巨量醫療資料之時序事件追蹤與分析

林慶和

doi:10.6342/NTU.2015.01898

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巨量医疗资料之时序事件追踪与分析

Temporal Event Tracing on Big Medical Data Analytics

林庆和 , 博士　　导师：曹承础　　

繁体中文 DOI： 10.6342/NTU.2015.01898

时序性医疗事件；医疗资料；药物不良反应；巨量资料； NoSQL ；分片丛集； MapReduce ； temporal medical event ； medical data ； drug reaction ； big data ； NoSQL ； sharded cluster ； MapReduce

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摘要 │ 参考文献 (69) │ 被引用文献 (1) │ 国际计量

摘要〈TOP〉

研究背景–全球高龄化趋势以及社会型态的改变，使得人口健康问题与健康照护的支出日渐沉重。为防患於未然，各地的政策制定者纷纷推动医疗资料的电子化，用以帮助实现医疗保健系统的五大目标：(1)提高医疗的质量、安全及效率、(2)致力於病人所需要的健康照护、(3)增进健康照护的协调、(4)提高人口的健康、以及(5)确保私密性和安全性。然而，这些电子医疗资料要能“有意义的使用”、扩大用途、以及创造出更多效益，并不容易，且存在很多困难的研究议题待克服。目前医疗资料分散在不同业界，资料汇集困难，也鲜少相互连结分析。而且，医疗纪录经长年累月已累积形成巨量资料(Big Data)，除对原有的计画与研究造成重大冲击，也带来巨量医疗资料的整合、处理、分析、以及健康照护促进等新的研究议题，且衍生加值创新应用与商机。
研究目标–有监於目前生医领域在巨量资料分析的基础建设仍严重落後於趋势、研究人员依然花费大量时间在建构与组织他们的资料，以及在这些资料上诠释意义与发掘问题。为推动生医巨量资料分析的变革，本研究的目标–提出一套从资料储存到分析处理的完整方法，并基於此方法，验证二个本研究所提出的巨量资料创新应用：(1)快速检验医疗通报事件，如药物不良反应通报事件、(2)时序性医疗事件的及时监视追踪，如新上市药品的监视。为达成此目标，本研究提出的方法须具备：(1)时效性，能迅速回应处理结果、(2)效能，须以低成本达成、(3)扩充性，须能水平扩充运算能力及储存容量、(4)计算容易，方便检验及追踪指标的计算、以及(5)应用性。
研究方法–有别於流行病学研究方法，时序性医疗事件的追踪与分析通常无法事先设定所要研究的问题。本研究提出一个新的模式，提供一个可及时追踪监视医疗事件与揭露相关资讯的运作机制。此模式包含四个部分，分别为：(1)来源资料，即现有的电子医疗资料、(2)资料管理，包含巨量医疗资料储存模型(PDMdoc)、时序性医疗事件模型(TMEdoc)、以及丛集的分片策略与管理等、(3)处理与运算，包含分片丛集的运作程序、云端运算MapReduce巨量资料处理方法、以及一个整合的时序事件追踪分析方法、(4)追踪指标，内容包含指标项目，以及记载每一个发生此事件的病患的指标数值。其中，影响此模式能否发挥及时监视追踪的功能，关键在於资料管理以及处理与运算的效能。
结果–本研究方法的复杂度：(1)丛集的水平扩充性与平行度为1，亦即每加入一个分片节点至丛集系统，运算能力及储存容量均会增加一个分片单位，且不受丛集的节点数影响、(2)网路I/O，仅与查询结果的资料量有关，也不受节点数影响、(3)搜寻与磁碟I/O，PDMdoc与TMEdoc平均搜寻时间分别为O(1)与O(logd(STMEdoc/B))，平均磁碟I/O (寻轨时间、旋转延迟、传递时间)分别为( O(1), O(1), O(EPDMdoc) ) 与 ( O(logd(STMEdoc/B)), O(1), O(ETMEdoc × LTMEdoc) )。在实验方面：(1)资料，取自於全民健保资料库承保抽样归人档(LHID2010)，计有100万人从1996年至2010年期间的健保资料、(2)测试系统，采用MongoDB以及5部PC建置成分片丛集系统(3个分片节点)、(3)实验结果：(a)效能测试，搜寻8个疾病族群的罹病患者，单伺服机系统与分片丛集的花费时间分别介於0.607∼63.248与0.336∼29.484秒，二者平均效能比为1 : 2.024、(b)药物不良反应通报事件的检验，以美国FDA於2009年9月发布的Januvia药品安全资讯为通报案例，检验结果(odds ratio = 1.626)显示此事件在台湾也有显着的情形、(c)新上市药品的监视，系统处理TME的数量可达140,000/秒以上，估计每日可监视数千至数万种药品。

