How machine learning is advancing precision medicine and pharmacogenomics
By
Dr. Sai Balasubramanian, M.D., J.D.
Summary
The article discusses how machine learning is transforming precision medicine and pharmacogenomics by moving beyond traditional evidence-based medicine that relies on statistical averages. It explains that while conventional medicine prescribes treatments based on what works for the majority, ML enables personalized treatment approaches by analyzing individual patient data, genetic profiles, and biomarkers. The piece highlights how ML algorithms can identify patterns in complex biological data to predict drug responses, optimize dosing, and reduce adverse reactions for specific patient subgroups, ushering in a new era of tailored healthcare.
Source
bskyHow machine learning is advancing precision medicine and pharmacogenomicsforbes.comKey quotes
· 3 pulledMedicine has always operated as an 'evidence based' field, meaning that it generally pursues experimentation to gather evidence to support a specific claim for diagnostic and treatment success.
Medications often operate on the laws of statistical averages: if an overwhelming majority of people respond positively to a specific drug, then it is generally prescribed more broadly as applicable to a wider part of the population, until the evidence proves otherwise.
However, this also means that a percentage of the population will be impacted by the margin of error and may experience side effects or lack of efficacy.
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