Using Machine Learning Techniques in eEarlyCare Precision Diagnosis and Intervention in 0–6 years Old

Sáiz-Manzanares, María Consuelo

doi:10.1007/978-3-031-42519-6_28

Cited by 1 publication

(3 citation statements)

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“…In addition, applying data visualisation techniques to clustering algorithms will make it easier for early care professionals to rapidly analyse results and better understand them [34]. This study has shown that the interpretation of functional development in different types of patients can be read differently depending on other variables, such as chronological age and type of impairment [18,39]. In addition, applying ML techniques made it possible to study patients' characteristics (age; type of impairment) and the significance of those characteristics in the results found.…”

Section: Discussionmentioning

confidence: 97%

“…The sample was split into two groups. Group 1 comprised 49 patients at a care centre for people with motor impairments (18 15 with impairments in communication and language development, 5 with autism spectrum impairments, and 1 with visual impairment. All patients in the two groups had functional developmental abilities in the 0-6 years age range, regardless of their chronological age.…”

Section: Participantsmentioning

confidence: 99%

“…This resource allows the transmission and interconnection of data in the cloud and subsequent processing by applying ML or DL techniques. This functionality may help improve diagnostic and treatment decision-making [17,18].…”

Section: Introductionmentioning

confidence: 99%

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

Sáiz-Manzanares,

Solórzano Mulas,

Escolar-Llamazares

et al. 2024

Children

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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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Section: Discussionmentioning

confidence: 97%

Section: Participantsmentioning

confidence: 99%