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.
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References
ADACE CLM: ADACE - Association of ABI of Castilla - La Mancha. https://www.adaceclm.org/
Choudhry, O.J., Prestigiacomo, C.J., Gala, N., Slasky, S., Sifri, Z.C.: Delayed neurological deterioration after mild head injury: cause, temporal course, and outcomes. Neurosurgery 73(5), 753–760 (2013)
Desgraupes, B.: Clustering indices (2017)
FITBIR: Federal Interagency Traumatic Brain Injury Research (FITBIR). https://fitbir.nih.gov/
FITBIR: Transforming Re-search and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) – Adult. https://fitbir.nih.gov/portal/study/viewStudyAction!view.action?studyId=FITBIR-STUDY0000267
Fred, A.: Finding consistent clusters in data partitions. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 309–318. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-48219-9_31
Lenrow, D.A.: Physical medicine and rehabilitation: an update for internists. Med. Clin. North Am. 104(2), xvii–xviii (2020)
Liu, Y., Li, Z., Xiong, H., Gao, X., Wu, J.: Understanding of internal clustering validation measures. In: 2010 IEEE International Conference on Data Mining, pp. 911–916. IEEE (2010)
Montero, F., López-Jaquero, V., Navarro, E., Sánchez, E.: Computer-aided relearning activity patterns for people with acquired brain injury. Comput. Educ. 57(1), 1149–1159 (2011)
Moya, A., Navarro, E., Jaén, J., López-Jaquero, V., Capilla, R.: Exploiting variability in the design of genetic algorithms to generate telerehabilitation activities. Appl. Soft Comput., 108441 (2022)
Moya, A., Pretel, E., Navarro, E., Jaén, J.: A systematic literature review of clustering techniques for patients with traumatic brain injury. Artif. Intell. Rev. (2023)
Nagin, D.S., Odgers, C.L.: Group-based trajectory modeling in clinical research. Annu. Rev. Clin. Psychol. 6(1), 109–138 (2010)
Network, T.A.: Definition of ABI. http://www.abinetwork.ca/definition
Neubauer, T.R., Peres, S.M., Fantinato, M., Lu, X., Reijers, H.A.: Interactive clustering: a scoping review. Artif. Intell. Rev. 54(4), 2765–2826 (2021)
Parimbelli, E., Marini, S., Sacchi, L., Bellazzi, R.: Patient similarity for precision medicine: a systematic review (2018). https://doi.org/10.1016/j.jbi.2018.06.001
Podell, J., et al.: Rapid prediction of secondary neurologic decline after traumatic brain injury: a data analytic approach. Sci. Rep. 13(1), 403 (2023)
Saxena, A., et al.: A review of clustering techniques and developments. Neurocomputing 267, 664–681 (2017)
Strehl, A., Ghosh, J.: Cluster ensembles-a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2002)
Trisuciana, F.M., Witarsyah, D., Sutoyo, E., Machado, J.M.: Clustering of COVID-19 vaccination recipients in DKI Jakarta using the K-medoids algorithm. In: 2022 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS), pp. 01–07. IEEE (2022)
UN: Convention on the rights of persons with disabilities (2022)
Wilson, J.T., Pettigrew, L.E., Teasdale, G.M.: Structured interviews for the Glasgow outcome scale and the extended Glasgow outcome scale: guidelines for their use. J. Neurotrauma 15(8), 573–585 (1998)
Wu, J., Song, C.-H., Kong, J.M., Lee, W.D.: Extended mean field annealing for clustering incomplete data. In: 2007 International Symposium on Information Technology Convergence (ISITC 2007), pp. 8–12. IEEE (2007)
Wu, X., Ma, T., Cao, J., Tian, Y., Alabdulkarim, A.: A comparative study of clustering ensemble algorithms. Comput. Electr. Eng. 68, 603–615 (2018)
Acknowledgements
This paper is part of the R+D+i projects PID2019-108915RB-I00 and PID2022-140907OB-I00, and the grant PRE2020-094056 funded by MCIN/AEI/10.13039/501100011033. It has also been funded by the University of Castilla-La Mancha (2022-GRIN-34436) and by ‘ERDF A way to make Europe, the PhD scholarship 2019-PREDUCLM-10772 and co-financed by the FSE Operational Programme 2014-2020 of Castilla-La Mancha through Axis 3.
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Moya, A., Zhinin-Vera, L., Navarro, E., Jaen, J., Machado, J. (2023). Clustering ABI Patients for a Customized Rehabilitation Process. In: Bravo, J., Urzáiz, G. (eds) Proceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023). UCAmI 2023. Lecture Notes in Networks and Systems, vol 842. Springer, Cham. https://doi.org/10.1007/978-3-031-48642-5_21
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