
- Financiación
- Financiación no declarada
Publicación
- Publicado
- 2019-10-29 · Comput Methods Programs Biomed · vol. 185 · pág. 105160
- Editorial
- Elsevier BV
- Citado
- 38 citas · más que el 90% de estudios similares · 2.7× el promedio de su campo
- Referencias
- 38 trabajos
- Acceso
- Acceso abierto (copia en repositorio) · CC-BY-NC-ND
- Áreas de investigación
- Music Therapy and Health · Neuroscience and Music Perception · Artificial Intelligence in Healthcare
- Palabras clave
- music therapy, music listening, relaxation, predictive factors, decision tree, music selection, musical training, individual differences
- MeSH
- humans, music therapy, adolescent, adult, aged, middle aged, child, child, preschool, infant, female, male, machine learning
10 autores
- Alfredo Raglio🇮🇹 · autor de correspondenciaIstituti Clinici Scientifici Maugeri
- Marcello Imbriani🇮🇹Istituti Clinici Scientifici Maugeri
- Chiara Imbriani🇮🇹Istituti Clinici Scientifici Maugeri
- Paola Baiardi🇮🇹Istituti Clinici Scientifici Maugeri
- Sara Manzoni
- Marta Gianotti
- Mauro Castelli🇵🇹Universidade Nova de Lisboa
- Leonardo Vanneschi🇵🇹Universidade Nova de Lisboa
- Francisco J. Vico🇪🇸Universidad de Málaga
- Luca Manzoni🇮🇹University of Trieste; University of Milano-Bicocca
Resumen
El texto original del estudio, en el idioma en que se publicó.
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.
Resumen vía Europe PMC. Los derechos de autor siguen siendo de los autores o la editorial.
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