Abstract
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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Aaro Jonsson C, Catroppa C, Godfrey C et al (2013) Individual profiles of predictors and their relations to 10 years outcome after childhood traumatic brain injury. Brain Inj 27:831–838. https://doi.org/10.3109/02699052.2013.775493
ADACE CLM ADACE - Association of ABI of Castilla - La Mancha. https://www.adaceclm.org/. Accessed 11 Feb 2021
Adams JW, Alvarez VE, Mez J et al (2018) Lewy body pathology and chronic traumatic encephalopathy associated with contact sports. J Neuropathol Exp Neurol 77:757–768. https://doi.org/10.1093/jnen/nly065
Addison A, Obafemi-Ajayi T (2022) Comparative single-cell RNA-sequencing cluster analysis for traumatic brain injury marker genes detection. Epic Ser Comput 83:155–164. https://doi.org/10.29007/gj4p
Adlam A-LR, Adams M, Turnbull O et al (2017) The bangor gambling task: characterising the performance of survivors of traumatic brain injury. Brain Impair 18:62–73. https://doi.org/10.1017/BrImp.2016.30
Agoston DV, Langford D (2017) Big data in traumatic brain injury; promise and challenges. Concussion 2:CNC44. https://doi.org/10.2217/cnc-2016-0013
Agrawal R, Gehrke J, Gunopulos D, Raghavan P (1998) Automatic subspace clustering of high dimensional data for data mining applications. ACM SIGMOD Rec 27:94–105. https://doi.org/10.1145/276305.276314
Ajdari A, Boyle LN, Kannan N et al (2017) Examining emergency department treatment processes in severe pediatric traumatic brain injury. J Healthc Qual 39:334–344. https://doi.org/10.1097/JHQ.0000000000000052
Åkerlund CAI, Holst A, Stocchetti N et al (2022) Clustering identifies endotypes of traumatic brain injury in an intensive care cohort: a CENTER-TBI study. Crit Care 26:228. https://doi.org/10.1186/s13054-022-04079-w
Alashwal H, El Halaby M, Crouse JJ et al (2019) The application of unsupervised clustering methods to Alzheimer’s disease. Front Comput Neurosci. https://doi.org/10.3389/fncom.2019.00031
Aldenderfer M, Blashfield R (1984) Cluster analysis. SAGE Publications, Thousand Oaks
Alim-Marvasti A, Kuleindiren N, Tiersen F et al (2022) Hierarchical clustering of prolonged post-concussive symptoms after 12 months: symptom-centric analysis and association with functional impairments. Brain Inj. https://doi.org/10.1080/026990522158229
Allen DN, Leany BD, Thaler NS et al (2010) Memory and attention profiles in pediatric traumatic brain injury. Arch Clin Neuropsychol 25:618–633. https://doi.org/10.1093/arclin/acq051
Alosco ML, Cherry JD, Huber BR et al (2020) Characterizing tau deposition in chronic traumatic encephalopathy (CTE): utility of the McKee CTE staging scheme. Acta Neuropathol 140:495–512. https://doi.org/10.1007/s00401-020-02197-9
Alvarez JA, Emory E (2006) Executive function and the frontal lobes: a meta-analytic review. Neuropsychol Rev 16:17–42. https://doi.org/10.1007/s11065-006-9002-x
Ankerst M, Breunig MM, Kriegel HP, Sander J (1999) OPTICS: ordering points to identify the clustering structure. SIGMOD Rec (ACM Spec Interes Gr Manag Data) 28:49–60. https://doi.org/10.1145/304181.304187
Arnould A, Rochat L, Azouvi P, Van der Linden M (2015) Apathetic symptom presentations in patients with severe traumatic brain injury: assessment, heterogeneity and relationships with psychosocial functioning and caregivers’ burden. Brain Inj 29:1597–1603. https://doi.org/10.3109/02699052.2015.1075156
Artiola L, Hermosillo D, Heaton R, Pardee RE (1999) Manual de normas y procedimientos para la bater{\’\i}a neuropsicológica en español. mPress, Tucson
Asgari S, Adams H, Kasprowicz M et al (2019) Feasibility of hidden markov models for the description of time-varying physiologic state after severe traumatic brain injury. Crit Care Med 47:e880–e885. https://doi.org/10.1097/CCM.0000000000003966
Bailie JM, Kennedy JE, French LM et al (2016) Profile analysis of the neurobehavioral and psychiatric symptoms following combat-related mild traumatic brain injury. J Head Trauma Rehabil 31:2–12. https://doi.org/10.1097/HTR.0000000000000142
Bailly N, Afquir S, Laporte J-D et al (2017) Analysis of injury mechanisms in head injuries in skiers and snowboarders. Med Sci Sport Exerc 49:1–10. https://doi.org/10.1249/MSS.0000000000001078
Bair E (2013) Semi-supervised clustering methods. Wiley Interdiscip Rev Comput Stat 5:349–361. https://doi.org/10.1002/wics.1270
Baird C, Haswell C, Watts A et al (2022) P83. Neurobiologically-derived, outcome-relevant subtypes of mild traumatic brain injury. Biol Psychiatry 91:S120–S121. https://doi.org/10.1016/j.biopsych.2022.02.317
Barlow KM (2013) Traumatic brain injury. Handb Clin Neurol 112:891–904. https://doi.org/10.1016/B978-0-444-52910-7.00011-8
Bayley MT, Tate R, Douglas JM et al (2014) INCOG guidelines for cognitive rehabilitation following traumatic brain injury: methods and overview. J Head Trauma Rehabil 29:290–306. https://doi.org/10.1097/HTR.0000000000000838
Beck AT, Steer RA, Brown GK, others (1987) Beck depression inventory. Harcourt Brace Jovanovich New York:
Beck AT, Epstein N, Brown G, Steer R (1993) Beck anxiety inventory. J Consult Clin Psychol
Belfry KD, Ham E, Kolla NJ, Hilton NZ (2022) Adverse childhood experiences and offending as a function of acquired brain injury among men in a high secure forensic psychiatric hospital. Can J Psychiatry. https://doi.org/10.1177/07067437221144629
Bergersen K, Halvorsen JØ, Tryti EA et al (2017) A systematic literature review of psychotherapeutic treatment of prolonged symptoms after mild traumatic brain injury. Brain Inj 31:279–289. https://doi.org/10.1080/02699052.2016.1255779
Berkhin P (2006) A survey of clustering data mining techniques. Grouping multidimensional data: recent advances in clustering. Springer, Berlin Heidelberg, pp 25–71
Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms, 1st edn. Springer US, Boston
Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10:191–203. https://doi.org/10.1016/0098-3004(84)90020-7
Brown JIM, Moulton RJ, Konasiewicz SJ, Baker AJ (1998) Cerebral oxidative metabolism and evoked potential deterioration after severe brain injury: new evidence of early posttraumatic ischemia. Neurosurgery 42:1057–1062. https://doi.org/10.1097/00006123-199805000-00060
