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Background: Borderline personality disorder (BPD) is a complex clinical state with highly polymorphic symptoms and signs. Studies have demonstrated that people with a BPD diagnosis are likely to have numerous co-occurring psychiatric disorders and physical comorbidities. The aim of our study was to obtain further insight about the associations among comorbidities of BPD and to demonstrate the practicality of using association rule mining (ARM) technique in clinical databases.Methods: A retrospective case-control study was conducted on information of 1460 patients (292 BPD patients and 1168 control patients) selected from the Taiwan National Health Insurance Research Database. Information on physical and psychiatric comorbidities, which were diagnosed within 3 years before and after enrollment, was collected. A logistic regression model was used to calculate the odds ratios of comorbidities between patients with and without BPD. ARM technique was used to study the associations of BPD and two or more psychiatric comorbidities.Results: We classified physical comorbidities into 13 categories according to the International Classification of Diseases, Ninth Revision, Clinical Modification system, and the results indicated that the 12 categories were more common in the BPD patients than in the control patients (except congenital anomalies). However, psychiatric comorbidities, including depressive disorder, bipolar disorder, anxiety disorder, sleep disorder, substance use disorder, and mental retardation were more common in the BPD patients than in the control patients. Furthermore, the associations of BPD and two or more comorbidities were evaluated.Conclusion: Most physical and psychiatric disorders were more common in the BPD patients than in the control patients. Because the failure to remit from BPD is associated with suffering from chronic physical conditions and because psychiatric comorbidities may lead to delays in diagnosis of BPD, clinicians caring for people with BPD should be aware of possible comorbidities. [ABSTRACT FROM AUTHOR]

Objectives: Due to the narrow therapeutic range and high drug-to-drug interactions (DDIs), improving the adequate use of warfarin for the elderly is crucial in clinical practice. This study examines whether the effectiveness of using warfarin among elderly inpatients can be improved when machine learning techniques and data from the laboratory information system are incorporated., Methods: Having employed 288 validated clinical cases in the DDI group and 89 cases in the non-DDI group, we evaluate the prediction performance of seven classification techniques, with and without an Adaptive Boosting (AdaBoost) algorithm. Measures including accuracy, sensitivity, specificity and area under the curve are used to evaluate model performance., Results: Decision tree-based classifiers outperform other investigated classifiers in all evaluation measures. The classifiers supplemented with AdaBoost can generally improve the performance. In addition, weight, congestive heart failure, and gender are among the top three critical variables affecting prediction accuracy for the non-DDI group, while age, ALT, and warfarin doses are the most influential factors for the DDI group., Conclusion: Medical decision support systems incorporating decision tree-based approaches improve predicting performance and thus may serve as a supplementary tool in clinical practice. Information from laboratory tests and inpatients' history should not be ignored because related variables are shown to be decisive in our prediction models, especially when the DDIs exist.