Use of Digitalisation and Machine Learning Techniques in Therapeutic Intervention at Early Ages: Supervised and Unsupervised Analysis

  1. Sáiz-Manzanares, María Consuelo 2
  2. Solórzano Mulas, Almudena 3
  3. Escolar-Llamazares, María Camino 2
  4. Alcantud Marín, Francisco 1
  5. Rodríguez-Arribas, Sandra 4
  6. Velasco-Saiz, Rut 5
  1. 1 Department of Developmental and Educational Psychology, Universitat de València, 46010 València, Spain
  2. 2 DATAHES Research Group, Consolidated Research Unit Nº. 348, Departamento de Ciencias de la Salud, Facultad de Ciencias de la Salud, Universidad de Burgos, 09001 Burgos, Spain
  3. 3 Unidad de Atención Temprana, ASPACE Salamanca, 37185 Villamayor de Armuña, Spain
  4. 4 BEST-AI Research Group, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, 09006 Burgos, Spain
  5. 5 Facultad de Ciencias de la Salud, Universidad de Burgos, 09001 Burgos, Spain
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Revista:
Children

ISSN: 2227-9067

Año de publicación: 2024

Volumen: 11

Número: 4

Páginas: 1-17

Tipo: Artículo

DOI: 10.3390/CHILDREN11040381 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Children

Objetivos de desarrollo sostenible

Resumen

Advances in technology and artificial intelligence (smart healthcare) open up a range of possibilities for precision intervention in the field of health sciences. The objectives of this study were to analyse the functionality of using supervised (prediction and classification) and unsupervised (clustering) machine learning techniques to analyse results related to the development of functional skills in patients at developmental ages of 0–6 years. We worked with a sample of 113 patients, of whom 49 were cared for in a specific centre for people with motor impairments (Group 1) and 64 were cared for in a specific early care programme for patients with different impairments (Group 2). The results indicated that in Group 1, chronological age predicted the development of functional skills at 85% and in Group 2 at 65%. The classification variable detected was functional development in the upper extremities. Two clusters were detected within each group that allowed us to determine the patterns of functional development in each patient with respect to functional skills. The use of smart healthcare resources has a promising future in the field of early care. However, data recording in web applications needs to be planned, and the automation of results through machine learning techniques is required.

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