| Author | Message | ||
     Mental_sachinodu Side Hero Username: Mental_sachinodu Post Number: 8152 Registered: 10-2008 Posted From: 208.85.128.5 Rating: N/A |
hmm, i agree n disagree to some extent. each patients medical history is different anedhi in a retricted parameter world, but as you expand the known or identifiable parameters they become more similar, or correlated. thappu anipisthe please clarify, length links maathram eeyakandi flzzz. i ahve a very short attention span right now  the world of appearances may or may not be real, or both may and may not be real - or may be indescribable; or may be real and indescribable, or unreal and indescribable; or in the end may be read and unreal and indescribable - its all Syadvada | ||
| Mental_sachinodu Side Hero Username: Mental_sachinodu Post Number: 8151 Registered: 10-2008 Posted From: 208.85.128.5 Rating: N/A |
i think this is basically an attempt at a learning algorithm, not a final predication machine. just was doing some reading, and found it interestin. there must be alot of data mining involved.
I completely agree, but the article somehow seems to suggest that these algorithms are going to be generic, and they will be based on existing data, not about a particular disease or model. may be im understanding it wrong. I was part of a research project related to this during grad times. where informatics was very particular disease based. as a student i wondered if this can be extended to a more generic system. andhuke konchem curiosity if there has been a break through in this , while i was drinking my spare time out the world of appearances may or may not be real, or both may and may not be real - or may be indescribable; or may be real and indescribable, or unreal and indescribable; or in the end may be read and unreal and indescribable - its all Syadvada | ||
| Ballasticmissile Junior Artist Username: Ballasticmissile Post Number: 266 Registered: 07-2012 Posted From: 125.99.197.136 Rating: N/A |
are you doctor or know about bioinformatics or related to that field in any way by chance... better to be a failure at something you love than to be success at some thing you hate-my heart is yours for ever | ||
| Ishan Side Hero Username: Ishan Post Number: 9640 Registered: 01-2009 Posted From: 98.201.146.48 Rating: N/A |
This is the growing area of Medical Informatics. Several of these models act as pointers to physicians to proceed towards differential diagnosis. As an outsider this might intrigue you but there are tons of such theories out there, some of them ARE useful indeed. To build robust models they should take as much information as possible in to account, for example genomics data. Current models however help only to an extent. Nothing beats bench work diagnotic methods. | ||
| Ballasticmissile Junior Artist Username: Ballasticmissile Post Number: 264 Registered: 07-2012 Posted From: 125.99.197.136 Rating: N/A |
i think may be helpful to some extent,but con not predict accurately...as one persons medical history is diff from other,....and the way the body reacts to the external agents is diff....so similar health problems does not indicate similar behavior of being to all conditions.... better to be a failure at something you love than to be success at some thing you hate-my heart is yours for ever | ||
| Ipc302 Moderator Username: Ipc302 Post Number: 15673 Registered: 02-2008 Rating: N/A |
idea bagundhi but human disease diagnosis lanti complex process ni algorithms predcut chese chance undha... either way manaki ee vishayam lo knowledge nill.. | ||
| Mental_sachinodu Side Hero Username: Mental_sachinodu Post Number: 8150 Registered: 10-2008 Posted From: 208.85.128.5 Rating: N/A |
isonti alogs meedha konsem light eyyandi. flzzzz. any concrete reading materials!! University of Washington researchers have created algorithms that make predictions based on what a patient has already experienced as well as the experiences of other patients showing a similar medical history, through analysis of medical records from thousands of patients. According to some experts, when combined with genomic studies, this can truly provide insight into patients'' probable future ailments. University of Washimgton's algorithms are not genomics driven. This provides physicians with insights on what might be coming next for a patient, based on experiences of other patients, says Washington professor Tyler McCormick. What differentiates the model from others is that it shares information across patients who have similar health problems, which allows for better predictions when details of a patient's medical history are hard to come by, according to McCormick. The new algorithm can compare a patient's health problems with other patients who have a more extensive medical record that includes similar symptoms. the world of appearances may or may not be real, or both may and may not be real - or may be indescribable; or may be real and indescribable, or unreal and indescribable; or in the end may be read and unreal and indescribable - its all Syadvada |
