Freeman College of Management, Bucknell University, 1 Dent Drive, Lewisburg, PA, 17837-2005, USA.
Department of Neurology, Division of Cerebrovascular Diseases, Geisinger Medical Center, 100 N Academy Ave, Danville, PA, 17822, USA.
BMC Med Inform Decis Mak. 2020 Jun 18;20(1):112. doi: 10.1186/s12911-020-01154-6.
Transient ischemic attack (TIA) is a brief episode of neurological dysfunction resulting from cerebral ischemia not associated with permanent cerebral infarction. TIA is associated with high diagnostic errors because of the subjective nature of findings and the lack of clinical and imaging biomarkers. The goal of this study was to design and evaluate a novel multinomial classification model, based on a combination of feature selection mechanisms coupled with logistic regression, to predict the likelihood of TIA, TIA mimics, and minor stroke.
We conducted our modeling on consecutive patients who were evaluated in our health system with an initial diagnosis of TIA in a 9-month period. We established the final diagnoses after the clinical evaluation by independent verification from two stroke neurologists. We used Recursive Feature Elimination (RFE) and Least Absolute Shrinkage and Selection Operator (LASSO) for prediction modeling.
The RFE-based classifier correctly predicts 78% of the overall observations. In particular, the classifier correctly identifies 68% of the cases labeled as "TIA mimic" and 83% of the "TIA" discharge diagnosis. The LASSO classifier had an overall accuracy of 74%. Both the RFE and LASSO-based classifiers tied or outperformed the ABCD2 score and the Diagnosis of TIA (DOT) score. With respect to predicting TIA, the RFE-based classifier has 61.1% accuracy, the LASSO-based classifier has 79.5% accuracy, whereas the DOT score applied to the dataset yields an accuracy of 63.1%.
The results of this pilot study indicate that a multinomial classification model, based on a combination of feature selection mechanisms coupled with logistic regression, can be used to effectively differentiate between TIA, TIA mimics, and minor stroke.
短暂性脑缺血发作（TIA）是一种短暂的神经功能障碍，由脑缺血引起，但不伴有永久性脑梗死。由于发现结果的主观性以及缺乏临床和影像学生物标志物，TIA 常伴有较高的诊断错误率。本研究旨在设计和评估一种新的多项分类模型，该模型基于特征选择机制与逻辑回归相结合，以预测 TIA、TIA 模拟和小中风的可能性。
我们对在 9 个月期间在我们的医疗系统中接受初始 TIA 诊断的连续患者进行了建模。我们通过两名卒中神经病学家的独立验证，在临床评估后确定最终诊断。我们使用递归特征消除（RFE）和最小绝对收缩和选择算子（LASSO）进行预测建模。
基于 RFE 的分类器正确预测了 78%的总体观察结果。特别是，该分类器正确识别了 68%标记为“TIA 模拟”的病例和 83%的“TIA”出院诊断。LASSO 分类器的整体准确率为 74%。基于 RFE 和 LASSO 的分类器的准确性均与 ABCD2 评分和 TIA 诊断评分（DOT）相当或优于它们。就预测 TIA 而言，基于 RFE 的分类器的准确率为 61.1%，基于 LASSO 的分类器的准确率为 79.5%，而 DOT 评分应用于数据集的准确率为 63.1%。
这项初步研究的结果表明，基于特征选择机制与逻辑回归相结合的多项分类模型可有效区分 TIA、TIA 模拟和小中风。
