Machine Learning e Processos Diagnósticos com Avaliação Neuropsicológica: Uma Revisão Integrativa
Resumo
Palavras-chave
Texto completo:
PDFReferências
Ang, T. F., An, N., Ding, H., Devine, S., Auerbach, S. H., Massaro, J., Joshi, P., … Lin, H. (2019). Using Data Science to Diagnose and Characterize Heterogeneity of Alzheimer’s Disease. Alzheimer’s & Dementia: Translational Research & Clinical Interventions, 5, 264-271. doi: https://doi.org/10.1016/j.trci.2019.05.002
Armañanzas, R., Alonso-Nanclares, L., Kastanaskaite, A., Sola, R. G., Bielza, C., Larrañaga, P., … DeFelipe, J. (2013). Machine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy Surgery. PLoS One, 8(4), 1-9. doi: https://doi.org/10.1371/journal.pone.0062819
Ashendorf, L., Alosco, M. L., Bing-Canar, H., Chapman, K. R., Martin, B., Chaisson, C. E., ... Stern, R. A. (2018). Clinical utility of select neuropsychological assessment battery tests in predicting functional abilities in dementia. Archives of Clinical Neuropsychology, 33(5), 530-540. doi: https://doi.org/10.1093/arclin/acx100
Bak, N., Ebdrup, B., Oranje, B., Fagerlund, B., Jensen, M., Hansen, L., … Nielson, M. (2017). Two Subgroups of Antipsychotic-Naive, First-Episode Schizophrenia Patients Identified With a Gaussian Mixture Model on Cognition and Electrophysiology. Translational Psychiatry, 7(4), 1-8. doi: https://doi.org/10.1038/tp.2017.59
Battista, P., Salvatore, C., & Castiglioni, I. (2017). Optimizing Neuropsychological Assessments for Cognitive, Behavioral and Functional Impairment Classification: A Machine Learning Study. Behavioural Neurology, 2017, article 185090, 1-19. doi: https://doi.org/10.1155/2017/1850909
Besga, A., Gonzaeles, I., Echeburua, E., Savio, A., Ayerd, B., Madrigal, J, L., … Leza, J. C. (2015). Discrimination Between Alzheimer’s Disease and Late Onset Bipolar Disorder Using Multivariate Analysis. Frontiers in Aging Neuroscience, 7, article 231, 231-240. doi: 10.3389/fnagi.2015.00231/full
Bhagyashree, S. I. R., Nagaraj, K., Prince, M., Fall, C, H., & Krishna, M. (2017). Diagnosis of Dementia by Machine Learning Methods in Epidemiological Studies: A Pilot Exploratory Study from South India. Social Psychiatry and Psychiatric Epidemiology, 53(1), 77-86. doi: https://doi.org/10.1007/s00127-017-1410-0
Bone, D., Bishop, S. L., Black, M. P., Goodwin, M. S., Lord, C., & Narayanan, S. S. (2016). Use of Machine Learning to Improve Autism Screening and Diagnostic Instruments: Effectiveness, Efficiency, and Multi‐Instrument Fusion. Journal of Child Psychology and Psychiatry, 57(8), 927-937. doi: https://doi.org/10.1111/jcpp.12559
Bruun, M., Koikkalainen, J., Baroni, M., Gjerum, L., Lemstra, A. W., Remes, A. M., … Mecocci, P. (2018). Evaluating Combinations of Diagnostic Tests to Discriminate Different Dementia Types. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 10, 509-518. doi: https://doi.org/10.1016/j.dadm.2018.07.003
Chandler, C., Foltz, P. W., Cohen, A. S., Holmlund, T. B., Cheng, J., Bernstein, J. C., ... Elvevåg, B. (2020). Machine learning for ambulatory applications of neuropsychological testing. Intelligence-Based Medicine, 1, 100006. doi: https://doi.org/10.1016/j.ibmed.2020.100006
Chang, T. S., Coen, M. H., Rue, A. L., Jonaitis, E., Koscik, R. L., Hermann, B., & Sager, M. A. (2012). Machine Learning Amplifies the Effect of Parental Family History of Alzheimer’s Disease on List Learning Strategy. Journal of the International Neuropsychological Society, 18(3), 428-439. doi: https://doi.org/10.1017/S1355617711001834
Chu, W., Huang, M., Jian, B., Hsu, C., & Cheng, K. (2016). A Correlative Classification Study of Schizophrenic Patients with Results of Clinical Evaluation and Structural Magnetic Resonance Images. Behavioural Neurology, 2016, article 7849526, 1-11. doi: https://doi.org/10.1155/2016/7849526
Costa, A. B., & Zoltowski, A. P. C. (2014). Como escrever um artigo de revisão sistemática. In Koller, S. H., de Paula Couto, M. C. P., & Von Hohendorff, J. (Orgs.). Manual de produção científica (pp. 55-70). Porto Alegre, RS: Penso Editora.
