Department of Psychology, Center for Complex Systems and Brain Sciences, Florida Atlantic University.
Department of Epidemiology, University of Florida.
Curr Opin Psychiatry. 2020 Jul;33(4):334-342. doi: 10.1097/YCO.0000000000000611.
To provide an accessible overview of some of the most recent trends in the application of machine learning to the field of substance use disorders and their implications for future research and practice.
Machine-learning (ML) techniques have recently been applied to substance use disorder (SUD) data for multiple predictive applications including detecting current abuse, assessing future risk and predicting treatment success. These models cover a wide range of machine-learning techniques and data types including physiological measures, longitudinal surveys, treatment outcomes, national surveys, medical records and social media.
The application of machine-learning models to substance use disorder data shows significant promise, with some use cases and data types showing high predictive accuracy, particularly for models of physiological and behavioral measures for predicting current substance use, portending potential clinical diagnostic applications; however, these results are uneven, with some models performing poorly or at chance, a limitation likely reflecting insufficient data and/or weak validation methods. The field will likely benefit from larger and more multimodal datasets, greater standardization of data recording and rigorous testing protocols as well as greater use of modern deep neural network models applied to multimodal unstructured datasets.
提供对机器学习在物质使用障碍领域应用的最新趋势的概述，及其对未来研究和实践的意义。
机器学习 (ML) 技术最近已被应用于物质使用障碍 (SUD) 数据，以进行多种预测性应用，包括检测当前滥用、评估未来风险和预测治疗效果。这些模型涵盖了广泛的机器学习技术和数据类型，包括生理测量、纵向调查、治疗结果、全国性调查、医疗记录和社交媒体。
机器学习模型在物质使用障碍数据中的应用显示出巨大的潜力，一些用例和数据类型显示出较高的预测准确性，特别是对于预测当前物质使用的生理和行为测量模型，预示着潜在的临床诊断应用；然而，这些结果参差不齐，有些模型表现不佳或处于随机状态，这一限制可能反映了数据不足和/或验证方法较弱。该领域可能受益于更大、更多的多模态数据集，更标准化的数据记录和严格的测试协议，以及更多地将现代深度神经网络模型应用于多模态非结构化数据集。
