文本和数据挖掘遇上制药行业:Markus Bundschus讲话

Steve Hardin
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引用次数: 3

摘要

文本和数据挖掘已被证明对世界生物医学研究产生了巨大的影响,特别是对德国彭茨堡的罗氏诊断公司。罗氏诊断的研究人员从患者文献、基因组癌症样本和PubMed文章等来源获取信息,能够以一种有助于创建个性化医疗保健的方式构建数据。用于构建结构化数据库的文本挖掘往往会产生与生物医学研究最相关的信息,因此Roche使用非结构化数据自动构建知识库。这个知识库,即疾病标记关联数据库,提供全文、摘要或精选数据的搜索功能。该数据库由5000万篇科学摘要组成,并依赖于基于规则的引擎和机器学习引擎。通过整合来自患者护理、诊断和治疗的信息,医疗保健行业可以转向数字化和更高效的护理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Text and Data Mining Meets the Pharmaceutical Industry: Markus Bundschus Speaks

EDITOR'S SUMMARY

Text and data mining have proven to greatly impact the world of biomedical research, especially for Roche Diagnostics in Penzberg, Germany. Taking information from such sources as patient literature, genomic cancer samples and PubMed articles, researchers at Roche Diagnostics are able to structure the data in a way that lends itself to creating personalized healthcare. Text mining used to build structured databases tends to yield the most relevant information for biomedical research, so Roche uses unstructured data to build a knowledge base automatically. This knowledge base, the disease marker association database, offers search capabilities for full text, abstracts or curated data. The database is made up of 50-million scientific abstracts and leans on rule-based engines as well as machine learning engines. By combining information from patient care, diagnoses and treatment, the healthcare industry can see a shift to digitization and more efficient care.

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