Crippa, A., Salvatore, C., Molteni, E., Mauri, M., Salandi, A., Trabattoni, S., ... Agostoni, C. (2017). The Utility of a Computerized Algorithm Based on A Multi-Domain Profile of Measures for the Diagnosis of Attention Deficit/Hyperactivity Disorder. Frontiers in psychiatry, 8, article 189, 1-10. doi: 10.3389/fpsyt.2017.00189
Cui, Y., Liu, B., Luo, S., Zhen, X. Fan, M., Liu, T., … Jiang, T. (2011). Identification of Conversion from Mild Cognitive Impairment to Alzheimer’s Disease Using Multivariate Predictors. PloS one, 6(7), 1-10. doi: https://doi.org/10.1371/journal.pone.0021896
Dauwan, M., Zande, J. J. V., Dellen, E. V., Sommer, I. E., Scheltens, P., Lemstra, A. W., & Stam, C. J. (2016). Random Forest to Differentiate Dementia with Lewy Bodies from Alzheimer’s Disease. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 4, 99-106. doi: https://doi.org/10.1016/j.dadm.2016.07.003
De Marco, M., Beltrachini, L. Biancardi, A., Frangi, A. F., & Venneri, A. (2017). Machine-learning Support to Individual Diagnosis of Mild Cognitive Impairment Using Multimodal MRI and Cognitive Assessments. Alzheimer Disease & Associated Disorders, 31(4), 278-286. doi: https://doi.org/10.1097/WAD.0000000000000208
Fuermaier, A. B., Fricke, J. A., Vries, S. M., Tucha, L., & Tucha, O. (2018). Neuropsychological assessment of adults with ADHD: A Delphi consensus study. Applied Neuropsychology: Adult, 26(4), 340-354. doi: https://doi.org/10.1080/23279095.2018.1429441
Galvão, T. F., Pansani, T. S. A., & Harrad, D. (2015). Principais Itens para Relatar Revisões Sistemáticas e Meta-Análises: a Recomendação PRISMA. Epidemiologia e Serviços de Saúde, Brasília, 24(2), 335-342. doi: 10.5123/S1679-49742015000200017
Gardner, J. (2019). Artificial Intelligence and Machine Learning Algorithms for Informing the Diagnostic Process of Mild Cognitive Impairment and Dementia. Archives of Clinical Neuropsychology, 34(6), 838-838. doi: https://doi.org/10.1093/arclin/acz035.06
Haller, S., Missonier, H. F., Rodriguez, C., Deiber, M. P., Nguyen, D., Gold, G., … Giannakopoulos, P. (2013). Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI. American Journal of Neuroradiology, 34(2), 283-291. doi: https://doi.org/10.3174/ajnr.A3223
Hazin, I., Fernandes, I., Gomes, E., & Garcia, D. (2018). Neuropsicologia no Brasil: Passado, Presente e Futuro. Estudos e Pesquisas em Psicologia, 18(4), 1137-1154. Retrieved from https://www.e-publicacoes.uerj.br/index.php/revispsi/article/view/42228/29298
Hua, X., Ching, C. R., Mezher, A., Gutman, B. A., Hibar, D. P., Bhatt, P., ... Leow, A. D. (2016). MRI-Based Brain Atrophy Rates in ADNI Phase 2: Acceleration and Enrichment Considerations for Clinical Trials. Neurobiology of aging, 37, 26-37. doi: https://doi.org/10.1016/j.neurobiolaging.2015.09.018
Khanna, S., Fernandéz, D. D., Iyappan, A., Emon, M. A., Apitius, M. H., & Fröhlich, H. (2018). Using Multi-Scale Genetic, Neuroimaging and Clinical Data for Predicting Alzheimer’s Disease and Reconstruction of Relevant Biological Mechanisms. Scientific reports, 8(1), article 11173, 1-13. doi: https://doi.org/10.1038/s41598-018-29433-3
König, A., Satt, A., Sorin, A., Hoory, R., Toledo, R. H., Derreumaux, A., … Verhey, F. (2015). Automatic Speech Analysis for The Assessment of Patients with Predementia and Alzheimer’s Disease. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 1(1), 112-124. doi: https://doi.org/10.1016/j.dadm.2014.11.012
