本文選題：健康醫(yī)療大數(shù)據(jù) + 罕見病　； 參考：《科技導報》2017年16期

【摘要】：藥物治療是罕見病的主要治療方式,然而目前僅有1%的罕見病能夠得到有效的藥物治療。不同來源的基因組、轉錄組等組學數(shù)據(jù)與臨床表型數(shù)據(jù)融合起來形成的"健康醫(yī)療大數(shù)據(jù)",可以通過集中小規(guī)模罕見病臨床數(shù)據(jù)的方式有效彌補罕見病樣本量少的不足。大數(shù)據(jù)信息可用于研究罕見病新的藥物靶點、探索成熟藥物在罕見病領域新用法、分析藥物不良反應實現(xiàn)個體化用藥,并可進一步通過大數(shù)據(jù)技術建立本地化罕見病知識庫,從而實現(xiàn)罕見病的精準診斷和治療。隨著大數(shù)據(jù)研究的不斷深入,仍然需要突破多組學融合及分析技術、基于真實世界的知識提取技術、基于組學的臨床決策支持等技術壁壘才能使大數(shù)據(jù)在罕見病的診療中得到最大應用。
[Abstract]:Drug therapy is the main treatment for rare diseases. However, only 1% of rare diseases can be effectively treated with drugs. The combination of genomes, transcriptome and clinical phenotypic data to form a "health care big data" can effectively compensate for the shortage of rare diseases by concentrating the clinical data of small scale rare diseases. Big data information can be used to study new drug targets of rare diseases, explore new uses of mature drugs in the field of rare diseases, analyze adverse drug reactions to realize individualized drug use, and further establish a knowledge base of localized rare diseases through big data technology. In order to achieve accurate diagnosis and treatment of rare diseases. With the deepening of big data research, it is still necessary to break through the technology of multi-group learning fusion and analysis, and the technology of knowledge extraction based on the real world. Technical barriers, such as clinical decision support based on cluster, can maximize the application of big data in the diagnosis and treatment of rare diseases.
【作者單位】： 神州數(shù)碼醫(yī)療科技股份有限公司;中國醫(yī)學科學院北京協(xié)和醫(yī)院中心實驗室;
【基金】：國家高技術研究發(fā)展計劃(863計劃)項目(2015AA020106) 國家重點研發(fā)計劃項目(2016YFC0901500) 上海市出生缺陷防治重點實驗室開放課題(16DZKF1007) 國家衛(wèi)生計生委2016年信息化與統(tǒng)計項目
【分類號】：R597

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本文編號：2036060