US5769074A
Expired / Lapsed
US
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Computer assisted methods for diagnosing diseases
Assignee: Horus Therapeutics Inc
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Filing: 1996-05-03
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Granted: 1998-06-23
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Expires: 2016-05-03
Abstract
Abstract The simultaneous multi access reasoning technology system of the present invention utilizes both existing knowledge and implicit information that can be numerically extracted from training data to provide a method and apparatus for diagnosing disease and treating a patient. This technology further comprises a system for receiving patient data from another location, analyzing the data in a trained neural network, producing a diagnostic value, and optionally transmitting the diagnostic value to another location.
Inventors
- 👤 Stephen D. Barnhill
- 👤 Zhen Zhang
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-23 09:47
CPC Classifications
G; G01; G01N; G01N33/00; G01N33/48; G01N33/50; G01N33/53; G01N33/575; G01N33/5758; G01N33/573; G16; G16H; G16H15/00; G16H50/00; G16H50/20; G16H50/30; G16H50/50; G16H10/00; G16H10/60; G16H70/00; G16H70/60; Y; Y10; Y10S; Y10S128/00; Y10S128/92; Y10S128/923; Y10S128/924
Claims Analysis
Total Claims: 19
Claim 1
Independent Claim
116 words
A method for training a computer-based neural network to be used in diagnosing or prognosing disease in a patient comprising: preprocessing patient biomarkers, comprising: selecting patient biomarkers associated with a disease process; statistically and/or computationally testing discriminating power for indicating presence or absence of the disease of the selected patient biomarkers individually in linear and/or non-linear combination; applying statistical, mathematical, or computational tools, and/or expert knowledge for the derivation of secondary input to the neural network that are linear or non-linear combinations of the original or transformed biomarkers; selecting only those patient biomarkers or derived secondary inputs that show discriminating power; and training the computer-based neural network using the preprocessed patient biomarkers or derived secondary inputs.
Claim 2
Independent Claim
116 words
A method for training a computer-based neural network to be used in diagnosing or prognosing disease in a patient comprising: preprocessing patient biomarkers, comprising: selecting patient biomarkers associated with a disease process; statistically and/or computationally testing discriminating power for indicating presence or absence of the disease of the selected patient biomarkers individually in linear and/or non-linear combination; applying statistical, mathematical, or computational tools, and/or expert knowledge for the derivation of secondary input to the neural network that are linear or non-linear combinations of the original or transformed biomarkers; selecting only those patient biomarkers or derived secondary inputs that show discriminating power; and training the computer-based neural network using the preprocessed patient biomarkers or derived secondary inputs.
Claim 3
Dependent Claim
66 words
A method for diagnosing or prognosing a disease in a patient, comprising: introducing patient biomarkers into the trained computer-based neural network of claim 1; receiving an output value from the computer-based neural network corresponding to the presence or the absence or the severity of the disease; and transmitting the output value from the computer-based neural network to an output value receiver connected to a display means.
Claim 4
Dependent Claim
21 words
The method of claim 2, wherein the disease is osteoporosis, breast cancer, ovarian cancer, colon cancer, prostate cancer, or testicular cancer.
Claim 5
Dependent Claim
11 words
The method of claim 3, wherein the disease is prostate cancer.
Claim 6
Dependent Claim
23 words
The method of claim 4, wherein the biomarkers are patient age, LASA-P®, PAP, PSA, CK-MB, CK-MM, CK-BB or any combination of the biomarkers.
Claim 7
Dependent Claim
10 words
The method of claim 3, wherein the disease is osteoporosis.
Claim 8
Dependent Claim
30 words
The method of claim 6, wherein the biomarkers are patient age, serum calcium, serum phosphate, estradiol, progesterone, ALP, ALP Isoenzyme 1, ALP Isoenzyme 2, or any combination of the biomarkers.
Claim 9
Dependent Claim
11 words
The method of claim 3, wherein the disease is ovarian cancer.
Claim 10
Dependent Claim
25 words
The method of claim 8, wherein the biomarkers are patient age, LASA-P®, CA125, CA125II, DM/70K, MCSF, OVX1, CA7-24, CA19-9 or any combination of the biomarkers.
Claim 11
Dependent Claim
11 words
The method of claim 3, wherein the disease is breast cancer.
Claim 12
Dependent Claim
22 words
The method of claim 10, wherein the biomarkers are patient age, LASA-P®, CEA, CA 15-3®, HER2/neu, or any combination of the biomarkers.
Claim 13
Dependent Claim
11 words
The method of claim 3, wherein the disease is testicular cancer.
Claim 14
Dependent Claim
22 words
The method of claim 12, wherein the biomarkers are patient age, LASA-P®, AFP, HCG-Beta, CA 15-3® or any combination of the biomarkers.
Claim 15
Dependent Claim
11 words
The method of claim 3, wherein the disease is colon cancer.
Claim 16
Dependent Claim
20 words
The method of claim 14, wherein the biomarkers are patient age, LASA-P®, CA19.9, CEA or any combination of the biomarkers.
Claim 17
Dependent Claim
13 words
An apparatus, comprising a computer containing the trained neural network of claim 1.
Claim 18
Dependent Claim
35 words
The method of claim 1, wherein the patient biomarkers are demographic data, electrical diagnostic data, imaging diagnostic data, histologic diagnostic data, and concentrations of biomarkers in a biological fluid or a combination of these biomarkers.
Claim 19
Dependent Claim
73 words
The method of claim 1, further comprising introducing the output value from the trained computer-based neural network and a second set of preprocessed values from patient biomarkers into a second trained neural network in a computer, wherein the second trained neural network is trained to diagnose or prognose the disease and produces a second output value, the second output value corresponding to the presence or the absence or the severity of the disease.
References Cited (Backward Citations)
USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2001-06-22 | 2001-12-22 | 2002-06-23 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2005-06-22 | 2005-12-22 | 2006-06-23 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2009-06-22 | 2009-12-22 | 2010-06-23 | $7700.00 | Window Closed / Lapsed |