Arterial Disease Computational Prediction and Health Record Feature Ranking Among Patients Diagnosed With Inflammatory Bowel Disease

Abstract

Inflammatory bowel diseases (IBDs) are a group of disorders causing chronic inflammation of small intestine and colon, and include Chron's disease and ulcerative colitis as most common occurrences. Patients suffering from IBD have more chances to experience an arterial event, such as a stroke or an acute coronary syndrome. In this setting, computational data mining methods applied to electronic medical records of patients diagnosed with IBD can provide useful information regarding the possibility for them to develop arterial diseases, in few minutes and at small cost. In this manuscript, we analyzed a dataset of 90 patients diagnosed with IBD, 30 of which having an arterial disease. After detecting the capability of predicting the arterial event and the most important features of the whole dataset, we repeated the analysis only on the subset of 30 patients suffering from both IBD and arterial disease. Our results show that machine learning can precisely predict both the occurrence of an arterial event and its type (stroke or acute coronary syndrome) from medical records, and can provide useful rankings about the most important clinical variables available in the dataset. Our otherwise unobservable findings can have a strong impact in the clinical settings, allowing physicians and medical doctors to make better decisions regarding prognoses and therapies of patients suffering from this disease.

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