big data meets biomedicine: opportunities & challenges
TRANSCRIPT
12016 The 31th Joint Annual Conference of Biomedical Science 3/26/2016
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IBM Big Data & Analytics Hub. http://www.ibmbigdatahub.com/infographic/four-vs-big-data
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Mayer-Schonberger V, et al. Big Data: A Revolution That Will Transform How We Live, Work, and Think.
Boston: Houghton Mifflin Harcourt, 2013.
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Andreu-Perez J, et al. Big Data for Health. IEEE J Biomed Health Inform. 2015;19(4):1193-208
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Mayer-Schonberger V, et al. Big Data: A Revolution That Will Transform How We Live, Work, and Think.
Boston: Houghton Mifflin Harcourt, 2013.
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• Sampling 1,100
• 3% of error
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r(定醫, 健保) = 0.88
r(定醫, RODS) = 0.89
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門診就診%
急診就診%
座標軸標題
2008-2009年RODS急診、健保與定醫門診類流感監測
RODS急診
定醫門診
健保門診
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http://nidss.cdc.gov.tw/
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Ginsberg J, et al. Detecting influenza epidemics using search engine query data. Nature 2009;457:1012-1014.
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Webster M, Kumar VS. Big Data Diagnostics. Clin Chem 2014;60(8):1130-2.
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肺炎＆流感死亡即時監測• 緣由：美國122城市肺炎＆流感死亡監測系統
• 資料來源：死亡通報網路系統
• 即時性：醫療機構應於七日內以網路通報
• 代表性：74％
• 2009/6/17公布2008年總死亡數共計142,283例
• 網路通報105,516例死亡
• 死亡原因：中文文字（醫師自由書寫）共4欄
• 肺炎＆流感死亡即時監測
• 關鍵字搜尋（肺炎、流感、感冒）
• 撰寫簡易規則研判主要死因
吳宛真等。運用死亡通報資料建立肺炎及流感死亡即時監測。疫情報導 2009
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衛生署肺炎及流感死亡與即時監測每週死亡數比較圖
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r(死亡統計, 及時) = 0.85
Updated: 2009/12/24
肺炎或流感死亡監視
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“Slight increase of
pulmonary vascular
congestion with new
left pleural effusion,
question mild
congestive changes”
pulmonary vascular congestion
change: increase
degree: low
pleural effusion
region: left
status: new
congestive changes
certainty: moderate
degree: low
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Slide Courtesy of Dr. George Hripcsak, DBMI, Columbia University
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Melton GN, Hripcsak G. Automated detection of adverse events using natural language
processing of discharge summaries. J Am Med Inform Assoc. 2005;12(4):448-57.
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Conway M, et al. Using chief complaints for syndromic surveillance: A review of chief
complaint based classifiers in North America. J Biomed Inform. 2013;46(4):734-43.
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Chan TC, et al. Spatiotemporal Analysis of Air Pollution & Asthma Patient Visits in Taipei, Taiwan. Int J Health Geogr.
2009;8:26
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van Panhuis WG, et al. Region-wide synchrony and traveling waves of dengue across eight countries in Southeast Asia.
Proc Natl Acad Sci U S A. 2015;112(42):13069-74
Sim I. Two Ways of Knowing: Big Data and Evidence-Based Medicine. Ann Intern Med. 2016
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Herr TM, et al. A conceptual model for translating omic data into clinical action. J Pathol Inform. 2015;6:46.
DIKW Pyramid
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Kumar S, et al. Center of excellence for mobile sensor data-to-knowledge (MD2K). JAMIA. 2015; 22(6):1137-42.
Mobile Sensor Data-to-Knowledge (MD2K)
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Kumar S, et al. Center of excellence for mobile sensor data-to-knowledge (MD2K). JAMIA. 2015; 22(6):1137-42.
Major Outcomes of MD2K
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Anthony Celi L, et al. “Big Data” in the Intensive Care Unit. Closing the Data Loop. Am J Respir Crit Care
Med 2013;187(11):1157-1160.
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Saeed M, et al. Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II): A public-access
intensive care unit database 2011; 39:952-960.
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Anthony Celi L, et al. “Big Data” in the Intensive Care Unit. Closing the Data Loop. Am J Respir Crit Care
Med 2013;187(11):1157-1160.
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http://cdcdengue.azurewebsites.net/DengueCluster.aspx
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Hampson NB, Weaver LK. Carbon monoxide poisoning and risk for ischemic stroke. Eur J Intern Med. 2016( In press).
Kao CH. Reply letter to the comments. Eur J Intern Med. 2016( In press).
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