A systematic literature review of clustering techniques for patients with traumatic brain injury
While the number of people suffering from traumatic brain injury (TBI) has increased considerably in recent years, the multiple deficits of these patients makes designing the rehabilitation process a challenge for practitioners. They need to group similar patients, due to their features and/ or dise...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad de Castilla-La Mancha |
| Repositorio: | RUIdeRA. Repositorio Institucional de la UCLM |
| OAI Identifier: | oai:ruidera.uclm.es:10578/33785 |
| Acceso en línea: | https://doi.org/10.1007/s10462-023-10531-2 https://hdl.handle.net/10578/33785 |
| Access Level: | acceso embargado |
| Palabra clave: | Acquired brain injury Cluster Clustering Survey Systematic literature review Traumatic brain injury |
| Sumario: | While the number of people suffering from traumatic brain injury (TBI) has increased considerably in recent years, the multiple deficits of these patients makes designing the rehabilitation process a challenge for practitioners. They need to group similar patients, due to their features and/ or diseases in order to assign them to the same clinically significant group to facilitate the design of appropriate rehabilitation activities. The information used to group the patients depends on the type of patient as well as the possible groups to be formed. This work focuses on studying how grouping patients with TBI has been carried out so far by means of clustering algorithms. The main interest in grouping TBI patients is the need to address this heterogeneity to create clinical guidelines or rehabilitation activities for individual groups and detect the characteristic features of each group. This study’s main aims are: (1) to determine the purposes of the clustering algorithms developed for TBI patients, (2) to identify the normally considered deficits, (3) to determine the most commonly used clustering algorithms, (4) to identify the types of features usually employed for TBI clustering, (5) to analyse the data pre-processing techniques applied, (5) to identify the parameters chosen when running a clustering algorithm for TBI patients, and (6) to determine the efficiency/effectiveness achieved by clustering algorithms. |
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