Using predictive process monitoring to assist thrombolytic therapy decision-making for ischemic stroke patients
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RESEARCH
Open Access
Using predictive process monitoring to assist thrombolytic therapy decisionmaking for ischemic stroke patients Haifeng Xu1,2, Jianfei Pang1, Xi Yang2, Mei Li3 and Dongsheng Zhao1* From 5th China Health Information Processing Conference Guangzhou, China. 22-24 November 2019
Abstract Background: 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. Methods: 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. Results: 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. Conclusions: 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. Keywords: Predictive process monitoring, Clinical decision support, Clinical guideline, Stroke thrombolytic therapy
Background In clinical practice, doctors often need to make decisions based on their experience of diagnosis and treatment, as well as the specific situation of each patient. For example, they want to know whether thrombolysis therapy is necessary for patients with ischemic acute stroke. * Correspondence: [email protected] 1 Information Center, Academy of Military Medical Sciences, Beijing, People’s Republic of China Full list of author information is available at the end of the article
According to reference [1], the general doctor’s judgment of thrombolysis for stroke patients is not accurate, and the misperception for the rate of fatal intracranial hemorrhage using rt-PA may interfere with their willingness to endorse this treatment. Clinical guidelines (CGs) offer the best practice in medical activities and play an important role for improving medical quality as well as reducing risks. However, evidence in CG is essentially a form of statistical knowledge, w
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