Bui LA, Yeboah D, Steinmeister L et al (2022) Heterogeneity in blood biomarker trajectories after mild TBI revealed by unsupervised learning. IEEE/ACM Trans Comput Biol Bioinform 19:1365–1378. https://doi.org/10.1109/TCBB.2021.3091972
Cao B, Chen Y, Yu R et al (2019) Abnormal dynamic properties of functional connectivity in disorders of consciousness. NeuroImage Clin 24:102071. https://doi.org/10.1016/j.nicl.2019.102071
Carpenter GA, Grossberg S (1987) A massively parallel architecture for a self-organizing neural pattern recognition machine. Comput vis, Graph Image Process 37:54–115. https://doi.org/10.1016/S0734-189X(87)80014-2
Cerasa A, Tartarisco G, Bruschetta R et al (2022) Predicting outcome in patients with brain injury: differences between machine learning versus conventional statistics. Biomedicines 10:2267. https://doi.org/10.3390/biomedicines10092267
Chen Z, Li X, Yang M et al (2022) Optimization of deep learning models for the prediction of gene mutations using unsupervised clustering. J Pathol Clin Res. https://doi.org/10.1002/cjp2.302
Choudhry OJ, Prestigiacomo CJ, Gala N et al (2013) Delayed neurological deterioration after mild head injury: cause, temporal course, and outcomes. Neurosurgery 73:753–760. https://doi.org/10.1227/NEU.0000000000000105
Cicerone KD, Kalmar K (1995) Persistent postconcussion syndrome. J Head Trauma Rehabil 10:1–17. https://doi.org/10.1097/00001199-199510030-00002
Cieslak K, Pato M, Buckley P et al (2016) Traumatic brain injury and bipolar psychosis in the Genomic Psychiatry Cohort. Am J Med Genet Part B Neuropsychiatr Genet 171:506–512. https://doi.org/10.1002/ajmg.b.32350
Cole E, Gillespie S, Vulliamy P et al (2020) Multiple organ dysfunction after trauma. Br J Surg 107:402–412. https://doi.org/10.1002/bjs.11361
Collie A, Prang K-H (2013) Patterns of healthcare service utilisation following severe traumatic brain injury: an idiographic analysis of injury compensation claims data. Injury 44:1514–1520. https://doi.org/10.1016/j.injury.2013.03.006
Collins R, Lanham RA, Sigford BJ (2000) Reliability and validity of the Wisconsin HSS Quality of Life Inventory in traumatic brain injury. J Head Trauma Rehabil 15:1139–1148. https://doi.org/10.1097/00001199-200010000-00007
Constantine G, Buliga M, Mi Q et al (2016) Dynamic profiling: modeling the dynamics of inflammation and predicting outcomes in traumatic brain injury patients. Front Pharmacol. https://doi.org/10.3389/fphar.2016.00383
Curtiss G, Vanderploeg RD, Spencer J, Salazar AM (2001) Patterns of verbal learning and memory in traumatic brain injury. J Int Neuropsychol Soc 7:574–585. https://doi.org/10.1017/S1355617701755051
Czyżewski A, Kurowski A, Odya P, Szczuko P (2020) Multifactor consciousness level assessment of participants with acquired brain injuries employing human–computer interfaces. Biomed Eng Online 19:2. https://doi.org/10.1186/s12938-019-0746-y
DeJong J, Donders J (2010) Cluster subtypes on the California Verbal Learning Test-Second Edition (CVLT–II) in a traumatic brain injury sample. J Clin Exp Neuropsychol 32:953–960. https://doi.org/10.1080/13803391003645640
Delis D, Kramer JH, Kaplan E, Ober B (1983) California verbal learning test, research. San Antonio Psychol Corp
Demery JA, Pedraza O, Hanlon RE (2002) Differential profiles of verbal learning in traumatic brain injury. J Clin Exp Neuropsychol 24:818–827. https://doi.org/10.1076/jcen.24.6.818.8400
Department of Health (2007) What is physiological measurement? A guide to the tests and procedures conducted by physiological measurement diagnostic services. NHS
Deshpande SA, Millis SR, Reeder KP et al (1996) Verbal learning subtypes in traumatic brain injury: a replication. J Clin Exp Neuropsychol 18:836–842. https://doi.org/10.1080/01688639608408306
Dijkland SA, Foks KA, Polinder S et al (2020) Prognosis in moderate and severe traumatic brain injury: a systematic review of contemporary models and validation studies. J Neurotrauma 37:1–13. https://doi.org/10.1089/neu.2019.6401
Dimitri GM, Agrawal S, Young A et al (2017) A multiplex network approach for the analysis of intracranial pressure and heart rate data in traumatic brain injured patients. Appl Netw Sci. https://doi.org/10.1007/s41109-017-0050-3
Dimitri GM, Spasov S, Duggento A, et al (2020) Unsupervised stratification in neuroimaging through deep latent embeddings. In: Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS 2020-July, pp 1568–1571. https://doi.org/10.1109/EMBC44109.2020.9175810
Dimitri GM, Beqiri E, Placek MM et al (2022a) Modeling brain-heart crosstalk information in patients with traumatic brain injury. Neurocrit Care 36:738–750. https://doi.org/10.1007/s12028-021-01353-7
Dimitri GM, Spasov S, Duggento A et al (2022b) Multimodal and multicontrast image fusion via deep generative models. Inf Fusion 88:146–160. https://doi.org/10.1016/j.inffus.2022.07.017
Ding C, Xiaofeng H (2002) Cluster merging and splitting in hierarchical clustering algorithms. In: 2002 IEEE International Conference on Data Mining, 2002. Proceedings. IEEE Comput. Soc, pp 139–146
Doig E, Fleming J, Tooth L (2001) Patterns of community integration 2–5 years post-discharge from brain injury rehabilitation. Brain Inj 15:747–762. https://doi.org/10.1080/02699050110034343
Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern 3:32–57. https://doi.org/10.1080/01969727308546046
Dybå T, Dingsøyr T (2008) Empirical studies of agile software development: a systematic review. Inf Softw Technol 50:833–859. https://doi.org/10.1016/j.infsof.2008.01.006
Elomaa T, Rousu J (2004) Efficient multisplitting revisited: optima-preserving elimination of partition candidates. Data Min Knowl Discov 8:97–126. https://doi.org/10.1023/B:DAMI.0000015868.85039.e6
Ensign J, Maricle DE, Brown C, Mayfield JW (2012) Psychosocial subtypes on the behavior assessment system for children, second edition following pediatric traumatic brain injury. Arch Clin Neuropsychol 27:277–292. https://doi.org/10.1093/arclin/acs030
Epstein NB, Baldwin LM, Bishop DS (1983) The McMaster family assessment device*. J Marital Fam Ther 9:171–180. https://doi.org/10.1111/j.1752-0606.1983.tb01497.x
Eriksson J, Nelson D, Holst A et al (2021) Temporal patterns of organ dysfunction after severe trauma. Crit Care 25:165. https://doi.org/10.1186/s13054-021-03586-6
