Other

Texaco Inc.

Neuralmed, Inc.

Neuralware, Inc.

Timothy J Graettinger

William B Braden

Paul A DuBose

Kent E Heckendoorn

Naveen V Bhat

Alexander J Federowicz

Paul Alton DuBose

A neural network system and method for diagnosing patients' medical conditions provide an efficient aid in identifying and interpreting factors which are significant in the medical diagnosis. The neural network is trained to recognize medical conditions by being provided with input data that is available for a number of patients, and diagnosis made by physicians in each case. Upon completion of the training period the neural network system uses input measurement and interview data to produce a score, or a graded classification, of a patient's medical condition that is accompanied with a diagnosis interpretation. The interpretation is a sorted catalogue of individual factors and interactions that influenced the score. The interpretive facility of the present invention is based on comparison with a set of nominal values for each input factor or interaction. It can assist the physician in making a diagnosis of the patient's condition and can further provide a 'second opinion' that may confirm the physician's findings or point to ambiguities that call for a more detailed analysis.

Computer-based neural network system and method for medical diagnosis

A control system having four major components: a target optimizer, a path optimizer, a neural network adaptation controller and a neural network. In the target optimizer, the controlled variables are optimized to provide the most economically desirable outputs, subject to operating constraints. Various manipulated variable and disturbance values are provided for modeling purposes. The neural network receives as inputs a plurality of settings for each manipulated and disturbance variable. For target optimization all the neural network input values are set equal to produce a steady state controlled variable value. The entire process is repeated with differing manipulated variable values until an optimal solution develops. The resulting target controlled and manipulated variable values are provided to the path optimizer to allow the manipulated variables to be adjusted to obtain the target output. Various manipulated variable values are developed to model moves from current to desired values. In this case trend indicating values of the manipulated and disturbance variables are provided to produce time varying values of the controlled variables. The process is repeated until an optimal path is obtained, at which time the manipulated variable values are applied to the actual process. On a periodic basis all of the disturbance, manipulated and controlled variables are sampled to find areas where the training of the neural network is sparse or where high dynamic conditions are indicated. These values are added to the set of values used to train the neural network.

Control system using an adaptive neural network for target and path

Growing community of inventors

Bethel Park, PA, United States of America

Timothy J Graettinger