今日词汇
medications药物治疗
prescribed规定的
repurposing再利用
coronary冠状的
artery动脉
informatics信息学
clinicians临床医生
原文（内容选自techxplore）
Scientists have developed a machine-learning method that crunches massive amounts of data to help determine which existing medications could improve outcomes in diseases for which they are not prescribed.
The intent of this work is to speed up drug repurposing, which is not a new concept.
The Ohio State University researchers created a framework that combines enormous patient care-related datasets with high-powered computation to arrive at repurposed drug candidates and the estimated effects of those existing medications on a defined set of outcomes.
Though this study focused on proposed repurposing of drugs to prevent heart failure and stroke in patients with coronary artery disease, the framework is flexible—and could be applied to most diseases.
"This work shows how artificial intelligence can be used to 'test' a drug on a patient, and speed up hypothesis generation and potentially speed up a clinical trial," said senior author Ping Zhang, assistant professor of computer science and engineering and biomedical informatics at Ohio State. "But we will never replace the physician—drug decisions will always be made by clinicians."
Drug repurposing is an attractive pursuit because it could lower the risk associated with safety testing of new medications and dramatically reduce the time it takes to get a drug into the marketplace for clinical use.
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