Eslinger P, Downey-Lamb M, Ward S et al (2002) Neuropsychological interventions: clinical research and practice. The Guilford Press, New York
Fahad A, Alshatri N, Tari Z et al (2014) A survey of clustering algorithms for big data: taxonomy and empirical analysis. IEEE Trans Emerg Top Comput 2:267–279. https://doi.org/10.1109/TETC.2014.2330519
Feldman B, Shen J, Chen C et al (2020) Perceived health after adult traumatic brain injury: a Group-Based Trajectory Modeling (GBTM) analysis. Brain Inj 34:741–750. https://doi.org/10.1080/02699052.2020.1753111
Ferris L (1996) Test of memory and learning by C. R. Reynolds and E. D. Bigler. Austin, TX: Pro-ed, 1994. Arch Clin Neuropsychol 11:251–255. https://doi.org/10.1016/S0887-6177(96)90003-7
Fisher DH (1987) Knowledge acquisition via incremental conceptual clustering. Mach Learn 2:139–172. https://doi.org/10.1023/A:1022852608280
FITBIR (2023) Federal interagency traumatic brain injury research (FITBIR). https://fitbir.nih.gov/. Accessed 5 Mar 2023
Fleming JM, Strong J (1997) The development of insight following severe traumatic brain injury: three case studies. Br J Occup Ther 60:295–300. https://doi.org/10.1177/030802269706000703
Fleming JM, Strong J, Ashton R (1996) Self-awareness of deficits in adults with traumatic brain injury: how best to measure? Brain Inj 10:1–16. https://doi.org/10.1080/026990596124674
Fleming JM, Strong J, Ashton R (1998) Cluster analysis of self-awareness levels in adults with traumatic brain injury and relationship to outcome. J Head Trauma Rehabil 13:39–51. https://doi.org/10.1097/00001199-199810000-00006
Fuest KE, Ulm B, Daum N et al (2023) Clustering of critically ill patients using an individualized learning approach enables dose optimization of mobilization in the ICU. Crit Care 27:1. https://doi.org/10.1186/s13054-022-04291-8
Galbraith S (2012) Applied missing data analysis by Craig K Enders. Aust N Z J Stat 54:251–251. https://doi.org/10.1111/j.1467-842X.2012.00656.x
Gallagher-Lepak S (1997) Development of the Wisconsin HSS Quality of Life Inventory. Diss. Abstr. Int. Sect. B Sci. Eng. 57
Gan G, Ma C, Wu J (2007) 12. Grid-based clustering algorithms. In: Data clustering: theory, algorithms, and applications. Society for Industrial and Applied Mathematics, pp 209–217
García-Rudolph A, Gibert K (2016) Understanding effects of cognitive rehabilitation under a knowledge discovery approach. Eng Appl Artif Intell 55:165–185. https://doi.org/10.1016/j.engappai.2016.06.007
Garcia-Rudolph A, Garcia-Molina A, Opisso E, Tormos Muñoz J (2020) Personalized web-based cognitive rehabilitation treatments for patients with traumatic brain injury: cluster analysis. JMIR Med Inform 8:e16077. https://doi.org/10.2196/16077
García-Rudolph A, García-Molina A, Opisso E et al (2021) Neuropsychological assessments of patients with acquired brain injury: a cluster analysis approach to address heterogeneity in web-based cognitive rehabilitation. Front Neurol. https://doi.org/10.3389/fneur.2021.701946
Gladsjo JA, Schuman CC, Evans JD et al (1999) Norms for letter and category fluency: demographic corrections for age, education, and ethnicity. Assessment 6:147–178. https://doi.org/10.1177/107319119900600204
Golden CJ, Berna G, Viena et al (1994) Grupo Editorial Hogrefe
Goldstein G, Allen DN, Caponigro JM (2010) A retrospective study of heterogeneity in neurocognitive profiles associated with traumatic brain injury. Brain Inj 24:625–635. https://doi.org/10.3109/02699051003670882
Grace J, Stout JC, Malloy PF (1999) Assessing frontal lobe behavioral syndromes with the frontal lobe personality scale. Assessment 6:269–284. https://doi.org/10.1177/107319119900600307
Gracey F, Malley D, Wagner PA, Clare I (2014) Characterising neuropsychological rehabilitation service users for service design. Soc Care Neurodisabil 5:16–28. https://doi.org/10.1108/SCN-09-2013-0034
Gravesteijn BY, Sewalt CA, Ercole A et al (2020) Toward a new multi-dimensional classification of traumatic brain injury: a collaborative european neurotrauma effectiveness research for traumatic brain injury study. J Neurotrauma 37:1002–1010. https://doi.org/10.1089/neu.2019.6764
Green P, Allen LM, Astner K (1996) The Word Memory Test: A user’s guide to the oral and computer-administered forms, US Version 1.1. Durham, NC CogniSyst
Guha S, Rastogi R, Shim K (1998) CURE: an efficient clustering algorithm for large databases. SIGMOD Rec 27:73–84. https://doi.org/10.1145/276305.276312
Guha S, Rastogi R, Shim K (2000) Rock: a robust clustering algorithm for categorical attributes. Inf Syst 25:345–366. https://doi.org/10.1016/S0306-4379(00)00022-3
Gurney JM, Loos PE, Prins M et al (2020) The prehospital evaluation and care of moderate/severe TBI in the austere environment. Mil Med 185:148–153. https://doi.org/10.1093/milmed/usz361
Hai T, Agimi Y, Stout K (2022) Clusters of conditions among US service members diagnosed with mild TBI from 2017 through 2019. Front Neurol. https://doi.org/10.3389/fneur.2022.976892
Han J, Kamber M, Pei J (2012) Data mining: concepts and techniques. Elsevier
Handy JD, Wright WG, Haskell A et al (2020) Enhanced acquisition and retention of conditioned eyeblink responses in veterans expressing PTSD symptoms: modulation by lifetime history of mild traumatic brain injury. Front Behav Neurosci. https://doi.org/10.3389/fnbeh.2020.595007
Hanna ARG, Rao C, Athanasiou T (2010) Graphs in statistical analysis. In: Athanasiou T, Debas H, Darzi A (eds) Key topics in surgical research and methodology. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 441–475
Harman-Smith YE, Mathias JL, Bowden SC et al (2013) Wechsler Adult Intelligence Scale-Third Edition profiles and their relationship to self-reported outcome following traumatic brain injury. J Clin Exp Neuropsychol 35:785–798. https://doi.org/10.1080/13803395.2013.824554
Hathaway SR, McKinley JC (1967) The MMPI manual. Psychological Corporation, New York
Heaton R (1993) Wisconsin card sorting test: Computer version 2. Odessa Psychol Assess Resour 04
Hinneburg A, Keim D (1998) An efficient approach to clustering in large multimedia databases with noise. In: Proc 4th Int Conf Knowl Discov Data Min (KDD 98)
Hinton G, Sejnowski TJ (1999) Unsupervised learning. The MIT Press
