FTO1/362: Using Neural Nets in Medical Decision Making

Medical Decision Making using

Bayesian Belief Networks (bbns) play a relevant role in the eld of Artiicial Intelligence in Medicine and they have been successfully applied to a wide variety of medical domains. An appealing character of bbns is that they easily extend into a complete decision-theoretic formalism known as Innuence Diagrams (ids). Unfortunately, bbns and ids require a large amount of information that is not al...

Medical Decision Making Neural Networks in Clinical Medicine on Behalf Of: Society for Medical Decision Making

Using hindsight in medical decision making.

As the clinical picture of a patient evolves over time, more information becomes available. Certain procedure require time to perform, causing delay between the time when the tests are ordered and when the results are available. Furthermore, as the patient's condition changes over time, serial measurements can be made. The availability of more data allows a more accurate assessment of the patie...

From Medical Decision-making to Medical-cost-comparative Decision-making.

WHEN A PHYSICIAN GATHERS A PATIENT’S history, runs diagnostic tests and performs an evaluation, he or she is performing medicine’s most fundamental act: medical decision-making. Patient care is all about physicians bringing their education, training and personal experience to bear with a particular patient and his or her particular symptoms, disease or injury. Frequently, the course of treatmen...

Bayesian decision making using neural fields

Bayesian statistics has become a popular framework in various fields of experimental psychology such as signal detection theory, speech recognition, cue integration and decision making. However, it is still an open question how the human brain actually incorporates this functionality. One assumption is that the activities of populations of neurons encode probability distributions. Indeed, it ha...