The major challenge in the diagnosis of disseminated intravascular coagulation (DIC) comes from the lack of specific biomarkers, leading to developing composite scoring systems. DIC scores are simple and rapidly applicable. However, optimal fibrin-related markers and their cut-off values remain to be defined, requiring optimization for use. The aim of this study is to optimize the use of DIC-related parameters through machine learning (ML)-approach. Further, we evaluated whether this approach could provide a diagnostic value in DIC diagnosis. For this, 46 DIC-related parameters were investigated for both clinical findings and laboratory results. We retrospectively reviewed 656 DIC-suspected cases at an initial order for full DIC profile and labeled their evaluation results (Set 1; DIC, n = 228; non-DIC, n = 428). Several ML algorithms were tested, and an artificial neural network (ANN) model was established via independent training and testing using 32 selected parameters. This model was externally validated from a different hospital with 217 DIC-suspected cases (Set 2; DIC, n = 80; non-DIC, n = 137). The ANN model represented higher AUC values than the three scoring systems in both set 1 (ANN 0.981; ISTH 0.945; JMHW 0.943; and JAAM 0.928) and set 2 (AUC ANN 0.968; ISTH 0.946). Additionally, the relative importance of the 32 parameters was evaluated. Most parameters had contextual importance, however, their importance in ML-approach was different from the traditional scoring system. Our study demonstrates that ML could optimize the use of clinical parameters with robustness for DIC diagnosis. We believe that this approach could play a supportive role in physicians' medical decision by integrated into electrical health record system. Further prospective validation is required to assess the clinical consequence of ML-approach and their clinical benefit.

中文翻译：

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基于机器学习的弥散性血管内凝血（DIC）诊断：开发，外部验证以及与评分系统的比较。

诊断弥散性血管内凝血（DIC）的主要挑战来自缺乏特异性生物标志物，从而导致开发了复合评分系统。DIC分数很简单并且可以快速应用。然而，最佳的纤维蛋白相关标志物及其临界值仍有待确定，需要优化使用。这项研究的目的是通过机器学习（ML）方法来优化DIC相关参数的使用。此外，我们评估了这种方法是否可以在DIC诊断中提供诊断价值。为此，对46个DIC相关参数进行了临床发现和实验室结果调查。我们以初始顺序回顾了656例DIC疑似病例，以了解其全部DIC概况，并标记了他们的评估结果（第1组； DIC，n = 228；非DIC，n = 428）。测试了几种ML算法，并通过独立训练和测试使用32个选定参数建立了人工神经网络（ANN）模型。该模型已在另一家医院进行了外部验证，其中包括217个DIC可疑病例（第2组； DIC，n = 80；非DIC，n = 137）。在集合1（ANN 0.981; ISTH 0.945; JMHW 0.943;和JAAM 0.928）和集合2（AUC ANN 0.968; ISTH 0.946）中，ANN模型都比三个评分系统具有更高的AUC值。此外，评估了32个参数的相对重要性。大多数参数具有上下文重要性，但是，它们在机器学习方法中的重要性与传统评分系统不同。我们的研究表明，ML可以优化具有鲁棒性的DIC诊断的临床参数的使用。我们认为，这种方法可以在医师的 通过将医疗决策集成到电子健康记录系统中。需要进一步的前瞻性验证来评估ML方法的临床结果及其临床益处。