Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflicted to the brain, ultimately resulting in diverse and complex outcomes. Using a data-driven approach on many distinct data elements may be necessary to describe this large set of outcomes and thereby robustly depict the nuanced differences among TBI patients' recovery. In this work, we develop a method for modeling large heterogeneous data types relevant to TBI. Our approach is geared toward the probabilistic representation of mixed continuous and discrete variables with missing values. The model is trained on a dataset encompassing a variety of data types, including demographics, blood-based biomarkers, and imaging findings. In addition, it includes a set of clinical outcome assessments at 3, 6, and 12 months post-injury. The model is used to stratify patients into distinct groups in an unsupervised learning setting. We use the model to infer outcomes using input data, and show that the collection of input data reduces uncertainty of outcomes over a baseline approach. In addition, we quantify the performance of a likelihood scoring technique that can be used to self-evaluate the extrapolation risk of prognosis on unseen patients.
翻译:临床指标对创伤性脑损伤(TBI)结果的预测既不容易,也不精确地从临床指标中确定。这在一定程度上是由于对大脑造成破坏,最终导致多种复杂的结果。对许多不同的数据要素采用数据驱动的方法,可能有必要描述这组大组结果,从而强有力地描述TBI病人康复过程中的细微差异。在这项工作中,我们开发了一种方法,用于模拟与TBI有关的大型不同数据类型。我们的方法针对的是具有缺失值的混合连续变量和离散变量的概率性表示。该模型在包含各种数据类型的数据集(包括人口统计、基于血液的生物标记和成像像结论)方面接受培训。此外,它还包括一套3、6和12个月后的临床结果评估。该模型用于将病人分成一个不统一学习环境的不同组。我们使用该模型来预测结果,使用输入数据,并表明收集输入数据会减少基线方法的结果的不确定性。此外,我们还用一个模型来量化对病人进行自我风险评估的可能性。我们使用了一种自我风险评估的方法。