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Using predictive process monitoring to assist thrombolytic therapy decision-making for ischemic stroke patients

机译：使用预测过程监测来促使缺血性卒中患者溶栓治疗决策

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摘要

Although clinical guidelines provide the best practice for medical activities, there are some limitations in using clinical guidelines to assistant decision-making in practical application, such as long update cycle and low compliance of doctors with the guidelines. Driven by data of actual cases, process mining technology provides the possibility to remedy these shortcomings of clinical guidelines. We propose a clinical decision support method using predictive process monitoring, which could be complementary with clinical guidelines, to assist medical staff with thrombolytic therapy decision-making for stroke patients. Firstly, we construct a labeled data set of 1191 cases to show whether each case actually need thrombolytic therapy, and whether it conform to the clinical guidelines. After prefix extraction and filtering the control flow of completed cases, the sequences with data flow are encoded, and corresponding prediction models are trained. Compared with the labeled results, the average accuracy of our prediction models for intravenous thrombolysis and arterial thrombolysis on the test set are 0.96 and 0.91, and AUC are 0.93 and 0.85 respectively. Compared with the recommendation of clinical guidelines, the accuracy, recall and AUC of our predictive models are higher. The performance and feasibility of this method are verified by taking thrombolytic decision-making of patients with ischemic stroke as an example. When the clinical guidelines are not applicable, doctors could be provided with assistant decision-making by referring to similar historical cases using predictive process monitoring.
机译：虽然临床指南为医疗活动提供了最佳做法，但在实际应用中使用临床指南，在实际应用中的辅助决策中存在一些局限性，例如长期更新周期和医生与指导方针的低遵守情况。由实际情况的数据驱动，过程采矿技术提供了解决这些临床指南缺点的可能性。我们提出了一种使用预测过程监测的临床决策支持方法，可以与临床指南互补，以协助医务人员对中风患者的溶血性治疗决策。首先，我们构建了1191例案例的标记数据集，以展示每种情况是否实际上需要溶栓治疗，以及它是否符合临床指南。在提取和过滤完成情况的控制流程之后，对具有数据流的序列被编码，并且训练了对应的预测模型。与标记结果相比，试验组静脉内溶栓和动脉溶栓的预测模型的平均准确性为0.96和0.91，AUC分别为0.93和0.85。与临床指南的建议相比，我们的预测模型的准确性，召回和AUC更高。通过以缺血性卒中患者的溶栓决策鉴定该方法的性能和可行性作为示例。当临床指南不适用时，通过参考使用预测过程监测的类似历史案例，医生可以提供助理决策。

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