Humphreys I, Wood RL, Phillips C, Macey S (2013) The costs of traumatic brain injury: a literature review. Clin Outcomes Res. https://doi.org/10.2147/CEOR.S44625
IBM (2022) IBM SPSS software. https://www.ibm.com/analytics/spss-statistics-software. Accessed 25 Jul 2022
Insko BE (2003) Measuring presence: subjective, behavioral and physiological methods. Emerg Commun 5:110–118
Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. In: ACM Computing Surveys. ACM PUB27 New York, NY, USA, pp 264–323
Jin R, Breitbart Y, Muoh C (2009) Data discretization unification. Knowl Inf Syst 19:1–29. https://doi.org/10.1007/s10115-008-0142-6
Jonker F, Weeda W, Rauwerda K, Scherder E (2019) The bridge between cognition and behavior in acquired brain injury: a graph theoretical approach. Brain Behav 9:e01208. https://doi.org/10.1002/brb3.1208
Jonsson CA, Catroppa C, Godfrey C et al (2013) Cognitive recovery and development after traumatic brain injury in childhood: a person-oriented, longitudinal study. J Neurotrauma 30:76–83. https://doi.org/10.1089/neu.2012.2592
Juengst SB, Switzer G, Oh BM et al (2017) Conceptual model and cluster analysis of behavioral symptoms in two cohorts of adults with traumatic brain injuries. J Clin Exp Neuropsychol 39:513–524. https://doi.org/10.1080/13803395.2016.1240758
Kantardzic M (2011) Data mining: concepts, models, methods, and algorithms: Second Edition
Kaufman L, Rousseeuw PJ (1990) Partitioning around medoids (Program PAM). Finding groups in data: an introduction to cluster analysis. Wiley, Hoboken, pp 68–125
Kaufman L, Rousseeuw PJ (2005) Finding groups in data: an introduction to cluster analysis
Kennedy JE, Cooper DB, Reid MW et al (2015) Profile analyses of the personality assessment inventory following military-related traumatic brain injury. Arch Clin Neuropsychol 30:236–247. https://doi.org/10.1093/arclin/acv014
Kim J-S, Kim O-L, Koo B-H et al (2013) Neurocognitive function differentiation from the effect of psychopathologic symptoms in the disability evaluation of patients with mild traumatic brain injury. J Korean Neurosurg Soc 54:390. https://doi.org/10.3340/jkns.2013.54.5.390
Kirchner K, Zec J, Delibašić B (2016) Facilitating data preprocessing by a generic framework: a proposal for clustering. Artif Intell Rev 45:271–297. https://doi.org/10.1007/s10462-015-9446-6
Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering
Kitchenham BA, Mendes E, Travassos GH (2007) Cross versus within-company cost estimation studies: a systematic review. IEEE Trans Softw Eng 33:316–329. https://doi.org/10.1109/TSE.2007.1001
Kitchenham B, Pearl Brereton O, Budgen D et al (2009) Systematic literature reviews in software engineering—a systematic literature review. Inf Softw Technol 51:7–15. https://doi.org/10.1016/j.infsof.2008.09.009
Klijn SL, Weijenberg MP, Lemmens P et al (2017) Introducing the fit-criteria assessment plot—a visualisation tool to assist class enumeration in group-based trajectory modelling. Stat Methods Med Res 26:2424–2436. https://doi.org/10.1177/0962280215598665
Klyce DW, Perrin PB, Fisher LB et al (2022) Identifying group-based patterns of suicidal ideation over the first 10 years after moderate-to-severe TBI. J Clin Psychol 78:877–891. https://doi.org/10.1002/jclp.23282
Kohonen T (1990) The self-organizing map. Proc IEEE 78:1464–1480. https://doi.org/10.1109/5.58325
Kriegel H, Kröger P, Sander J, Zimek A (2011) Density-based clustering. Wires Data Min Knowl Discov 1:231–240. https://doi.org/10.1002/widm.30
Kroenke K, Spitzer RL, Williams JBW (2001) The PHQ-9. J Gen Intern Med 16:606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x
Kucukboyaci NE, Long C, Smith M et al (2018) Cluster analysis of vulnerable groups in acute traumatic brain injury rehabilitation. Arch Phys Med Rehabil 99:2365–2369. https://doi.org/10.1016/j.apmr.2017.11.016
Kumar A, Athar M (2017) Management of severe TBI—a review of recent literature. JHN J. https://doi.org/10.29046/JHNJ.012.1.004
Kumar RG, Rubin JE, Berger RP et al (2016) Principal components derived from CSF inflammatory profiles predict outcome in survivors after severe traumatic brain injury. Brain Behav Immun 53:183–193. https://doi.org/10.1016/j.bbi.2015.12.008
Kumar RG, Juengst SB, Wang Z et al (2018) Epidemiology of comorbid conditions among adults 50 years and older with traumatic brain injury. J Head Trauma Rehabil 33:15–24. https://doi.org/10.1097/HTR.0000000000000273
Kumar MA, Cao W, Pham HP et al (2019) Relative deficiency of plasma a disintegrin and metalloprotease with thrombospondin type 1 repeats 13 activity and elevation of human neutrophil peptides in patients with traumatic brain injury. J Neurotrauma 36:222–229. https://doi.org/10.1089/neu.2018.5696
Lam CS, McMahon BT, Priddy DA, Gehred-Schultz A (1988) Deficit awareness and treatment performance among traumatic head injury adults. Brain Inj 2:235–242. https://doi.org/10.3109/02699058809150947
Lange RT, Iverson GL, Franzen MD (2008) Comparability of neuropsychological test profiles in patients with chronic substance abuse and mild traumatic brain injury. Clin Neuropsychol 22:209–227. https://doi.org/10.1080/13854040701290062
Lee CJ, Felix ER, Levitt RC et al (2018) Traumatic brain injury, dry eye and comorbid pain diagnoses in US veterans. Br J Ophthalmol 102:667–673. https://doi.org/10.1136/bjophthalmol-2017-310509
Lenrow DA (2020) Physical medicine and rehabilitation: an update for internists. Med Clin North Am 104:xvii–xviii. https://doi.org/10.1016/j.mcna.2019.11.006
Lezak MD (1987) Relationships between personality disorders, social disturbances, and physical disability following traumatic brain injury. J Head Trauma Rehabil 2:57–69. https://doi.org/10.1097/00001199-198703000-00009
Li T, Hongyu L, Xuping F et al (2005) Dimension reduction of microarray data based on local tangent space alignment. In: Fourth IEEE Conference on Cognitive Informatics, 2005. (ICCI 2005). IEEE, pp 154–159
Li D, Zhong C, Zhang L (2010) Fuzzy c-means clustering of partially missing data sets based on statistical representation. In: 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, pp 460–464
Lin E, Yuh EL (2022) Computational approaches for acute traumatic brain injury image recognition. Front Neurol. https://doi.org/10.3389/fneur.2022.791816
