Concussions, also known as mild traumatic brain injury (mTBI), are a growing health challenge. Approximately four million concussions are diagnosed annually in the United States. Concussion is a heterogeneous disorder in causation, symptoms, and outcome making precision medicine approaches to this disorder important. Persistent disabling symptoms sometimes delay recovery in a difficult to predict subset of mTBI patients. Despite abundant data, clinicians need better tools to assess and predict recovery. Data-driven decision support holds promise for accurate clinical prediction tools for mTBI due to its ability to identify hidden correlations in complex datasets. We apply a Locality-Sensitive Hashing model enhanced by varied statistical methods to cluster blood biomarker level trajectories acquired over multiple time points. Additional features derived from demographics, injury context, neurocognitive assessment, and postural stability assessment are extracted using an autoencoder to augment the model. The data, obtained from FITBIR, consisted of 301 concussed subjects (athletes and cadets). Clustering identified 11 different biomarker trajectories. Two of the trajectories (rising GFAP and rising NF-L) were associated with a greater risk of loss of consciousness or post-traumatic amnesia at onset. The ability to cluster blood biomarker trajectories enhances the possibilities for precision medicine approaches to mTBI.

中文翻译：

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无监督学习揭示轻度 TBI 后血液生物标志物轨迹的异质性


脑震荡，也称为轻度创伤性脑损伤 (mTBI)，是一个日益严重的健康挑战。在美国，每年大约诊断出四百万例脑震荡。脑震荡是一种在因果关系、症状和结果方面具有异质性的疾病，因此针对这种疾病的精准医学方法非常重要。持续的致残症状有时会延迟难以预测的 mTBI 患者的康复。尽管数据丰富，临床医生仍需要更好的工具来评估和预测康复情况。数据驱动的决策支持有望成为 mTBI 的准确临床预测工具，因为它能够识别复杂数据集中隐藏的相关性。我们应用通过各种统计方法增强的局部敏感哈希模型来对多个时间点获取的血液生物标志物水平轨迹进行聚类。使用自动编码器提取来自人口统计、伤害背景、神经认知评估和姿势稳定性评估的其他特征来增强模型。从 FITBIR 获得的数据包括 301 名脑震荡受试者（运动员和学员）。聚类确定了 11 种不同的生物标志物轨迹。其中两条轨迹（GFAP 上升和 NF-L 上升）与发病时意识丧失或创伤后遗忘症的更大风险相关。血液生物标志物轨迹聚类的能力增强了针对 mTBI 的精准医学方法的可能性。