Legnani, L. K. B., & de Souza, T. P. (2021). O perfil da produção científica neuropsicológica no Brasil: uma revisão integrativa. Espaço para Saúde, 22. doi: https://doi.org/10.22421/1517-7130/es.2021v22.e739
Liang, S., Vega, R., Kong, X., Deng, W., Wang, Q., Ma, X., … Li, M. (2017). Neurocognitive Graphs of First-Episode Schizophrenia and Major Depression Based on Cognitive Features. Neuroscience Bulletin, 34(2), 312-320. doi: https://doi.org/10.1007/s12264-017-0190-6
Mamédio, C., Santos, D. C., Andrucioli, C., Pimenta, M., Roberto, M., & Nobre, C. (2007). The PICO strategy for the research question construction and evidence search. Rev. Latino-Am Enfermagem., 15(3), 508-511. doi: https://doi.org/10.1590/S0104-11692007000300023.
Moradi, E., Hallikainen, I., Hanninen, T., & Tohka, J. (2017). Rey’s Auditory Verbal Learning Test Scores Can Be Predicted from Whole Brain MRI in Alzheimer’s Disease. NeuroImage: Clinical, 13, 415-427. doi: https://doi.org/10.1016/j.nicl.2016.12.011
Ogawa, M., Sone, D., Beheshti, I., Maikusa, N., Okita, K., Takano, H., & Matsuda, H. (2019). Association Between Subfield Volumes of The Medial Temporal Lobe and Cognitive Assessments. Heliyon, 5(6), 1-7. doi: https://doi.org/10.1016/j.heliyon.2019.e01828
Patel, M. J., Andreescu, C., Price, J. C., Eldeman, K. L., Reynolds III, C. F., & Aizenstein, H. J. (2015). Machine Learning Approaches for Integrating Clinical and Imaging Features in Late‐Life Depression Classification and Response Prediction. International Journal of Geriatric Psychiatry, 30(10), 1056-1067. doi: https://doi.org/10.1002/gps.4262
Pereira, T., Lemos, L., Cardoso, S., Silva, D., Rorigues, A., Santana, I., … Guerreiro, M. (2017). Predicting Progression of Mild Cognitive Impairment to Dementia Using Neuropsychological Data: A Supervised Learning Approach Using Time Windows. BMC medical informatics and decision making, 17(1), 110-125. doi: https://doi.org/10.1186/s12911-017-0497-2
Pettersson-Yeo, W., Benetti, S., Marquand, A., Dell’acqua, F., Williams, S., Allen, P., … Prata, D. (2013). Using Genetic, Cognitive and Multi-Modal Neuroimaging Data to Identify Ultra-High-Risk and First-Episode Psychosis at The Individual Level. Psychological medicine, 43(12), 2547-2562. doi: https://doi.org/10.1017/S003329171300024X
Primi, R. (2018). Avaliação Psicológica no Século XXI: de Onde Viemos e para Onde Vamos. Psicologia: Ciência e Profissão, 38(3), 87-97. doi: https://doi.org/10.1590/1982-3703000209814
Ramos, A. A., & Hamdan, A. C. (2016). O Crescimento da Avaliação Neuropsicológica no Brasil: Uma Revisão Sistemática. Psicologia: Ciência e Profissão, 36(2), 471-485. doi: https://doi.org/10.1590/1982-3703001792013
Rhodius-Meester, H. F., Liedes, H., Koikkalainen, J., Wolfsgruber, N. C. P., Peters, O., Jessen, F., & Rami, L. (2018). Computer-Assisted Prediction of Clinical Progression in the Earliest Stages of AD. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 10, 726-736. doi: https://doi.org/10.1016/j.dadm.2018.09.001