Linacre JM, Heinemann AW, Wright BD et al (1994) The structure and stability of the functional independence measure. Arch Phys Med Rehabil 75:127–132. https://doi.org/10.1016/0003-9993(94)90384-0
Lindblad C, Pin E, Just D et al (2021) Fluid proteomics of CSF and serum reveal important neuroinflammatory proteins in blood–brain barrier disruption and outcome prediction following severe traumatic brain injury: a prospective, observational study. Crit Care 25:103. https://doi.org/10.1186/s13054-021-03503-x
Lingsma HF, Roozenbeek B, Steyerberg EW et al (2010) Early prognosis in traumatic brain injury: from prophecies to predictions. Lancet Neurol 9:543–554. https://doi.org/10.1016/S1474-4422(10)70065-X
Lovell MR (2016) ImPACT administration and interpretation manual. Pittsburgh, PA ImPACT Appl Inc Retrieved March 15:2016
Luckett PH, Chen C, Gordon BA et al (2023) Biomarker clustering in autosomal dominant Alzheimer’s disease. Alzheimer’s Dement 19:274–284. https://doi.org/10.1002/alz.12661
Macqueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proc Fifth Berkeley Symp Math Stat Probab. pp 281–297
Maestas KL, Sander AM, Clark AN et al (2014) Preinjury coping, emotional functioning, and quality of life following uncomplicated and complicated mild traumatic brain injury. J Head Trauma Rehabil 29:407–417. https://doi.org/10.1097/HTR.0b013e31828654b4
Malec J (2005) The Mayo–Portland Adaptability Inventory. The Center for Outcome Measurement in Brain Injury. http://www.tbims.org/combi/mpai/. Accessed 18 Mar 2022
Malec JF, Machulda MM, Smigielski JS (1993) Cluster analysis of neuropsychological test results among patients with traumatic brain injury (TBI): implications for a model of TBI-related disability. Clin Neuropsychol 7:48–58. https://doi.org/10.1080/13854049308401887
Malec JF, Kragness M, Evans RW et al (2003) Further psychometric evaluation and revision of the Mayo-Portland Adaptability Inventory in a national sample. J Head Trauma Rehabil 18:479–492. https://doi.org/10.1097/00001199-200311000-00002
Malhotra R (2015) A systematic review of machine learning techniques for software fault prediction. Appl Soft Comput 27:504–518. https://doi.org/10.1016/j.asoc.2014.11.023
Malinowsky C, Fallahpour M, Lund ML et al (2018) Skill clusters of ability to manage everyday technology among people with and without cognitive impairment, dementia and acquired brain injury. Scand J Occup Ther 25:99–107. https://doi.org/10.1080/11038128.2017.1298665
Mawdsley E, Reynolds B, Cullen B (2021) A systematic review of the effectiveness of machine learning for predicting psychosocial outcomes in acquired brain injury: which algorithms are used and why? J Neuropsychol 15:319–339. https://doi.org/10.1111/jnp.12244
McIntyre A, Rice D, Janzen S et al (2020) Anxiety, depression, and quality of life among subgroups of individuals with acquired brain injury: the role of anxiety sensitivity and experiential avoidance. NeuroRehabilitation 47:45–53. https://doi.org/10.3233/NRE-203080
McKee AC, Stein TD, Nowinski CJ et al (2013) The spectrum of disease in chronic traumatic encephalopathy. Brain. https://doi.org/10.1093/brain/aws307
Medley AR, Powell T, Worthington A et al (2010) Brain injury beliefs, self-awareness, and coping: a preliminary cluster analytic study based within the self-regulatory model. Neuropsychol Rehabil 20:899–921. https://doi.org/10.1080/09602011.2010.517688
Mehta V, Bawa S, Singh J (2020) Analytical review of clustering techniques and proximity measures. Artif Intell Rev 53:5995–6023. https://doi.org/10.1007/s10462-020-09840-7
Meier EL, Lo M, Kiran S (2016) Understanding semantic and phonological processing deficits in adults with aphasia: effects of category and typicality. Aphasiology 30:719–749. https://doi.org/10.1080/02687038.2015.1081137
Millis SR, Ricker JH (1994) Verbal learning patterns in moderate and severe traumatic brain injury. J Clin Exp Neuropsychol 16:498–507. https://doi.org/10.1080/01688639408402661
Mirzaei G, Adeli H (2022) Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia. Biomed Signal Process Control 72:103293. https://doi.org/10.1016/j.bspc.2021.103293
Mollayeva T, Tran A, Chan V et al (2022) Sex-specific analysis of traumatic brain injury events: applying computational and data visualization techniques to inform prevention and management. BMC Med Res Methodol 22:30. https://doi.org/10.1186/s12874-021-01493-6
Molteni E, Ranzini MBM, Beretta E et al (2021) Individualized prognostic prediction of the long-term functional trajectory in pediatric acquired brain injury. J Pers Med 11:675. https://doi.org/10.3390/jpm11070675
Monsour M, Ebedes D, Borlongan CV (2022) A review of the pathology and treatment of TBI and PTSD. Exp Neurol 351:114009. https://doi.org/10.1016/j.expneurol.2022.114009
Montero F, López-Jaquero V, Navarro E, Sánchez E (2011) Computer-aided relearning activity patterns for people with acquired brain injury. Comput Educ 57:1149–1159. https://doi.org/10.1016/j.compedu.2010.12.008
Moore AD, Stambrook M (1992) Coping strategies and locus of control following traumatic brain injury: Relationship to long-term outcome. Brain Inj 6:89–94. https://doi.org/10.3109/02699059209008129
Moreno JA, McKerral M (2017) Towards a taxonomy of sexuality following traumatic brain injury: a pilot exploratory study using cluster analysis. NeuroRehabilitation 41:281–291. https://doi.org/10.3233/NRE-172201
Moses J (2004) Comprehensive Trail Making Test (CTMT) by Cecil R. Reynolds. Austin, Texas: PRO-ED Inc, 2002. Arch Clin Neuropsychol 19:703–708. https://doi.org/10.1016/j.acn.2004.02.004
Moss-Morris R, Weinman J, Petrie K et al (2002) The Revised Illness Perception Questionnaire (IPQ-R). Psychol Health 17:1–16. https://doi.org/10.1080/08870440290001494
Moya A, Navarro E, Jaén J et al (2022) Exploiting variability in the design of genetic algorithms to generate telerehabilitation activities. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2022.108441
Murtagh F (1983) A survey of recent advances in hierarchical clustering algorithms. Comput J 26:354–359. https://doi.org/10.1093/comjnl/26.4.354
Najafabadi MK, Mahrin MN (2016) A systematic literature review on the state of research and practice of collaborative filtering technique and implicit feedback. Artif Intell Rev 45:167–201. https://doi.org/10.1007/s10462-015-9443-9
