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Machine Learning on Early Diagnosis of Depression

함병주|이광식
대한신경정신의학회 (Korean Neuropsychiatric Association)|2022.08|PSYCHIATRY INVESTIGATION|제19권 제8호|pp. 597-605|DOI https://doi.org/10.30773/pi.2022.0075

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참고문헌

키워드

DepressionEarly diagnosisMachine learning.
주제분류정신과학

초록

To review the recent progress of machine learning for the early diagnosis of depression (major depressive disorder). The source of data was 32 original studies in the Web of Science. The search terms were “depression” (title) and “random forest” (abstract). The eligibility criteria were the dependent variable of depression, the interventions of machine learning (the decision tree, the naïve Bayesian, the random forest, the support vector machine and/or the artificial neural network), the outcomes of accuracy and/or the area under the receiver operating characteristic curve (AUC) for the early diagnosis of depression, the publication year of 2000 or later, the publication language of English and the publication journal of SCIE/SSCI. Different machine learning methods would be appropriate for different types of data for the early diagnosis of depression, e.g., logistic regression, the random forest, the support vector machine and/or the artificial neural network in the case of numeric data, the random forest in the case of genomic data. Their performance measures reported varied within 60.1–100.0 for accuracy and 64.0–96.0 for the AUC. Machine learning provides an effective, non-invasive decision support system for early diagnosis of depression

Abstract

To review the recent progress of machine learning for the early diagnosis of depression (major depressive disorder). The source of data was 32 original studies in the Web of Science. The search terms were “depression” (title) and “random forest” (abstract). The eligibility criteria were the dependent variable of depression, the interventions of machine learning (the decision tree, the naïve Bayesian, the random forest, the support vector machine and/or the artificial neural network), the outcomes of accuracy and/or the area under the receiver operating characteristic curve (AUC) for the early diagnosis of depression, the publication year of 2000 or later, the publication language of English and the publication journal of SCIE/SSCI. Different machine learning methods would be appropriate for different types of data for the early diagnosis of depression, e.g., logistic regression, the random forest, the support vector machine and/or the artificial neural network in the case of numeric data, the random forest in the case of genomic data. Their performance measures reported varied within 60.1–100.0 for accuracy and 64.0–96.0 for the AUC. Machine learning provides an effective, non-invasive decision support system for early diagnosis of depression

참고문헌 (57)

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