Matjaž Kukar

TL;DR: The ROC analysis shows significant improvements of sensitivity and specificity compared to the performance of the clinicians and it is shown that the predictive power of standard tests with that of machine learning techniques can be significantly improved.

TL;DR: A novel system AgroDSS is described that bridges the gap between agricultural systems and state-of-the-art decision support methodology and provides a cloud-based decision support toolbox, allowing farmers to upload their own data, utilize several data analysis methods and retrieve their outputs.

TL;DR: This study is the first to show that a machine learning predictive model based on blood tests alone, can be successfully applied to predict hematologic diseases and could open up unprecedented possibilities in medical diagnosis.

TL;DR: In this paper, a machine learning model for COVID-19 diagnosis was constructed based and cross-validated on the routine blood tests of 5333 patients with various bacterial and viral infections, and 160 positive patients.

TL;DR: A machine learning model was constructed based and cross-validated on the routine blood tests of 5333 patients with various bacterial and viral infections, and 160 COVID-19-positive patients that showed that the blood parameters of the patients with a severe CO VID-19 course are more like the parameters of a bacterial than a viral infection.