Computer assisted methods for diagnosing diseases

• US 5,769,074 A
• Filed: 05/03/1996
• Issued: 06/23/1998
• Est. Priority Date: 10/13/1994
• Status: Expired due to Term

• Current Assignee: Horus Therapeutics, Inc.
• Original Assignee: Horus Therapeutics, Inc.
• Inventors: Zhang, Zhen, Barnhill, Stephen D.
• Primary Examiner(s): Jaworski, Francis
• Application Number:
  US08/642,848
• Time in Patent Office: 781 Days
• Field of Search: 128/630, 128/924, 364/413.01, 364/413.02, 364/413.26, 382/156-158
• US Class Current: 600/300
• CPC Class Codes: G01N 33/50 Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing measuring or testing processes involving enzymes or microorganisms, compositions or test papers therefor; processes for forming such compositions, condition responsive control in microbiological or enzymological processes C12Q
• CPC Class Codes: G01N 33/53 Immunoassay; Biospecific binding assay; Materials therefor
• CPC Class Codes: G01N 33/573 for enzymes or isoenzymes
• CPC Class Codes: G01N 33/57484 involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16H 70/60 relating to pathologies
• CPC Class Codes: Y10S 128/924 using artificial intelligence

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.

Claims

1. 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.

2. 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.

3. The method of claim 2, wherein the disease is osteoporosis, breast cancer, ovarian cancer, colon cancer, prostate cancer, or testicular cancer.

4. The method of claim 3, wherein the disease is prostate cancer.

5. 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.

6. The method of claim 3, wherein the disease is osteoporosis.

7. 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.

8. The method of claim 3, wherein the disease is ovarian cancer.

9. 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.

10. The method of claim 3, wherein the disease is breast cancer.

11. 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.

12. The method of claim 3, wherein the disease is testicular cancer.

13. 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.

14. The method of claim 3, wherein the disease is colon cancer.

15. The method of claim 14, wherein the biomarkers are patient age, LASA-P®

, CA19.9, CEA or any combination of the biomarkers.

16. An apparatus, comprising a computer containing the trained neural network of claim 1.

17. 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.

18. 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.