Clinicians working in the field of acquired brain injury (ABI, an injury to the brain sustained after birth) are challenged to develop suitable care pathways for an individual client’s needs. Being able to predict psychosocial outcomes after ABI would enable clinicians and service providers to make advance decisions and better tailor care plans. Machine learning (ML, a predictive method from the field of artificial intelligence) is increasingly used for predicting ABI outcomes. This review aimed to examine the efficacy of using ML to make psychosocial predictions in ABI, evaluate the methodological quality of studies, and understand researchers’ rationale for their choice of ML algorithms. Nine studies were reviewed from five databases, predicting a range of psychosocial outcomes from stroke, traumatic brain injury, and concussion. Eleven types of ML were employed with a total of 75 ML models. Every model was evaluated as having high risk of bias, unable to provide adequate evidence for predictive performance due to poor methodological quality. Overall, there was limited rationale for the choice of ML algorithms and poor evaluation of the methodological limitations by study authors. Considerations for overcoming methodological shortcomings are discussed, along with suggestions for assessing the suitability of data and suitability of ML algorithms for different ABI research questions.

中文翻译：

---

对机器学习在预测获得性脑损伤中的社会心理结果方面的有效性进行系统评价：使用了哪些算法，为什么？

在获得性脑损伤（ABI，出生后持续的大脑损伤）领域工作的临床医生面临着为个体客户的需求开发合适的护理途径的挑战。能够预测 ABI 后的社会心理结果将使临床医生和服务提供者能够提前做出决定并更好地定制护理计划。机器学习（ML，人工智能领域的一种预测方法）越来越多地用于预测 ABI 结果。本综述旨在检验使用 ML 在 ABI 中进行社会心理预测的有效性，评估研究的方法学质量，并了解研究人员选择 ML 算法的理由。从五个数据库中回顾了九项研究，预测了中风、创伤性脑损伤和脑震荡的一系列社会心理结果。使用了 11 种类型的 ML，总共 75 个 ML 模型。每个模型都被评估为具有高偏倚风险，由于方法学质量差，无法为预测性能提供足够的证据。总体而言，选择 ML 算法的理由有限，研究作者对方法学局限性的评估不佳。讨论了克服方法学缺陷的考虑因素，以及评估数据适用性和 ML 算法对不同 ABI 研究问题的适用性的建议。选择 ML 算法的理由有限，研究作者对方法学局限性的评估不佳。讨论了克服方法学缺陷的考虑因素，以及评估数据适用性和 ML 算法对不同 ABI 研究问题的适用性的建议。选择 ML 算法的理由有限，研究作者对方法学局限性的评估不佳。讨论了克服方法学缺陷的考虑因素，以及评估数据适用性和 ML 算法对不同 ABI 研究问题的适用性的建议。