Sáiz-Manzanares María Consuelo, Solórzano Mulas Almudena, Escolar-Llamazares María Camino, Alcantud Marín Francisco, Rodríguez-Arribas Sandra, Velasco-Saiz Rut
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.
Unidad de Atención Temprana, ASPACE Salamanca, 37185 Villamayor de Armuña, Spain.
Children (Basel). 2024 Mar 22;11(4):381. doi: 10.3390/children11040381.
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.
技术和人工智能（智能医疗）的进步为健康科学领域的精准干预开辟了一系列可能性。本研究的目的是分析使用监督式（预测和分类）和无监督式（聚类）机器学习技术来分析与0至6岁发育年龄患者功能技能发展相关结果的功能。我们研究了113名患者的样本，其中49名在专门的运动障碍患者中心接受护理（第1组），64名在针对不同障碍患者的特定早期护理项目中接受护理（第2组）。结果表明，在第1组中，实足年龄对功能技能发展的预测率为85%，在第2组中为65%。检测到的分类变量是上肢的功能发育。在每组中检测到两个聚类，这使我们能够确定每名患者在功能技能方面的功能发展模式。智能医疗资源在早期护理领域有着广阔的前景。然而，需要规划网络应用中的数据记录，并且需要通过机器学习技术实现结果的自动化。
