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面向医药行业和临床试验
的大数据分析

云 Hortonworks 是领导者。阅读 Forrester Wave。

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寻找不可访问数据的对策

What happens when the data you need is hidden in silos, or when billions of dollars are riding on drug testing data you can’t access? How do you see a long-term view of 10 billion records to understand biological response to drugs? Researchers in the pharmaceutical industry turn to Hortonworks for advanced big data analytics on integrated translational data and to gain a holistic view of their pharmaceutical data.

挖掘药品数据的潜力

Big Data integration, pharmaceutical big data analytics, internal and external collaboration, portfolio decision support, more efficient clinical trials, faster time to market, improved yields, improved safety - these are just a few of the benefits pharmaceutical companies around the world achieve by tapping into the full power of their pharma big data.

使用案例

默克公司提高了疫苗产量:努力制造“黄金批次”

默克公司通过对制造数据进行分析,将“黄金批次”最重要的可预测变量分离出来,从而提高了疫苗产量。长期以来,默克公司的领导层一直依靠精益制造理念来提高产量和降低成本,但是要不断地提高产量,这种方法已经显得越来越力不从心。于是,他们通过 Open Enterprise Hadoop 来寻找新方法,以期进一步降低成本和提高产量。默克公司向 Hortonworks 求助,对一种疫苗过去 10 年的 255 个批次的记录数据进行了挖掘。这些数据分布在 16 个维护和构建管理系统中,并且其中包含有关校准设置、空气压力,以及温度和湿度的精确性传感器数据。通过将所有这些数据集中到 Hortonworks Data Platform 上并运行 150 亿次计算后,Merck 终于找到了过去十年一直在思考的问题的新答案。默克团队可以从数百个变量中分辨出哪些对提高产量有效。该公司进一步将这些经验运用到其他疫苗的生产中去,全心全力地提高药品的质量并最大限度地降低价格。观看 InformationWeek(信息周刊)的 Doug Henschen 对默克公司的 George Llado 的采访。


最大限度降低药品制造流程中的资源浪费

One Hortonworks pharmaceutical customer uses HDP for a single view of its supply chain and their self-declared “War on Waste”. The operations team added up the ingredients going into making their drugs, and compared that with the physical product they shipped. They found a big gap between the two and launched their War on Waste, using HDP big data analytics to identify where those valuable resources were going. Once it identifies those root causes of waste, real-time alerts in HDP notify the team when they are at risk of exceeding pre-determined thresholds.


转化研究:将科学研究变成个体化药品

The goal of Translational Research is to apply the results of laboratory research towards improving human health. Hadoop empowers researchers, clinicians, and analysts to unlock insights from translational data to drive evidence-based medicine programs. The data sources for translational research are complex and typically locked in data siloes, making it difficult for scientists to obtain an integrated, holistic view of their data. Other challenges revolve around data latency (the delay in getting data loaded into traditional data stores), handling unstructured and semi-structured types of data, and bridging lack of collaborative analysis between translation and clinical development groups. Researchers are turning to Open Enterprise Hadoop as a cost-effective, reliable platform for managing big data in clinical trials and performing advanced analytics on integrated translational data. HDP allows translational and clinical groups to combine key data from sources such as: Omics (genomics, proteomics, transcription profiling, etc) Preclinical data Electronic lab notebooks Clinical data warehouses Tissue imaging data Medical devices and sensors File sources (such as Excel and SAS) Medical literature Through Hadoop, analysts can build a holistic view that helps them understand biological response and molecular mechanisms for compounds or drugs. They’re also able to uncover biomarkers for use in R&D and clinical trials. Finally, they can be assured that all data will be stored forever, in its native format, for analysis with multiple future applications.


下一代测序

IT systems cannot economically store and process next generation sequencing (NGS) data. For example, primary sequencing results are in large image format and are too costly to store over the long term. Point solutions have lacked the flexibility to keep up with changing analytical methodologies, and are often expensive to customize and maintain. Open Enterprise Hadoop overcomes those challenges by helping data scientists and researchers unlock insights from NGS data while preserving the raw results on a reliable, cost-effective platform. NGS scientists are discovering the benefits of large-scale processing and analysis delivered by HDP components such as Apache Spark. Pharmaceutical researchers are using Hadoop to easily ingest diverse data types from external sources of genetic data, such as TCGA , GENBank , and EMBL. Another clear advantage of HDP for NGS is that researchers have access to cutting-edge bioinformatics tools built specifically for Hadoop. These enable analysis of various NGS data formats, sorting of reads, and merging of results. This takes NGS to the next level through: Batch processing of large NGS data sets Integration of internal with publically available external sequence data Permanent data storage for large image files, in their native format Substantial cost savings on data processing and storage.

HDP 使用真实数据来提供真实证据

Real-World Evidence (RWE) promises to quantify improvements to health outcomes and treatments, but this data must be available at scale. High data storage and processing costs, challenges with merging structured and unstructured data, and an over-reliance on informatics resources for analysis-ready data have all slowed the evolution of RWE. With Hadoop, RWE groups are combining key data sources, including claims, prescriptions, electronic medical records, HIE, and social media, to obtain a full view of RWE. With big data analytics in the pharmaceutical industry, analysts are unlocking real insights and delivering advanced insights via cost-effective and familiar tools such as SAS® ,R®, TIBCO™ Spotfire®, or Tableau®. RWE through Hadoop delivers value with optimal health resource utilization across different patient cohorts, a holistic view of cost/quality tradeoffs, analysis of treatment pathways, competitive pricing studies, concomitant medication analysis, clinical trial targeting based on geographic & demographic prevalence of disease, prioritization of pipelined drug candidates, metrics for performance-based pricing contracts, drug adherence studies, and permanent data storage for compliance audits.

在研究之前不间断访问原始数据

利用真实数据来交付真实证据
HDP 真实证据 (RWE) 有望量化对健康成果和治疗的改进,但是这种数据必须大规模可用。数据存储和处理的高昂成本、难以合并结构化和非结构化数据以及过度依赖可分析数据的信息资源,这些都影响了 RWE 的发展。借助 Hadoop,RWE 团队正在合并关键数据源(包括索赔、处方、电子病历、HIE 和社交媒体)以全面了解 RWE。分析可提供真实洞察力并通过经济高效且常见的工具(如 SAS®、R®、TIBCO™ Spotfire® 或 Tableau®)来提供高级分析洞察力。借助 Hadoop 的 RWE,可以实现以下方面的价值:不同患者群组的最佳健康资源利用率,全面了解成本/质量取舍,分析治疗方法,竞争价格研究,合并用药分析,基于疾病的地理和人口发病率的临床试验定位靶标,管道化候选药物的优先级划分,度量基于绩效的定价合同,坚持服药研究,合规性审计的永久数据存储。