A Data Mining Approach for Visual and Analytical Identification of Neurorehabilitation Ranges in Traumatic Brain Injury Cognitive Rehabilitation
Traumatic brain injury (TBI) is a critical public health and socioeconomic problem throughout the world. Cognitive rehabilitation (CR) has become the treatment of choice for cognitive impairments after TBI. It consists of hierarchically organized tasks that require repetitive use of impaired cogniti...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2015/823562 |
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| author | Alejandro García-Rudolph Karina Gibert |
| author_facet | Alejandro García-Rudolph Karina Gibert |
| author_sort | Alejandro García-Rudolph |
| collection | DOAJ |
| description | Traumatic brain injury (TBI) is a critical public health and socioeconomic problem throughout the world. Cognitive rehabilitation (CR) has become the treatment of choice for cognitive impairments after TBI. It consists of hierarchically organized tasks that require repetitive use of impaired cognitive functions. One important focus for CR professionals is the number of repetitions and the type of task performed throughout treatment leading to functional recovery. However, very little research is available that quantifies the amount and type of practice. The Neurorehabilitation Range (NRR) and the Sectorized and Annotated Plane (SAP) have been introduced as a means of identifying formal operational models in order to provide therapists with decision support information for assigning the most appropriate CR plan. In this paper we present a novel methodology based on combining SAP and NRR to solve what we call the Neurorehabilitation Range Maximal Regions (NRRMR) problem and to generate analytical and visual tools enabling the automatic identification of NRR. A new SAP representation is introduced and applied to overcome the drawbacks identified with existing methods. The results obtained show patterns of response to treatment that might lead to reconsideration of some of the current clinical hypotheses. |
| format | Article |
| id | doaj-art-23e1d5cf7bef4809840f5a87986185a7 |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-23e1d5cf7bef4809840f5a87986185a72025-02-03T01:09:44ZengWileyAbstract and Applied Analysis1085-33751687-04092015-01-01201510.1155/2015/823562823562A Data Mining Approach for Visual and Analytical Identification of Neurorehabilitation Ranges in Traumatic Brain Injury Cognitive RehabilitationAlejandro García-Rudolph0Karina Gibert1Institut Guttmann, Institut Universitari de Neurorehabilitació adscrit a la UAB, Badalona, Barcelona, SpainDepartament d’Estadística i Investigació Operativa, Universitat Politècnica de Catalunya (BarcelonaTech), Jordi Girona 1-3, 08034 Barcelona, SpainTraumatic brain injury (TBI) is a critical public health and socioeconomic problem throughout the world. Cognitive rehabilitation (CR) has become the treatment of choice for cognitive impairments after TBI. It consists of hierarchically organized tasks that require repetitive use of impaired cognitive functions. One important focus for CR professionals is the number of repetitions and the type of task performed throughout treatment leading to functional recovery. However, very little research is available that quantifies the amount and type of practice. The Neurorehabilitation Range (NRR) and the Sectorized and Annotated Plane (SAP) have been introduced as a means of identifying formal operational models in order to provide therapists with decision support information for assigning the most appropriate CR plan. In this paper we present a novel methodology based on combining SAP and NRR to solve what we call the Neurorehabilitation Range Maximal Regions (NRRMR) problem and to generate analytical and visual tools enabling the automatic identification of NRR. A new SAP representation is introduced and applied to overcome the drawbacks identified with existing methods. The results obtained show patterns of response to treatment that might lead to reconsideration of some of the current clinical hypotheses.http://dx.doi.org/10.1155/2015/823562 |
| spellingShingle | Alejandro García-Rudolph Karina Gibert A Data Mining Approach for Visual and Analytical Identification of Neurorehabilitation Ranges in Traumatic Brain Injury Cognitive Rehabilitation Abstract and Applied Analysis |
| title | A Data Mining Approach for Visual and Analytical Identification of Neurorehabilitation Ranges in Traumatic Brain Injury Cognitive Rehabilitation |
| title_full | A Data Mining Approach for Visual and Analytical Identification of Neurorehabilitation Ranges in Traumatic Brain Injury Cognitive Rehabilitation |
| title_fullStr | A Data Mining Approach for Visual and Analytical Identification of Neurorehabilitation Ranges in Traumatic Brain Injury Cognitive Rehabilitation |
| title_full_unstemmed | A Data Mining Approach for Visual and Analytical Identification of Neurorehabilitation Ranges in Traumatic Brain Injury Cognitive Rehabilitation |
| title_short | A Data Mining Approach for Visual and Analytical Identification of Neurorehabilitation Ranges in Traumatic Brain Injury Cognitive Rehabilitation |
| title_sort | data mining approach for visual and analytical identification of neurorehabilitation ranges in traumatic brain injury cognitive rehabilitation |
| url | http://dx.doi.org/10.1155/2015/823562 |
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