Key result
A Bayesian Network machine learning model achieved a precision rate of 84.55% and an AUC of 0.857 for predicting aortic dissection, outperforming the physician benchmark precision of 67.07%.
Why the study?
Aortic dissection requires fast diagnosis, and delay or misdiagnosis causes severe consequences; models were needed to help physicians identify dissection among misdiagnosed patients early.
Does a machine learning model using routine clinical and laboratory attributes improve the early diagnosis of aortic dissection compared to physician judgment?
Observational (n=492)
No
Does a machine learning model using routine clinical and laboratory attributes improve the early diagnosis of aortic dissection compared to physician judgment?
A Bayesian Network machine learning model using 13 routine clinical and laboratory attributes can classify aortic dissection in the early diagnosis phase with 84.55% precision, outperforming baseline physician judgment.
May support ML-assisted early aortic dissection diagnosis; leaves open need for prospective validation before clinical use.
Aortic dissection is one of the most clinical-challenging and life-threatening cardiovascular diseases associated with high morbidity and mortality. Aortic dissection requires fast diagnosis and timely therapy. Any delay or misdiagnosis can cause severe consequence to aortic dissection patients with even higher mortality. To better help physicians identify the potential dissection within the scope of all misdiagnosed patients, this paper describes a method which is developed with data mining methods for aortic dissection patient classification and prediction in the phase of early diagnosis. Various machine learning algorithms were used to build the models which were all trained and tested on the patient dataset with cross validation. Among them, Bayesian Network model achieved the best performance by predicting at a precision rate of 84.55% with Area Under the Curve (AUC) value of 0.857. On this basis, the Bayesian Network model can help physicians better with early diagnosis of aortic dissection in clinical practice. Beyond this study, more data from diverse regions and the internal pathology can be crucial to further build a universal model with broader predictive power.
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Huo et al. (2019) conducted an observational in Aortic dissection (n=492). Bayesian Network machine learning model vs. Physician benchmark was evaluated on Prediction precision rate for classifying aortic dissection (AUC 0.857). A Bayesian Network machine learning model achieved a precision rate of 84.55% and an AUC of 0.857 for predicting aortic dissection, outperforming the physician benchmark precision of 67.07%.
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