title: A data-driven living review for pharmacogenomic decision support in cancer treatment.
abstract: With drastically decreasing costs of genetic sequencing, it has become feasible to use individual genetic markers to optimize treatment selection in cancer therapy. However, it is still difficult for medical practitioners to integrate these new kinds of data into clinical routine, since available information is growing rapidly. We demonstrate how a blend of manual curation and automated data extraction and evidence synthesis can be used to generate a 'living review', a summarization of current evidence on cancer classification, corresponding genetic markers, genetic tests and treatment options that can be used by clinicians to refine treatment choices. In contrast to a classical review, this automated 'living review' offers the opportunity of automatically updating core content when available data changes, making it easier to keep an overview of the best current evidence. We discuss some of the findings we made while creating a prototype of a 'living review' for colorectal cancer pharmacotherapy.
authors.affiliation: Medical University of Vienna, Austria.
authors.forename: Gökhan
authors.initials: G
authors.lastname: Karakülah
authors.forename: Aslı
authors.initials: A
authors.lastname: Suner
authors.forename: Klaus-Peter
authors.initials: KP
authors.lastname: Adlassnig
authors.forename: Matthias
authors.initials: M
authors.lastname: Samwald
mesh_terms: D057225:Data Mining / Q000379:methods*; D020000:Decision Support Systems, Clinical; D004360:Drug Therapy, Computer-Assisted / Q000379:methods; D057286:Electronic Health Records; D005820:Genetic Testing / Q000379:methods; D055991:Health Records, Personal; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis / Q000188:drug therapy* / Q000235:genetics*; D010597:Pharmacogenetics / Q000379:methods*