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Method for selecting medical and biochemical diagnostic tests using neural network-related applications
| Details |
Inventors: Lapointe, Jerome; DeSieno, Duane;
Assignee: Adeza Biomedical Corporation (Sunnyvale, CA)
Primary Examiner: Powell; Mark R.
Assistant Examiner: Starks; Wilbert
Attorney, Agent or Firm: Seidman; Stephanie L. Heller Ehrman White & McAuliffe LLP
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. |
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DETAILED DESCRIPTION Methods using decision-support systems for the diagnosis of and for aiding in the diagnosis of diseases, disorders and other medical conditions are provided.
The methods provided herein, include a method of using patient history data and identification of important variables to develop a diagnostic test; a method for identification of important selected variables; a method of designing a diagnostic test; a method of evaluating the usefulness of diagnostic test; a method of expanding clinical utility of diagnostic test, and a method of selecting a course of treatment by predicting the outcome of various possible treatments.
Also provided are disease parameters or variables to aid in the diagnosis of disorders, including any disorders that are difficult to diagnose, such as endometriosis, predicting pregnancy related events, such as the likelihood of delivery within a particular time period, and other such disorders relevant to women's health.
It is understood that although women's disorders are exemplified herein, the methods herein are applicable to any disorder or condition.
Also provided are means to use neural network training to guide the development of the tests to improve their sensitivity and specificity, and to select diagnostic tests that improve overall diagnosis of, or potential for, a disease state or medical condition.
Finally, a method for evaluating the effectiveness of any given diagnostic test is described.
Thus, provided herein is a method for identifying variables or sets of variables that aid in the diagnosis of disorders or conditions.
In the methods for identifying and selection of important variables and generating systems for diagnosis, patient data or information, typically patient history or clinical data are collected and variables based on this data are identified.
For example, the data includes information for each patient regarding the number of pregnancies each patient has had.
The extracted variable is, thus, number of pregnancies.
The variables are analyzed by the decision-support systems, exemplified by neural networks, to identify important or relevant variables
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