Artificial Intelligence: An Effective Strategy to Identify Drug Interactions
Patients who take a variety of different medications are at increased risk of experiencing adverse effects from the interaction of the drugs. This is especially true when the person combines prescription drugs with over-the-counter medications without notifying their healthcare provider.
Now, however, technology may help to solve this problem. Machine learning can be used to detect potential adverse effects from the interaction of drugs that a patient is taking. An algorithm is used to analyze data about drug interactions reported from the FDA and other sources. If it recognizes possible dangerous side effects from a drug interaction, the AI technology will alert the patient. So, for example, AI would be used to gather all of the data available about diseases of the liver and determine which drug interactions affect the liver.
Defined, an algorithm is a group of instructions created to perform a specific task. It can be something as basic as instructions for multiplying two numbers or as complicated as playing a compressed video file. A recipe is a common example of an algorithm. It is a list of instructions that are used to perform a specific task, such as cooking a roast or baking a cake.
A Potential Drawback
One drawback of this algorithmic setup for drug interactions, however, is that there are many drug interactions with varying degrees of negative side effects. As a result, the patient would be getting a lot of alerts, and this could lead the person to simply ignore them after a while. To handle this problem, the algorithm would be modified to report only those drug interactions that are serious enough to jeopardize a person’s life or seriously injure their health.
The algorithm will be working with data on nearly 3,000 drugs and more than 110,000 combinations, which could result in more than 1.7 million types of adverse interactions.
But, as researchers point out, the adverse drug interactions are also affected by the genetic background of the person taking the drugs, and so the developers are looking to incorporate genomic data of individual patients in future versions of the algorithm, leading to more personalized treatment.
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