Artwork

Content provided by Dave Pechter, M.S.M.E., Dave Pechter, and M.S.M.E.. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Dave Pechter, M.S.M.E., Dave Pechter, and M.S.M.E. or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Player FM - Podcast App
Go offline with the Player FM app!

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

19:54
 
Share
 

Manage episode 397112748 series 3550916
Content provided by Dave Pechter, M.S.M.E., Dave Pechter, and M.S.M.E.. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Dave Pechter, M.S.M.E., Dave Pechter, and M.S.M.E. or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Volume 25 Issue 2, April 2020

Dave Pechter discusses with Agata Blasiak & Theodore Kee regarding their article, "CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence."
The clinical team attending to a patient upon a diagnosis is faced with two main questions: what treatment, and at what dose? Clinical trials’ results provide the basis for guidance and support for official protocols that clinicians use to base their decisions upon. However, individuals rarely demonstrate the reported response from relevant clinical trials, often the average from a group representing a population or subpopulation. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual’s response to the treatment varies over time with the changes in his or her condition, whether via the indication or physiology. In practice, the drug and the dose selection depend greatly on the medical protocol of the healthcare provider and the medical team’s experience. As such, the results are inherently varied and often suboptimal. Big data approaches have emerged as an excellent decision-making support tool, but their application is limited by multiple challenges, the main one being the availability of sufficiently big datasets with good quality, representative information. An alternative approach—phenotypic personalized medicine (PPM)—finds an appropriate drug combination (quadratic phenotypic optimization platform [QPOP]) and an appropriate dosing strategy over time (CURATE.AI) based on small data collected exclusively from the treated individual. PPM-based approaches have demonstrated superior results over the current standard of care. The side effects are limited while the desired output is maximized, which directly translates into improving the length and quality of individuals’ lives.

  continue reading

29 episodes

Artwork
iconShare
 
Manage episode 397112748 series 3550916
Content provided by Dave Pechter, M.S.M.E., Dave Pechter, and M.S.M.E.. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Dave Pechter, M.S.M.E., Dave Pechter, and M.S.M.E. or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Volume 25 Issue 2, April 2020

Dave Pechter discusses with Agata Blasiak & Theodore Kee regarding their article, "CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence."
The clinical team attending to a patient upon a diagnosis is faced with two main questions: what treatment, and at what dose? Clinical trials’ results provide the basis for guidance and support for official protocols that clinicians use to base their decisions upon. However, individuals rarely demonstrate the reported response from relevant clinical trials, often the average from a group representing a population or subpopulation. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual’s response to the treatment varies over time with the changes in his or her condition, whether via the indication or physiology. In practice, the drug and the dose selection depend greatly on the medical protocol of the healthcare provider and the medical team’s experience. As such, the results are inherently varied and often suboptimal. Big data approaches have emerged as an excellent decision-making support tool, but their application is limited by multiple challenges, the main one being the availability of sufficiently big datasets with good quality, representative information. An alternative approach—phenotypic personalized medicine (PPM)—finds an appropriate drug combination (quadratic phenotypic optimization platform [QPOP]) and an appropriate dosing strategy over time (CURATE.AI) based on small data collected exclusively from the treated individual. PPM-based approaches have demonstrated superior results over the current standard of care. The side effects are limited while the desired output is maximized, which directly translates into improving the length and quality of individuals’ lives.

  continue reading

29 episodes

All episodes

×
 
Loading …

Welcome to Player FM!

Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.

 

Quick Reference Guide