2023
Moya, A.; Zhinin-Vera, L.; Navarro, E.; Jaen, J.; Machado, J.
Clustering ABI Patients for a Customized Rehabilitation Process Proceedings Article
Em: J., Urzaiz G. Bravo (Ed.): pp. 217-228, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 23673370, (cited By 0; Conference of 15th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2023 ; Conference Date: 28 November 2023 Through 29 November 2023; Conference Code:304769).
Resumo | Links | BibTeX | Etiquetas: Acquired brain injuries; Amount of information; Clusterings; Comparative analyzes; Medical conditions; Quality of life; Rehabilitation activities; Systematic methodology, Clustering algorithms; Patient treatment, Patient rehabilitation
@inproceedings{Moya2023217,
title = {Clustering ABI Patients for a Customized Rehabilitation Process},
author = {A. Moya and L. Zhinin-Vera and E. Navarro and J. Jaen and J. Machado},
editor = {Urzaiz G. Bravo J.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178644656&doi=10.1007%2f978-3-031-48642-5_21&partnerID=40&md5=5f6a0755f77fbc70a3d9148792855ca9},
doi = {10.1007/978-3-031-48642-5_21},
issn = {23673370},
year = {2023},
date = {2023-01-01},
journal = {Lecture Notes in Networks and Systems},
volume = {842 LNNS},
pages = {217-228},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Acquired Brain Injury (ABI) is a medical condition resulting from injury or disease that affects the functioning of the brain. The incidence of ABI has increased in recent years, highlighting the need for a comprehensive approach to treatment and rehabilitation to improve patients’ quality of life. Developing appropriate therapies for these patients is a challenging task because of the wide diversity of effects and severity they may suffer. This problem exacerbates the complexity of designing the rehabilitation activities, which is a time-consuming and complicated task that may cause poor patient recovery, if such activities are poorly designed. In order to overcome this problem, it is common practice to create groups of patients with similar complaints and deficits and to design rehabilitation activities that may be reused internally by such groups, facilitating comparative analyses. Usually, such grouping is conducted by specialists who may neglect to detect commonalities due to the huge amount of information to be processed. In this work, a clustering of ABI patients is performed following a systematic methodology, from preprocessing the data to applying appropriate clustering algorithms, in order to guarantee an adequate clustering of ABI patients. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 0; Conference of 15th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2023 ; Conference Date: 28 November 2023 Through 29 November 2023; Conference Code:304769},
keywords = {Acquired brain injuries; Amount of information; Clusterings; Comparative analyzes; Medical conditions; Quality of life; Rehabilitation activities; Systematic methodology, Clustering algorithms; Patient treatment, Patient rehabilitation},
pubstate = {published},
tppubtype = {inproceedings}
}
Acquired Brain Injury (ABI) is a medical condition resulting from injury or disease that affects the functioning of the brain. The incidence of ABI has increased in recent years, highlighting the need for a comprehensive approach to treatment and rehabilitation to improve patients’ quality of life. Developing appropriate therapies for these patients is a challenging task because of the wide diversity of effects and severity they may suffer. This problem exacerbates the complexity of designing the rehabilitation activities, which is a time-consuming and complicated task that may cause poor patient recovery, if such activities are poorly designed. In order to overcome this problem, it is common practice to create groups of patients with similar complaints and deficits and to design rehabilitation activities that may be reused internally by such groups, facilitating comparative analyses. Usually, such grouping is conducted by specialists who may neglect to detect commonalities due to the huge amount of information to be processed. In this work, a clustering of ABI patients is performed following a systematic methodology, from preprocessing the data to applying appropriate clustering algorithms, in order to guarantee an adequate clustering of ABI patients. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.