Ritchie, L. J., & Tuokko, Holly. (2011). Clinical Decision Trees for Predicting Conversion from Cognitive Impairment No Dementia (CIND) to Dementia in a Longitudinal Population-Based Study. Archives of clinical neuropsychology, 26(1), 16-25. doi: https://doi.org/10.1093/arclin/acq089
Sampaio, R. F., & Mancini, M. C. (2007). Estudos de Revisão Sistemática: Um Guia para Síntese Criteriosa da Evidência Científica. Revista Brasileiro Fisioterapia, 11(1), 83-89. doi: https://doi.org/10.1590/S1413-35552007000100013
Santos, H. G. D., Nascimento, C. F. D., Izbicki, R., Duarte, Y. A. D. O., & Chiavegatto Filho, P. D. A. (2019). Machine Learning para Análises Preditivas em Saúde: Exemplo de Aplicação para Predizer Óbito em Idosos de São Paulo, Brasil. Cadernos de Saúde Pública, 35(7), 1-16. doi: https://doi.org/10.1590/0102-311X00050818
Segovia, F., Bastin, C., Salmon, E., Górriz, J. M., Ramírez, J., & Phillips, C. (2014). Combining PET Images and Neuropsychological Test Data for Automatic Diagnosis of Alzheimer’s Disease. PLoS One, 9(2), 1-8. doi: https://doi.org/10.1371/journal.pone.0088687
Seixas, F. L., Zadrozny, B., Laks, J., Conci, A., & Saade, D. C. M. (2014). A Bayesian Network Decision Model for Supporting the Diagnosis of Dementia, Alzheimer S Disease and Mild Cognitive Impairment. Computers in biology and medicine, 51,140-158. doi: https://doi.org/10.1016/j.compbiomed.2014.04.010
Souza, M. T., Silva, M. D., & Carvalho, R. (2010). Revisão integrativa: o que é e como fazer. Einstein (São Paulo), 8(1), 102–106. doi: https://doi.org/10.1590/S1679-45082010RW1134
Squeglia, L. M., Ball, T. M., Jacobus, J., Brumback, T., McKenna, B. S., Sorg, S. F., … Paulus, M. P. (2016). Neural Predictors of Initiating Alcohol Use During Adolescence. American journal of psychiatry, 174(2), 172-185. doi: https://doi.org/10.1176/appi.ajp.2016.15121587
Van Calster, B., Wynants, L., Timmerman, D., Steyerberg, E. W., & Collins, G. S. (2019). Predictive analytics in health care: how can we know it works?. Journal of the American Medical Informatics Association, 26(12), 1651-1654. doi: https://doi.org/10.1093/jamia/ocz130
Wallert, J., Westman, E., Ulinder, J., Annerstedt, M., Terzis, B., & Ekman, U. (2018). Differentiating Patients at the Memory Clinic with Simple Reaction Time Variables: A Predictive Modeling Approach Using Support Vector Machines and Bayesian Optimization. Frontiers in aging neuroscience, 10, 144. doi: https://doi.org/10.3389/fnagi.2018.00144
Weakley, A., Williams, J. A., Schmitter-Edgeccombe, M., & Cook, D. J. (2015). Neuropsychological Test Selection for Cognitive Impairment Classification: A Machine Learning Approach. Journal of Clinical and Experimental Neuropsychology, 37(9), 899-916. doi: https://doi.org/10.1080/13803395.2015.1067290
Wu, M., Passos, I. C., Bauer, I. E., Lavagnino, L., Cao, B., Soares, J. C., & Mwang, B. (2016). Individualized Identification of Euthymic Bipolar Disorder Using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and Machine Learning. Journal of affective disorders, 192, 219-225. doi: https://doi.org/10.1016/j.jad.2015.12.053
DOI: https://doi.org/10.18256/2175-5027.2022.v14i1.4568
Apontamentos
- Não há apontamentos.
Direitos autorais 2022 Mariana Costa Biermann, Clara Monte Arruda, Leonardo Carneiro Holanda
ISSN 2175-5027
Este obra está licenciada com uma Licença Creative Commons Atribuição 4.0 Internacional.
BASES DE DADOS E INDEXADORES