Aleksandar Poleksic, Carson Turner, Rishabh Dalal, Paul Gray, Lei Xie
{"title":"Mining FDA resources to compute population-specific frequencies of adverse drug reactions.","authors":"Aleksandar Poleksic, Carson Turner, Rishabh Dalal, Paul Gray, Lei Xie","doi":"10.1109/BIBM.2017.8217935","DOIUrl":null,"url":null,"abstract":"<p><p>Adverse drug reactions (ADRs) represent one of the main health and economic problems in the world. With increasing data on ADRs, there is an increased need for software tools capable of organizing and storing the information on drug-ADR associations in a form that is easy to use and understand. Here we present a step by step computational procedure capable of extracting drug-ADR frequency data from the large collection of patient safety reports stored in the Federal Drug Administration database. Our procedure is the first of its type capable of generating population specific drug-ADR frequencies. The drug-ADR data generated by our method can be made specific to a single patient population group (such as gender or age) or a single therapy characteristic (such as drug dosage, duration of therapy) or any combination of such.</p>","PeriodicalId":74563,"journal":{"name":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","volume":"2017 ","pages":"1809-1814"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BIBM.2017.8217935","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. IEEE International Conference on Bioinformatics and Biomedicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIBM.2017.8217935","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2017/12/18 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Adverse drug reactions (ADRs) represent one of the main health and economic problems in the world. With increasing data on ADRs, there is an increased need for software tools capable of organizing and storing the information on drug-ADR associations in a form that is easy to use and understand. Here we present a step by step computational procedure capable of extracting drug-ADR frequency data from the large collection of patient safety reports stored in the Federal Drug Administration database. Our procedure is the first of its type capable of generating population specific drug-ADR frequencies. The drug-ADR data generated by our method can be made specific to a single patient population group (such as gender or age) or a single therapy characteristic (such as drug dosage, duration of therapy) or any combination of such.