Computer model has learned to predict the recurrence of tuberculosis

One of the key health challenges set by WHO is to put an end to the tuberculosis epidemic by 2030. This requires the methods of early diagnosis and effective treatment of this disease. Scientists at TSU with specialists from Siberian State Medical University, Tomsk Oblast Tuberculosis Dispensary and the Research Institute of Tuberculosis (Novosibirsk), have developed a new approach to identifying drug-resistant forms of pulmonary tuberculosis. Researchers have created a computer model that has passed machine learning and can, with an accuracy of more than 95 percent, identify patients at risk.
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