Network TA (2019) Definition of ABI. http://www.abinetwork.ca/definition. Accessed 21 Mar 2019
Neubauer TR, Peres SM, Fantinato M et al (2021) Interactive clustering: a scoping review. Artif Intell Rev 54:2765–2826. https://doi.org/10.1007/s10462-020-09913-7
Otten EJ, Dorlac WC (2017) Managing traumatic brain injury: translating military guidelines to the wilderness. Wilderness Environ Med 28:S117–S123. https://doi.org/10.1016/j.wem.2017.02.008
Ownsworth T, Fleming J, Strong J et al (2007) Awareness typologies, long-term emotional adjustment and psychosocial outcomes following acquired brain injury. Neuropsychol Rehabil 17:129–150. https://doi.org/10.1080/09602010600615506
Oyeyemi GM, Bukoye A, Akeyede I (2015) Comparison of outlier detection procedures in multiple linear regressions related papers. Am J Math Stat 5:37–41
Palmer GA, Palmer DG (2021) Subtypes in PTSD for veterans: do similar profiles exist in polytrauma patients? J Loss Trauma 26:409–420. https://doi.org/10.1080/15325024.2020.1833550
Parimbelli E, Marini S, Sacchi L, Bellazzi R (2018) Patient similarity for precision medicine: a systematic review. J Biomed Inform 83:87–96
Park HS, Jun CH (2009) A simple and fast algorithm for K-medoids clustering. Expert Syst Appl 36:3336–3341. https://doi.org/10.1016/j.eswa.2008.01.039
Patterson F, AbuOmar O, Jones M et al (2019) Data mining the effects of testing conditions and specimen properties on brain biomechanics. Int Biomech 6:34–46. https://doi.org/10.1080/23335432.2019.1621206
Perel PA, Olldashi F, Muzha I et al (2008) Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients. BMJ 336:425–429. https://doi.org/10.1136/bmj.39461.643438.25
Podell J, Pergakis M, Yang S et al (2022) Leveraging continuous vital sign measurements for real-time assessment of autonomic nervous system dysfunction after brain injury: a narrative review of current and future applications. Neurocrit Care 37:206–219. https://doi.org/10.1007/s12028-022-01491-6
Podell J, Yang S, Miller S et al (2023) Rapid prediction of secondary neurologic decline after traumatic brain injury: a data analytic approach. Sci Rep 13:403. https://doi.org/10.1038/s41598-022-26318-4
Ponomarev VA, Gurskaya OE, Kropotov YD et al (2010) Comparison of methods for clustering independent EEG components in healthy subjects and patients with postconcussion syndrome after traumatic brain injury. Hum Physiol 36:123–131. https://doi.org/10.1134/S0362119710020015
Prigatano GP, Fordyce DJ (1986) Neuropsychological rehabilitation after brain injury. Johns Hopkins University Press
Proctor CJ, Best LA (2019) Social and psychological influences on satisfaction with life after brain injury. Disabil Health J 12:387–393. https://doi.org/10.1016/j.dhjo.2019.01.001
Pugh MJV, Finley EP, Copeland LA et al (2014) Complex comorbidity clusters in OEF/OIF veterans. Med Care 52:172–181. https://doi.org/10.1097/MLR.0000000000000059
Quintana M, Peña-Casanova J, Sánchez-Benavides G et al (2011) Spanish multicenter normative studies (neuronorma project): norms for the abbreviated Barcelona test. Arch Clin Neuropsychol 26:144–157. https://doi.org/10.1093/arclin/acq098
Rabinowitz AR, Arnett PA (2013) Intraindividual cognitive variability before and after sports-related concussion. Neuropsychology 27:481–490. https://doi.org/10.1037/a0033023
Raghavaiah P, Varadarajan S (2022) A CAD system design for Alzheimer’s disease diagnosis using temporally consistent clustering and hybrid deep learning models. Biomed Signal Process Control 75:103571. https://doi.org/10.1016/j.bspc.2022.103571
Rajagopalan S, Baker W, Mahanna-Gabrielli E et al (2022) Hierarchical cluster analysis identifies distinct physiological states after acute brain injury. Neurocrit Care 36:630–639. https://doi.org/10.1007/s12028-021-01362-6
Rakers SE, Timmerman ME, Scheenen ME et al (2021) Trajectories of fatigue, psychological distress, and coping styles after mild traumatic brain injury: a 6-month prospective cohort study. Arch Phys Med Rehabil 102:1965-1971.e2. https://doi.org/10.1016/j.apmr.2021.06.004
Ramos Emmendorfer L, de Paula Canuto AM (2021) A generalized average linkage criterion for hierarchical agglomerative clustering. Appl Soft Comput 100:106990. https://doi.org/10.1016/j.asoc.2020.106990
Rappaport M, Hall KM, Hopkins K et al (1982) Disability rating scale for severe head trauma: coma to community. Arch Phys Med Rehabil 63:118–123
Rasmussen C (2000) The infinite gaussian mixture model. In: Solla S, Leen T, Müller K (eds) Advances in neural information processing systems. MIT Press
Reitan RM, Wolfson D (1985) The Halstead-Reitan neuropsychological test battery: theory and clinical interpretation. Reitan Neuropsychology
Rey A (1964) L’Examen Clinique en Psychologie [clinical examination in psychology]
Ringdahl EN, Becker ML, Hussey JE et al (2019) Executive function profiles in pediatric traumatic brain injury. Dev Neuropsychol 44:172–188. https://doi.org/10.1080/87565641.2018.1557190
Rosenblatt CK, Harriss A, Babul A-N, Rosenblatt SA (2021) Machine learning for subtyping concussion using a clustering approach. Front Hum Neurosci. https://doi.org/10.3389/fnhum.2021.716643
Ruff RM, Light RH, Parker SB, Levin HS (1996) Benton controlled oral word association test: reliability and updated norms. Arch Clin Neuropsychol 11:329–338
Sander J, Ester M, Kriegel HP, Xu X (1998) Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Min Knowl Discov 2:169–194. https://doi.org/10.1023/A:1009745219419
SAS Institute Inc (2022) SAS—analytics software & solutions. https://www.sas.com/en_gb/home.html
Saxena A, Prasad M, Gupta A et al (2017) A review of clustering techniques and developments. Neurocomputing 267:664–681. https://doi.org/10.1016/j.neucom.2017.06.053
Sherer M, Nick TG, Sander AM et al (2017) Groupings of persons with traumatic brain injury: a new approach to classifying traumatic brain injury in the post-acute period. J Head Trauma Rehabil 32:125–133. https://doi.org/10.1097/HTR.0000000000000207
Sherer M, Sander AM, Ponsford J et al (2020) Patterns of cognitive test scores and symptom complaints in persons with TBI who failed performance validity testing. J Int Neuropsychol Soc 26:932–938. https://doi.org/10.1017/S1355617720000351
