A machine learning approach developed by Dr. Diego Galeano and Professor Alberto Paccanaro from
It is generally accepted that numerous side effectsdrugs are not observed during clinical trials, but are only identified after the drug enters the market and is purchased by patients. This can cause an increase in morbidity and mortality in the health sector.
Scientists have created a new recommendation algorithm,similar to Netflix, which predicts the tastes and preferences of users, and then suggests films and TV shows suitable for watching. He will be able to determine how patients will respond to the drug. And also calculate the percentage of people who will have side effects after taking the drug in the early stages of human trials. The new solution could change the course of testing in the future.
It is extremely important to predict what the frequency of drug side effects will be after the first stages of clinical trials. At the moment we have no systems that could do this.
Professor Alberto Paccanaro of Royal Holloway
Although an accurate estimate of the frequency of side effectsvital to patient care in clinical practice, it is also essential for pharmaceutical companies as it reduces the risk of drug withdrawal from the market or costly reassessment of the incidence of side effects through new clinical trials.
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