Radabaugh Hannah L, Bonnell Jerry, Dietrich W Dalton, Bramlett Helen M, Schwartz Odelia, Sarkar Dilip
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2416-2420. doi: 10.1109/EMBC44109.2020.9175658.
Traumatic brain injury (TBI) is a leading cause of death and disability yet treatment strategies remain elusive. Advances in machine learning present exciting opportunities for developing personalized medicine and informing laboratory research. However, their feasibility has yet to be widely assessed in animal research where data are typically limited or in the TBI field where each patient presents with a unique injury. The Operation Brain Trauma Therapy (OBTT) has amassed an animal dataset that spans multiple types of injury, treatment strategies, behavioral assessments, histological measures, and biomarker screenings. This paper aims to analyze these data using supervised learning techniques for the first time by partitioning the dataset into acute input metrics (i.e. 7 days post-injury) and a defined recovery outcome (i.e. memory retention). Preprocessing is then applied to transform the raw OBTT dataset, e.g. developing a class attribute by histogram binning, eliminating borderline cases, and applying principal component analysis (PCA). We find that these steps are also useful in establishing a treatment ranking; Minocycline, a therapy with no significant findings in the OBTT analyses, yields the highest percentage recovery in our ranking. Furthermore, of the seven classifiers we have evaluated, Naïve Bayes achieves the best performance (67%) and yields significant improvement over our baseline model on the preprocessed dataset with borderline elimination. We also investigate the effect of testing on individual treatment groups to evaluate which groups are difficult to classify, and note the interpretive qualities of our model that can be clinically relevant.Clinical Relevance- These studies establish methods for better analyzing multivariate functional recovery and understanding which measures affect prognosis following traumatic brain injury.
创伤性脑损伤（TBI）是死亡和致残的主要原因，但治疗策略仍然难以捉摸。机器学习的进展为开发个性化医疗和指导实验室研究带来了令人兴奋的机遇。然而，它们在动物研究（数据通常有限）或TBI领域（每个患者的损伤都独一无二）中的可行性尚未得到广泛评估。脑创伤治疗行动（OBTT）已经积累了一个动物数据集，涵盖多种损伤类型、治疗策略、行为评估、组织学测量和生物标志物筛查。本文旨在首次使用监督学习技术分析这些数据，将数据集划分为急性输入指标（即受伤后7天）和确定的恢复结果（即记忆保持）。然后进行预处理以转换原始的OBTT数据集，例如通过直方图装箱开发类属性、消除临界情况并应用主成分分析（PCA）。我们发现这些步骤在建立治疗排名方面也很有用；米诺环素在OBTT分析中没有显著结果，但在我们的排名中恢复百分比最高。此外，在我们评估的七个分类器中，朴素贝叶斯表现最佳（67%），并且在经过临界值消除的预处理数据集上比我们的基线模型有显著改进。我们还研究了对各个治疗组进行测试的效果，以评估哪些组难以分类，并注意到我们模型的解释性特征可能具有临床相关性。临床相关性——这些研究建立了更好地分析多变量功能恢复以及理解哪些措施影响创伤性脑损伤后预后的方法。