Shi Y (2008) Detecting Clusters and Outliers for Multi-dimensional Data. In: 2008 International Conference on Multimedia and Ubiquitous Engineering (MUE 2008). IEEE, pp 429–432
Si B, Dumkrieger G, Wu T et al (2018a) Sub-classifying patients with mild traumatic brain injury: a clustering approach based on baseline clinical characteristics and 90-day and 180-day outcomes. PLoS ONE 13:1–18. https://doi.org/10.1371/journal.pone.0198741
Si B, Dumkrieger G, Wu T et al (2018ab) A cross-study analysis for reproducible sub-classification of traumatic brain injury. Front Neurol. https://doi.org/10.3389/fneur.2018.00606
Snell DL, Surgenor LJ, Hay-Smith EJC et al (2015) The contribution of psychological factors to recovery after mild traumatic brain injury: is cluster analysis a useful approach? Brain Inj 29:291–299. https://doi.org/10.3109/02699052.2014.976594
Solmaz B, Tunç B, Parker D et al (2017) Assessing connectivity related injury burden in diffuse traumatic brain injury. Hum Brain Mapp 38:2913–2922. https://doi.org/10.1002/hbm.23561
Sorani MD, Hemphill JC, Morabito D et al (2007) New approaches to physiological informatics in neurocritical care. Neurocrit Care 7:45–52. https://doi.org/10.1007/s12028-007-0043-7
Spiga O, Cicaloni V, Dimitri GM et al (2021) Machine learning application for patient stratification and phenotype/genotype investigation in a rare disease. Brief Bioinform 22:1–13. https://doi.org/10.1093/bib/bbaa434
Stambrook M (1993) Alternatives to the Glasgow coma scale as a quality of life predictor following traumatic brain injury. Arch Clin Neuropsychol 8:95–103. https://doi.org/10.1016/0887-6177(93)90027-X
Standring OJ, Friedberg J, Tripodis Y et al (2019) Contact sport participation and chronic traumatic encephalopathy are associated with altered severity and distribution of cerebral amyloid angiopathy. Acta Neuropathol 138:401–413. https://doi.org/10.1007/s00401-019-02031-x
Steyerberg EW, Mushkudiani N, Perel P et al (2008) Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. PLoS Med 5:1251–1261. https://doi.org/10.1371/journal.pmed.0050165
Tajunisha N, Saravanan V (2010) An increased performance of clustering high dimensional data using principal component analysis. In: 2010 First International Conference on Integrated Intelligent Computing. IEEE, pp 17–21
Teas H (1957) Physiological genetics. Annu Rev Plant Physiol 8:393–412. https://doi.org/10.1146/annurev.pp.08.060157.002141
Thaler NS, Bello DT, Randall C et al (2010) IQ profiles are associated with differences in behavioral functioning following pediatric traumatic brain injury. Arch Clin Neuropsychol 25:781–790. https://doi.org/10.1093/arclin/acq073
Thaler NS, Linck JF, Heyanka DJ et al (2013) Heterogeneity in trail making test performance in OEF/OIF/OND veterans with mild traumatic brain injury. Arch Clin Neuropsychol 28:798–807. https://doi.org/10.1093/arclin/act080
Thaler NS, Terranova J, Turner A et al (2015) A comparison of IQ and memory cluster solutions in moderate and severe pediatric traumatic brain injury. Appl Neuropsychol Child 4:20–30. https://doi.org/10.1080/21622965.2013.790820
Thomas I, Dickens AM, Posti JP et al (2020) Integrative analysis of circulating metabolite profiles and magnetic resonance imaging metrics in patients with traumatic brain injury. Int J Mol Sci 21:1395. https://doi.org/10.3390/ijms21041395
Tulsky DS, Kisala PA, Lai J-S et al (2015) Developing an item bank to measure economic quality of life for individuals with disabilities. Arch Phys Med Rehabil 96:604–613. https://doi.org/10.1016/j.apmr.2014.02.030
Tulsky DS, Kisala PA, Victorson D et al (2016) TBI-QOL. J Head Trauma Rehabil 31:40–51. https://doi.org/10.1097/HTR.0000000000000131
Turner AP, Bombardier CH, Rimmele CT (2003) A typology of alcohol use patterns among persons with recent traumatic brain injury or spinal cord injury: implications for treatment matching. Arch Phys Med Rehabil 84:358–364. https://doi.org/10.1053/apmr.2003.50107
Ubukata S, Ueda K, Fujimoto G et al (2022) Extracting apathy from depression syndrome in traumatic brain injury by using a clustering method. J Neuropsychiatry Clin Neurosci 34:158–167. https://doi.org/10.1176/appi.neuropsych.21020046
UN (2022) Convention on the rights of persons with disabilities
Van Der Heijden P, Donders J (2003) WAIS-III factor index score patterns after traumatic brain injury. Assessment 10:115–122. https://doi.org/10.1177/1073191103010002001
Van Der Maaten LJP, Postma EO, Van Den Herik HJ (2009) Dimensionality reduction: a comparative review. J Mach Learn Res. https://doi.org/10.1080/13506280444000102
Velikonja D, Warriner E, Brum C (2010) Profiles of emotional and behavioral sequelae following acquired brain injury: cluster analysis of the Personality Assessment Inventory. J Clin Exp Neuropsychol 32:610–621. https://doi.org/10.1080/13803390903401302
Vijapur SM, Vaughan LE, Awan N et al (2021) Treelet transform analysis to identify clusters of systemic inflammatory variance in a population with moderate-to-severe traumatic brain injury. Brain Behav Immun 95:45–60. https://doi.org/10.1016/j.bbi.2021.01.026
Vijaya, Sharma S, Batra N (2019) Comparative study of single linkage, complete linkage, and ward method of agglomerative clustering. In: 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). IEEE, pp 568–573
Vincent J-L, Moreno R, Takala J et al (1996) The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. Intensive Care Med 22:707–710. https://doi.org/10.1007/BF01709751
Walker PB, Norris JN, Tschiffely AE et al (2017) Applications of transductive spectral clustering methods in a military medical concussion database. IEEE/ACM Trans Comput Biol Bioinform 14:534–544. https://doi.org/10.1109/TCBB.2016.2591549
Wang W, Yang J, Muntz R (1997a) STING : a statistical information grid approach to spatial data mining
Warriner EM, Rourke BP, Velikonja D, Metham L (2003) Subtypes of emotional and behavioural sequelae in patients with traumatic brain injury. J Clin Exp Neuropsychol 25:904–917. https://doi.org/10.1076/jcen.25.7.904.16494
Weaver RH, Roberto KA (2015) Home and community-based service use by vulnerable older adults. Gerontologist. https://doi.org/10.1093/geront/gnv149
Wechsler D (1987) Wechsler memory scale-revised. Psychol Corp
Wechsler D (1989) Wechesler preschool and primary scale of intelligence-revised. WPPSI-R. Psychological Corporation, San Antonio
