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挖掘医疗数据以改善患者预后

Mining Medical Data to Improve Patient Outcomes
课程网址: http://videolectures.net/kdd2010_rao_mmdi/  
主讲教师: R Bharat Rao
开课单位: 德勤会计师事务所
开课时间: 2010-10-01
课程语种: 英语
中文简介:
上个世纪,诊断测试的准确性和灵敏度大幅提高:从观察外部症状到精确的实验室面板,到非侵入性内部检查的复杂成像方法,以及在不久的将来,使用基因组和床边的分子分析。这种改进的诊断准确性导致医生可获得的患者数据呈指数增长。此外,医学知识不断增长,医生们涌入了一系列新的测试,更新了关于如何诊断和治疗患者的临床指南,以及临床试验的循证结果。随着医疗保健转变为越来越个性化的医疗实践,这些趋势 - 患者数据和医疗知识的增加 - 只会加剧。 数据挖掘方法有很大的机会来帮助医生,改善患者护理,控制成本,并最终挽救生命。在本次演讲中,我们将概述推出新的医疗保健数据挖掘产品所面临的特殊挑战,并为希望在该领域创建新业务的企业家确定一些关键要点。我们首先分析产品的临床需求,以挖掘医学图像,使放射科医师能够识别无症状患者的癌症和其他疾病,从而尽早开始治疗。下一步是个性化治疗选择,其需要数据挖掘方法来挖掘不同的患者数据源,包括图像,自由文本,实验室,药房,分子和基因组数据。我们将讨论如何确定这些产品的范围和市场规模,并确定我们已经解决的关键方法问题。我们专注于过去十年中我们必须解决的临床,监管和营销挑战,因为我们已经从概念转变为今天在全球成千上万患者中使用的部署产品。最后,我们通过强调结果来证明数据挖掘对患者护理和改善结果的影响。
课程简介: The last century has seen a massive increase in the accuracy and sensitivity of diagnostic tests: from observing external symptoms, to precise laboratory panels, to complex imaging methods for non-invasive internal examinations, to, in the very near future, the use of genomic and molecular analysis at the bedside. This improved diagnostic accuracy has resulted in an exponential increase in the patient data available to the physician. Furthermore, medical knowledge is continuously growing, with physicians being flooded with an expanding array of new tests, updated clinical guidelines on how to diagnose and treat patients, and evidence-based results from clinical trials. Both these trends – the increase in patient data and medical knowledge – will only intensify, as healthcare transforms into the practice of increasingly personalized medicine. There is a tremendous opportunity for data mining methods to assist the physician, improve patient care, control costs, and ultimately to save lives. In this talk we will provide an overview of the special challenges faced in launching new healthcare data mining products, and identify a few key take aways for entrepreneurs who want to create new businesses in this domain. We begin by analyzing the clinical need for products to mine medical images to enable radiologists to identify cancers and other medical conditions in asymptomatic patients, and thus begin treatment as early as possible. The next step is personalized therapy selection, which requires data mining methods to mine different patient data sources, including images, free text, labs, pharmacy, molecular & genomic data. We discuss how to determine the scope and market size for products such as these, and identify the key methodological issues we have tackled. We focus on the clinical, regulatory and marketing challenges that we have had to solve over the last decade, as we have gone from concepts, to deployed products that are used today in thousands of patient encounters worldwide. We conclude by highlighting results that demonstrate the impact of data mining on patient care and improved outcomes.
关 键 词: 分子分析; 数据挖掘方法; 个性化治疗选择
课程来源: 视频讲座网
最后编审: 2019-05-11:cjy
阅读次数: 63