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The more drugs someone is taking, the more likely the risk of drug-drug interactions. Physicians prescribing the drugs cannot predict all the possible side effects that could occur because there is so much that is unknown about potential adverse effects from combining drugs. Drugs meant to help patients often produce side effects in which certain drugs block the release of life-dependent enzymes in the body which can result in the drug becoming toxic. Drug companies have long relied on conventional tools to try to predict whether certain drug candidates will have side effects that end up inhibiting enzyme action in patients, but their predictions are wrong about a third of the time. In those cases, these enzyme-blocking side effects related to toxicity may come to light only during human trials, resulting in millions of dollars and years of effort going to waste. It’s difficult to perform experiments on a large scale to get reliable information on potential side effects from drug combinations, given there are more than 5,000 drugs on the US market, which makes it impossible to test different combinations of all these drugs.
AI will assist physicians in analyzing drug interactions in order to improve side effect prediction. By leveraging information from past cases where patients developed side effects, AI can apply its pattern recognition features to existing clinical trial patients to predict whether or not they would develop side effects based on the combination of drugs prescribed. There are 1000 drug side effects, meaning that combinations of drugs can have over 100 billion different outcomes. Statistical explanations will assist physicians in their understanding of the top risk drivers in drug interactions, enabling them to prescribe the best combination of medicine given the vast possibility of reactions. Leveraging AI will also allow your organization to develop a repository of side effect data, improving your physicians understanding of any potential treatment strategies.
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Healthcare companies are using machine learning and AI to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from readmission risk and occupancy rates to marketing, in order to make data-driven decisions that lead to increased profitability.