Characterizing neuroimaging 'brain-behavior' model performance bias in rural populations

项目总结 近五分之一的美国人口居住在农村地区，其中约五分之一的人居住在农村地区 居民们患有精神疾病。虽然这些精神疾病的发病率与城市地区相似，但个人 生活在农村地区的人面临着不成比例的负面精神后果负担。现代科技的进展 精神病学研究的重点是使用机器学习和人类神经成像来预测诊断 和治疗结果。然而，最近的证据表明，机器学习模型本身可能 通过绩效偏见来推动健康差距。具体地说，在 多数群体在创建期间代表性不足的群体中可能表现不佳 这种模式(例如，如果患者是农村人，选择正确治疗的可能性就更低)。鉴于几乎所有的 精神病学研究中的神经成像“大脑行为”预测模型是从 在人口稠密的大都市地区，这项研究将评估农村地区表现偏差的“大脑行为”模型 人口。它还将调查消除这种偏见的方法，这种偏见在农村地区造成了进一步的健康差距 人口。在目标1中，我将使用9811名青少年大脑和认知研究人员的神经成像数据 开发研究，以创建认知的“大脑行为”预测模型。在目标2中，我将对该模型进行评估 针对城乡绩效偏差，寻求降低模式偏差的策略。这项研究将具有重要的意义 对于理解医疗保健中的算法如何推动健康差距以及我们如何减少 通过设计在代表性不足的人群中公平地发挥作用的模型，消除差距。

PROJECT SUMMARY Nearly one-fifth of the Unites States population resides in a rural region, and approximately one-fifth of those residents suffers from a mental illness. While these rates of mental illness are similar to urban areas, individuals living in rural regions face a disproportionate burden of negative psychiatric outcomes. Modern advances in psychiatric research have focused on using machine learning and human neuroimaging to predict diagnoses and treatment outcomes. However, recent evidence suggests that machine learning models themselves may drive health disparities through performance bias. Specifically, clinical decision-making models created in majority populations may not perform as well in populations that were underrepresented during the creation of the model (e.g., poorer likelihood of choosing the correct treatment if patients are rural). Given that virtually all neuroimaging ‘brain-behavior’ predictive models in psychiatry research are generated from data collected in highly populated metropolitan areas, this study will evaluate ‘brain-behavior’ models for performance bias in rural populations. It will also investigate means of eliminating this bias that creates further health disparities in rural populations. In Aim 1, I will use neuroimaging data from 9,811 individuals in the Adolescent Brain and Cognitive Development Study to create a ‘brain-behavior’ predictive model of cognition. In Aim 2, I will evaluate this model for urban-rural performance bias and pursue strategies to reduce model bias. This study will have important implications for understanding how algorithms in healthcare drive health disparities and how we can reduce these disparities by designing models that perform equitably within underrepresented populations.