图 书 馆
挖掘医疗数据以改善患者结果Mining Medical Data to Improve Patient Outcomes |
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| 课程网址: | http://videolectures.net/kdd2010_rao_mmdi/ |
| 主讲教师: | Bharat Rao |
| 开课单位: | 德勤律师事务所 |
| 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敏感性有了大幅提高：从观察外部症状到精确的实验室面板，再到用于无创内部检查的复杂成像方法，再到在不久的将来使用在床边进行基因组和分子分析。这种提高的诊断准确性导致医生可用的患者数据呈指数增长。此外，医学知识不断增长，医生被大量新测试、更新的关于如何诊断和治疗患者的临床指南以及基于证据的临床试验结果所淹没。随着医疗保健转变为日益个性化的医疗实践，这两种趋势——患者数据和医学知识的增加——只会加剧。 数据挖掘方法有巨大的机会来帮助医生、改进病人护理，控制成本，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，并为想要在该领域创建新业务的企业家确定一些关键要点。我们首先分析对挖掘医学图像的产品的临床需求，以使放射科医生能够识别无症状患者的癌症和其他医疗状况，从而尽早开始治疗。下一步是个性化治疗选择，这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。我们讨论如何确定此类产品的范围和市场规模，并确定我们已解决的关键方法论问题。我们专注于在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发展到如今在全球数千名患者中使用的部署产品。最后，我们强调了一些结果，这些结果证明了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: | 临床指南; 数据挖掘; 基因组分析 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-06-09：zyk |
| 最后编审: | 2021-06-09：zyk |
| 阅读次数: | 57 |