{"id":2509,"date":"2018-04-13T13:16:46","date_gmt":"2018-04-13T20:16:46","guid":{"rendered":"https:\/\/keck.usc.edu\/news\/how-to-prevent-opioid-addiction-before-it-begins\/"},"modified":"2023-11-19T12:29:13","modified_gmt":"2023-11-19T20:29:13","slug":"how-to-prevent-opioid-addiction-before-it-begins","status":"publish","type":"post","link":"https:\/\/keck.usc.edu\/news\/how-to-prevent-opioid-addiction-before-it-begins\/","title":{"rendered":"How to prevent opioid addiction before it begins"},"content":{"rendered":"\n \n \n\n\n\n\n\n\n\n
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How to prevent opioid addiction before it begins<\/h1>\n\n\n<\/div>\n \n \n
\n Rheagan Rizio<\/span>\n \n April 13, 2018<\/span>\n <\/div>\n \n \n
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USC Viterbi\u2019s Yan Liu uses machine learning to fight the opioid addiction crisis sweeping the nation. (Photo\/Will Taylor)<\/figcaption>\n <\/figure>\n \n \n \n\n<\/div>\n \n\n <\/div><\/div>\n\r\n\r\n\n\n \n \n\n\n\n\n\n\n\n
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USC Viterbi School of Engineering researchers are working to combat opioid addiction before it can begin<\/a>.
\nYan Liu, associate professor in computer science at the USC Viterbi School of Engineering, is one of the leaders in the field<\/a>. She heads the USC Machine Learning Center<\/a>. The interdisciplinary center \u2014 with representatives from USC Viterbi, the USC Marshall School of Business and the USC Dornsife College of Letters, Arts and Sciences \u2014 enlists researchers to harness the potential of machine learning.
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USC Viterbi\u2019s Yan Liu uses machine learning to fight the opioid addiction crisis sweeping the nation. (Photo\/Will Taylor)<\/figcaption><\/figure>In one of the Machine Learning Center\u2019s 2017 studies, computer mapping was used to study opioid addiction and adverse effect analysis. In simpler terms, the researchers used computer-learning models \u2014 similar to those in self-driving cars or those in the iPhone X\u2019s new facial recognition software \u2014 and programmed them to sift through a patient\u2019s medical history, identifying trends that may indicate opioid dependency and addiction. In essence, they\u2019re learning how to predict opioid dependency.
\nIdentifying these patterns is key. One of Liu\u2019s goals with her research is to successfully predict, based on medical history, whether a patient is susceptible to developing an addiction to opioid-based pain medication before physicians ever prescribe it. If key warning variables can be identified, physicians can then take steps to combat the addiction before it can begin, prescribing the patient alternative forms of pain medication to avoid their exposure altogether.
\n\u201cWe conducted this research because of its importance and urgency,\u201d said Liu, holder of the Philip and Cayley MacDonald Endowed Early Career Chair, who joined USC Viterbi\u2019s faculty in 2010.<\/p>\n

Opioid addiction and a person\u2019s prescription history<\/h3>\n

The study itself achieved its goal, according to Liu: It demonstrated that deep-learning models have the potential to be useful tools when fighting opioid addiction. Models used in the study looked at each patient\u2019s medical history, focusing on several key factors. These included the patient\u2019s prescription history, and whether they had been diagnosed with any other relevant disorders that could indicate an increased risk of developing an addiction to opioid pain medication: alternative substance abuse, recreational drugs, alcohol or anxiety disorders.
\nBased on these traits, the models classified all relevant patients \u2014 those who had previously been prescribed opioid medication \u2014 into three groups: short-term users, long-term users (and opioid-dependent users. The research was done in collaboration with the Mayo Health Clinic, using electronic health records provided by the Rochester Epidemiology Project.
\nResearchers used a sample size of 102,166 patients, one of the biggest datasets ever used for such research, according to Liu, to achieve the most accurate data. The results: 79 percent were short-term users and 21 percent were long-term users. According to the algorithm, 3.47 percent were also opioid-dependent \u2014 a significant increase from the 0.7 percent that had been diagnosed with opioid dependence before the study.
\nBasing its research on data taken from medical records, Liu\u2019s team aimed to protect the privacy of their subjects.
\n\u201cPrivacy is our top concern when we conduct human subject research,\u201d Liu said. \u201cWe only used the data of patients who agreed to share their medical records [for the study], and we followed the strict data-use agreement \u2014 we did not utilize or disclose any direct identifiers of the individual.\u201d<\/p>\n\n\n\n<\/div>\n <\/div>\n\n\n <\/div><\/div>\n","protected":false},"excerpt":{"rendered":"

USC Viterbi researchers respect privacy while reviewing medical histories to predict the possibility of a person\u2019s dependency on opioids<\/p>\n","protected":false},"author":150,"featured_media":2511,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"advgb_blocks_editor_width":"","advgb_blocks_columns_visual_guide":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[6],"tags":[139,135],"class_list":["post-2509","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-press-release","tag-medicine-engineering-science-and-humanities-program","tag-mesh"],"acf":[],"yoast_head":"\nHow to prevent opioid addiction before it begins<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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