并列摘要〈TOP〉

Backgroud – Global aging trend combined with societal changes are creating population health problems and increasing health care spending. As a precaution, local policy makers have been promoting electronic medical data to help achieve five major goals of health care system: 1) improving health care quality, safety, and performance, 2) committing to patient health needs, 3) improving health care coordination, 4) improving the health of the population, and 5) ensuring privacy and security. However, in order to make these medical data to be "Meaningful Use", to expand data usage, and to create more profits, many research difficulties have to be overcome and it will not an easy task. Currently medical data is scattered in different industries, data collection is difficult, and mutual analysis is rare. Furthermore, medical records have been accumulating to big data after many years. This not only significantly impacts original plan and research, but also creates bonus innovative applications and opportunities.
Objectives – Given that the current biomedical field in big data analysis infrastructure is still seriously lagging behind current trend, researchers have to spend considerable time on constructing and organizing their data and on interpreting meaning and identifying issues with these data. To revolutionize biomedical big data analysis, this study proposes a set of methods ranging from data storage to data analysis. Based on this set of methods, two novel applications for big data were verified, 1) prompt testing of medical reported incidents, such as adverse drug reactions reported incidents, 2) timely monitoring and tracking of temporal medical events, such as monitoring of newly marketed drugs. To achieve the objectives, this set of methods must have: 1) timeliness, to quickly respond process results, 2) effectiveness, shall reach low cost reach, 3) scalability, shall allow horizontal expansion of computing power and storage capacity, 4) easy calculation, convenient for testing and calculating tracking indicators, and 5) applicability.
Methods – Unlike epidemiological research methods, problems to be studied for tracking and analysis of temporal medical events cannot be delivered in advance. This study proposes a new model, providing an operation mechanism which allows for timely tracking and monitoring of medical events and uncovering relevant information. This model contains four parts, which are: 1) source of data, namely current electronic medical data, 2) data management, including big data storage model PDMdoc, temporal medical events model TMEdoc, and tactics and management of sharded cluster, 3) processing and computing, including sharded cluster operating procedures, cloud computing MapReduce big data processing methods, and an integrated temporal event tracking analysis, 4) tracking indicator, content mainly comprising of a number of indicators, and recording patient index value for every occurrence. Among them, indicators belong to practical application level; therefore impacting whether this model can achieve timely monitoring and tracking function, the essential part lies in data management and efficiency of processing and calculation method.
Results – Complexity of the research methods in this study: 1) sharded cluster horizontal scaling and degree of parallelism is 1 unit, specifically, every time a shard is added to the cluster system, the computing power and storage capacity will both be increased by 1 unit, not affected by the number of cluster nodes, 2) network I/O, only relevant to the amount of data for search results, irrelevant to the number of cluster nodes, 3) search and disk I/O, average seek time for PDMdoc and TMEdoc are O(1) and O(logd(STMEdoc/B)), respectively, average disk I/O for seek time, rotational delay, transmission time are "O(1), O(1), O(EPDMdoc)" and "O(logd(STMEdoc/B)), O(1), O(ETMEdoc × LTMEdoc)", respectively. Statistics in experiments performed, 1) data, gathered from Taiwan NHIRD LHID2010 Dataset, containing health care data of a total of one million people for the period 1996 to 2010, 2) test system, sharded cluster containing 3 shard nodes built on MongoDB and five PCs, 3) experiments results: a) benchmarks, the times required to search diseased patients from 8 disease groups for single server system and sharded cluster range from 0.607 to 63.248 seconds and from 0.336 to 29.484 seconds, respectively, the two systems have performance ratio of 1:2.024, b) adverse drug reactions reported incidents, take Januvia drug safety information published by FDA in September, 2009 for example, the test result for odds ratio is 1.626, showing that this type of incidents had significant occurrences in Taiwan as well, c) monitoring for newly marketed drugs, system processing capacity for number of TME can exceed 140,000 per second, the daily number of drugs that can be monitored is estimated to be above tens of thousands.

参考文献 ( 69 ) 〈TOP〉

[1] United Nations, Department of Economic and Social Affairs, Population Division (2013). World Population Prospects: The 2012 Revision, Volume I: Comprehensive Tables ST/ESA/SER.A/336. and, Key Findings and Advance Tables. Working Paper No. ESA/P/WP.227.
连结：
[2] United Nations, Department of Economic and Social Affairs, Population Division (2007). World Population Prospects: The 2006 Revision, Highlights,Working Paper No. ESA/P/WP.202.
连结：
[7] WHO, United States of America, Statistics (2012), Available: http://www.who.int/countries/usa/en/
连结：
[8] Y.P. Wen, S.M. Huang, T.L. Chiang, “An analysis of the growth of healthcare expenditure in Taiwan:healthcare inflation, volume-intensity, and equity,” Taiwan J Public Health, Vol. 31, No.1, pp. 1–10, 2012.
连结：
[12] HITECH, Available: http://www.hitechanswers.net/
连结：

被引用文献 (1) 〈TOP〉

杨天怡(2017)。天气对於皮肤病之相关性研究。国立台湾大学资讯管理学系学位论文。2017。1-53。

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