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Machine Learning and Diagnostic Processes with Neuropsychological Assessment: an Integrative Review
Abstract
Machine learning tools have the potential to assist in diagnostic processes and in empirical research, and have become popular in the international literature, but their development is still embryonic in Brazilian context. This study aimed to analyze the use of machine learning as an auxiliary mechanism in cases with neuropsychological assessments. Therefore, an integrative review was carried out, searching articles published and indexed in the SciELO, PePsic, LILACS, BVS, PubMed, MedLine, APA PsycNET and Science Direct databases, using the terms “machine learning” AND “avaliação neuropsicológica” AND “diagnóstico” in Portuguese and the terms “machine learning” AND “neuropsychological assessment” AND “diagnosis” in English. The final sample consisted in 31 articles published in English only. The analyzed studies demonstrated the adequate identification of different diagnoses even based on subtle differentiations. The algorithms used considered information resulting from psychometric tests, neuroimaging, clinical and family history, as well as tests that included physiological and, in some cases, genetic biomarkers. It is noteworthy that the synthesis in this study demonstrates the potential to minimize scientific gap on the development of neuropsychology and diagnostic processes in Brazilian context in order to assist in planning and conducting future studies.
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DOI: https://doi.org/10.18256/2175-5027.2022.v14i1.4568
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ISSN 2175-5027
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