Mining Medical Data to Improve Patient Outcomes

author: Bharat Rao, Deloitte LLP
published: Oct. 1, 2010,   recorded: July 2010,   views: 6314
Categories

Slides

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography

Description

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.

See Also:

Download slides icon Download slides: kdd2010_rao_mmdi_01.pdf (732.5 KB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Reviews and comments:

Comment1 Mardhiah , January 24, 2011 at 11:10 p.m.:

Good job!

Write your own review or comment:

make sure you have javascript enabled or clear this field: