Publications by authors named "Dubravka Ukalovic"
Article Synopsis
- The study aimed to create machine learning models to predict the effectiveness of five different biological disease-modifying antirheumatic drugs (bDMARDs) using patient data from the Austrian Biologics Registry.
- A total of 1397 patients' data with 19 variables was analyzed, leading to the development of models that can predict the risk of ineffectiveness of these drugs within the first 26 weeks of treatment.
- Results showed varied prediction accuracy for each drug, highlighting the importance of specific patient factors, such as dosage and co-therapy, suggesting that machine learning can aid in tailored drug selection for patients.
Treat-to-target (T2T) is a main therapeutic strategy in rheumatology; however, patients and rheumatologists currently have little support in making the best treatment decision. Clinical decision support systems (CDSSs) could offer this support. The aim of this study was to investigate the accuracy, effectiveness, usability, and acceptance of such a CDSS-Rheuma Care Manager (RCM)-including an artificial intelligence (AI)-powered flare risk prediction tool to support the management of rheumatoid arthritis (RA).
View Article and Find Full Text PDFBackground: There have been multiple efforts toward individual prediction of recurrent strokes based on structured clinical and imaging data using machine learning algorithms. Some of these efforts resulted in relatively accurate prediction models. However, acquiring clinical and imaging data is typically possible at provider sites only and is associated with additional costs.
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