Adeza Biomedical Corporation

Other

Duane DeSieno

Jerome Lapointe

Andrew E Senyei

Robert O Hussa

Ricardo Martinez

Emory V Anderson

Edward Nemec

Gail Marzolf

Ronald Pong

Lynn Jones

Jerome P LaPointe

Systems and methods for medical diagnosis or risk assessment for a patient are provided. These systems and methods are designed to be employed at the point of care, such as in emergency rooms and operating rooms, or in any situation in which a rapid and accurate result is desired. The systems and methods process patient data, particularly data from point of care diagnostic tests or assays, including immunoassays, electrocardiograms, X-rays and other such tests, and provide an indication of a medical condition or risk or absence thereof. The systems include an instrument for reading or evaluating the test data and software for converting the data into diagnostic or risk assessment information.

Point of care diagnostic systems

Decision-support systems, such as neural networks, for assessing the risk of delivery within a predetermined time or the likelihood of preterm delivery are provided.

Methods for selecting, developing and improving diagnostic tests for pregnancy-related conditions

Methods are provided for developing medical diagnostic tests using decision-support systems, such as neural networks. Patient data or information, typically patient history or clinical data, are analyzed by the decision-support systems to identify important or relevant variables and decision-support systems are trained on the patient data. Patient data are augmented by biochemical test data, or results, where available, to refine performance. The resulting decision-support systems are employed to evaluate specific observation values and test results, to guide the development of biochemical or other diagnostic tests, too assess a course of treatment, to identify new diagnostic tests and disease markers, to identify useful therapies, and to provide the decision-support functionality for the test. Methods for identification of important input variables for a medical diagnostic tests for use in training the decision-support systems to guide the development of the tests, for improving the sensitivity and specificity of such tests, and for selecting diagnostic tests that improve overall diagnosis of, or potential for, a disease state and that permit the effectiveness of a selected therapeutic protocol to be assessed are provided. The methods for identification can be applied in any field in which statistics are used to determine outcomes. A method for evaluating the effectiveness of any given diagnostic test is also provided.

Method for selecting medical and biochemical diagnostic tests using neural network-related applications

A graded-learning processing network which grades its performance as it maps the input-output relationship during a training period. At the end of an operation of the Processing Newtork, its performance is graded and network variables are adjusted or amended, and the processing network is operated again and its performance is graded. The order or direction of performance grades (e.g. better or worse) are noted and the adjustments or amendments of the processing network may proceed in the same direction (or opposite direction) depending upon the grading of subsequent performances. This obviates the need for information about a desired response on output performance of the processing network at any given time, and is conducive to efficient learning for improved performance in a processing environment in which the operational parameters are not known.

Graded learning device and method

Growing community of inventors

La Jolla, CA, United States of America

Duane DeSieno