Wechsler D (1997) Wechsler adult intelligence scale—revised UK. New York Psychol Corp, New York
Wechsler D (1999) WAIS-III. Escala de inteligencia de Wechsler para adultos-III
Wechsler D (2008) WAIS-IV technical and interpretive manual, 4th edn. Pearson, San Antonio
Wen J, Li S, Lin Z et al (2012) Systematic literature review of machine learning based software development effort estimation models. Inf Softw Technol 54:41–59. https://doi.org/10.1016/j.infsof.2011.09.002
Whitehouse DP, Monteiro M, Czeiter E et al (2022) Relationship of admission blood proteomic biomarkers levels to lesion type and lesion burden in traumatic brain injury: a CENTER-TBI study. EBioMedicine. https://doi.org/10.1016/j.ebiom.2021.103777
Widerström-Noga E, Govind V, Adcock JP et al (2016) Subacute pain after traumatic brain injury is associated with lower insular n-acetylaspartate concentrations. J Neurotrauma 33:1380–1389. https://doi.org/10.1089/neu.2015.4098
Wiegner S, Donders J (1999a) Performance on the California verbal learning test after traumatic brain injury. J Clin Exp Neuropsychol 21:159–170. https://doi.org/10.1076/jcen.21.2.159.925
Wiegner S, Donders J (1999b) Performance on the Wisconsin card sorting test after traumatic brain injury. Assessment 6:179–187. https://doi.org/10.1177/107319119900600205
Wiles MD, Braganza M, Edwards H et al (2023) Management of traumatic brain injury in the non-neurosurgical intensive care unit: a narrative review of current evidence. Anaesthesia. https://doi.org/10.1111/anae.15898
Wilier B, Ottenbacher KJ, Lou CM (1994) The community integration questionnaire a comparative examination. Am J Phys Med Rehabil 73:103–111. https://doi.org/10.1097/00002060-199404000-00006
Wilson JT, Pettigrew LE, Teasdale GM (1998) Structured interviews for the Glasgow Outcome Scale and the Extended Glasgow Outcome Scale: guidelines for their use. J Neurotrauma 15:573–585. https://doi.org/10.1089/neu.1998.15.573
Wojtusiak J, Bagais W, Vang J et al (2023) The role of symptom clusters in triage of COVID-19 patients. Qual Manag Health Care 32:S21–S28. https://doi.org/10.1097/QMH.0000000000000399
Woolger C (2001) Wechsler intelligence scale for children-third edition (Wisc-III). Understanding psychological assessment. Springer US, Boston, pp 219–233
Wu J, Song C-H, Kong JM, Lee WD (2007) Extended mean field annealing for clustering incomplete data. In: 2007 International Symposium on Information Technology Convergence (ISITC 2007). IEEE, pp 8–12
Xu D, Tian Y (2015) A comprehensive survey of clustering algorithms. Ann Data Sci 2:165–193. https://doi.org/10.1007/s40745-015-0040-1
Yang L, Zhang H, Shen H et al (2021) Quality assessment in systematic literature reviews: a software engineering perspective. Inf Softw Technol 130:106397. https://doi.org/10.1016/j.infsof.2020.106397
Yeboah D, Steinmeister L, Hier DB et al (2020) An explainable and statistically validated ensemble clustering model applied to the identification of traumatic brain injury subgroups. IEEE Access 8:180690–180705. https://doi.org/10.1109/ACCESS.2020.3027453
Yoder PJ, Lloyd BP, Symons FJ (2018) Observational measurement of behavior, 2nd edn. Brookes Publishing
Yuh EL, Jain S, Sun X et al (2021) Pathological computed tomography features associated with adverse outcomes after mild traumatic brain injury. JAMA Neurol 78:1137. https://doi.org/10.1001/jamaneurol.2021.2120
Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: an efficient data clustering method for very large databases. SIGMOD Rec (ACM Spec Interes Gr Manag Data) 25:103–114. https://doi.org/10.1145/235968.233324
Zhang Y, Zhang C, Wang Y (2022) CT image under improved fuzzy C-means clustering algorithm for evaluation of the relationship between cerebrospinal fluid change and communicating hydrocephalus after decompressive craniectomy in patients with traumatic brain injury. Sci Program 2022:1–10. https://doi.org/10.1155/2022/9466706
Zigmond AS, Snaith RP (1983) The Hospital Anxiety and Depression Scale. Acta Psychiatr Scand 67:361–370. https://doi.org/10.1111/j.1600-0447.1983.tb09716.x
Zimmermann N, Pereira N, Hermes-Pereira A et al (2015) Executive functions profiles in traumatic brain injury adults: implications for rehabilitation studies. Brain Inj 29:1071–1081. https://doi.org/10.3109/02699052.2015.1015613
Acknowledgements
This paper is part of the R+D+i project PID2019-108915RB-I00 funded by MCIN/AEI/10.130.39/501100011033. It has also been funded by the University of Castilla-La Mancha thanks to the PhD scholarship 2019-PREDUCLM-10772.
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Alejandro Moya: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Elena Pretel: Conceptualization, Investigation, Writing – review & editing. Elena Navarro: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing – review & editing. Javier Jaén: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing – review & editing.
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Appendices
Appendix
Appendix A: Deficits in TBI
According to specialists in the area, such as the Association of Acquired Brain Injury of Castilla-La Mancha (ADACE) (ADACE CLM), people with TBI may suffer multiple deficits depending on the area of the brain that has been damaged. The deficits that a person with TBI may suffer can be divided into three large groups (Montero et al. 2011): Physical/Motor deficits (Table 21), Cognitive/Intellectual deficits (Table 22) and Behavioural/Emotional deficits (Table 23).
Appendix B: Articles included
Following the methodology described in Sect. 4, a total of 105 articles were obtained to perform the analysis. The main information for each included article can be seen in Table 24.
Appendix C: Quality evaluation of the articles included
In the following table (Table 25), the quality score computed for each article included in the review is presented.
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Moya, A., Pretel, E., Navarro, E. et al. A systematic literature review of clustering techniques for patients with traumatic brain injury. Artif Intell Rev 56 (Suppl 1), 351–419 (2023). https://doi.org/10.1007/s10462-023-10531-2
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DOI: https://doi.org/10.1007/s10462-023-10531-2