Genetic breakthrough in multiple sclerosis -- biggest for decades

This is what data mining and predictive modeling is all about, a tool for subject matter experts to identify "new suspects". Once predictive modeling helps identify new suspects the subject matter experts apply their knowledge to determine whether this has value in their filed.

New processors present problems, payoff

The new challenge and opportunity in designing microprocessors is presented in this article. A new operating systems will be needed to optimized the utilization of these microprocessors. In my opinion the combination of data mining technologies that allow "automatic data mining (or predictive modeling) factories", intelligent agents, and parallel computing are going to be the fundamental blocks in addressing this challenge. Those technologies combined with gaming, simulation and other visualization technologies will be part of ingredients needed for this leap into the future.

Conceptually I think that it will be like this:

1. Data mining technologies will provide the fundamentals of pattern and error detection. Due to the complexity and diversity of the rich data environment that we currently face we will need the ability to have part of this technology embedded into any program, and we will probably need multiple and different data mining models analyzing data simultaneously so as to customize the needs of the end users;
2. Intelligent mobile agent technologies would be fundamental to access and process data from servers, mainframes, and handheld devices like cellphones;
3. Web based technologies will be fundamental in solving finding patterns and in improving remote communications;
4. Parallel computing technologies will be needed to optimize the processing of large quantities of data; and
5. Visualization technologies that make complex patterns easily understood, while simulateously adhering to establish laws of nature (i.e., medicine, or physics), or previous experience (business rules) would also be a keystone in this endevour.

Our biggest challenge is going to be to reach out acrross multiple disciplines and technologies to integrate all these technologies into a great schema. In this sense we are all pioneers. We bring different skills set that we combined will mark the path for others to follow. It will not be easy, but it will be worthwhile!

Understanding Molecular Imaging

GE Healthcare is correct in their assessment that if you can track molecular changes in cells and link them to disease progression an enterprise will be demostrating "the power of molecular imaging". I believe that web analytics algorithms and software is what is going to make the step possible. The reason is that web analytics algorithms allows to predict a variable (i.e., disease) given a series of inputs (medical procedures and other diagnoses) over a sequence.

Web Analytics and Healthcare: Disease Progression

We are starting to develop a heatlhcare model for disease progression prediction using Microsoft Sequence Clustering algorithm in SQL 2005 Server Analysis Services. It seems to work well, but I would like to make a comparison with other algorithms. I was wondering if anyone in the community knows how can we obtain Gooogle's permission to use (or adapt) their Web Analytics algorithm for disease progression prediction. Or if anyone has any other suggestion for Web Analytics software that we could try. We have the largest private payer healthcare database in the U.S. so we need robust algorithms.

Moving Closer To Solving Lou Gehrig's Disease Mystery

http://www.medicalnewstoday.com/medicalnews.php?newsid=75539

This is an area that I hope predictive modeling and data mining can make a difference. If we can do a linear disease progression modeling at the cellular level we might be able to diagnose and prevent ALS before its onset.