Traumatic brain injury (TBI) is a leading cause of death and disability. Yet, despite immense research efforts, treatment options remain elusive. Translational failures in TBI are often attributed to the heterogeneity of the TBI population and limited methods to capture these individual variabilities. Advances in machine learning (ML) have the potential to further personalized treatment strategies and better inform translational research. However, the use of ML has yet to be widely assessed in pre-clinical neurotrauma research, where data are strictly limited in subject number. To better establish ML's feasibility, we utilized the fluid percussion injury (FPI) portion of the rich, rat data set collected by Operation Brain Trauma Therapy (OBTT), which tested multiple pharmacological treatments. Previous work has provided confidence that both unsupervised and supervised ML techniques can uncover useful insights from this OBTT pre-clinical research data set.

中文翻译：

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使用机器学习重新评估流体撞击伤后多变量功能恢复的模式：脑外伤手术治疗

创伤性脑损伤 (TBI) 是导致死亡和残疾的主要原因。然而，尽管进行了巨大的研究努力，治疗方案仍然难以捉摸。TBI 的转化失败通常归因于 TBI 人群的异质性和捕捉这些个体差异的方法有限。机器学习 (ML) 的进步有可能进一步推动个性化治疗策略并更好地为转化研究提供信息。然而，ML 的使用尚未在临床前神经创伤研究中得到广泛评估，其中数据在受试者数量方面受到严格限制。为了更好地确定 ML 的可行性，我们利用了脑外伤手术治疗 (OBTT) 收集的丰富大鼠数据集中的流体冲击损伤 (FPI) 部分，该数据集测试了多种药物治疗。