Computer assisted methods for diagnosing diseases

• US 6,248,063 B1
• Filed: 12/22/1997
• Issued: 06/19/2001
• 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 J.
• Application Number:
  US08/997,092
• Time in Patent Office: 1,275 Days
• Field of Search: 600/300, 128/920-924, 395/22, 706/16, 706/21-22, 706/25, 706/31
• 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 computer based system for screening for a disease, prognosing a disease, diagnosing a disease, determining a presence or an absence or a severity of a disease, or a likelihood of obtaining a disease, comprising:

a computation station for receiving and processing a patient data file, said computation station comprising a trained neural network for processing said patient data file and producing an output value corresponding to a presence or an absence or a severity of a disease, or a likelihood of obtaining a disease, wherein said trained neural network is trained with a method comprising;

preprocessing patient biomarkers, comprising;

selecting patient biomarkers associated with the disease process;

statistically and/or computationally testing discriminating power of the selected patient biomarkers individually in linear and/or non-linear combination for indicating the presence or the absence or the severity of the disease, or the likelihood of obtaining the disease;

applying statistical, mathematical, or computational tools, and/or expert knowledge for the derivation of secondary inputs 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. The computer based system of claim 1, further comprising a communication station in communication with said computation station;

wherein said communication station transmits said patient data file to said computation station for introduction into said trained neural network;

wherein said output value from said trained neural network is stored in a result data file; and

wherein said communication station receives said result data file from said computation station.

3. The computer based system of claim 2, further comprising a remote computer in communication with said communication station;

wherein said remote computer transmits patient data to said communication station;

wherein said communication station stores said patient data in said patient data file prior to transmitting said patient data file to said computation station; and

wherein said communication station transmits said result data file received from said computation station to said remote computer.

4. The computer based system of claim 2, further comprising a first remote computer and a second remote computer in communication with said communication station;

wherein said first remote computer transmits patient data to said communication station;

wherein said communication station stores said patient data in said patient data file prior to transmitting said patient data file to said computation station; and

wherein said communication station transmits said result data file received from said computation station to said second remote computer.

5. The computer based system of claim 1, wherein said patient data comprises patient biomarkers and patient demographic data.

6. The computer based system of claim 1, wherein said trained neural network is trained to determine the likelihood of obtaining a disease, screen for the disease, determine the severity of the disease, or diagnose or prognose the disease, wherein the disease is selected from the group consisting of osteoporosis, osteopenia, breast cancer, ovarian cancer, colon cancer, prostate cancer, and testicular cancer.

7. The computer based system of claim 6, wherein the disease is prostate cancer and the biomarkers are patient age, LASA-P®

, PAP, PSA, CK-MB, CK-MM, CK-BB or any combination of the biomarkers.

8. The computer based system of claim 6, wherein the disease is osteoporosis and the biomarkers are patient age, serum calcium, serum phosphate, estradiol, progesterone, ALP, ALP Isoenzyme 1, ALP Isoenzyme 2, or any combination of the biomarkers.

9. The computer based system of claim 6, wherein the disease is ovarian cancer and the biomarkers are patient age, LASA-P®

, CA125, CA12511, DM/70K, MCSF, OVX1, CA7-24, CA19-9 or any combination of the biomarkers.

10. The computer based system of claim 6, wherein the disease is breast cancer and the biomarkers are patient age, LASA-P®

, CEA, CA15-3®

, HER2/neu, or any combination of the biomarkers.

11. The computer based system of claim 6, wherein the disease is testicular cancer and the biomarkers are patient age, LASA-P®

, AFP, HCG-Beta, CA15-3®

or any combination of the biomarkers.

12. The computer based system of claim 6, wherein the disease is colon cancer and the biomarkers are patient age, LASA-POR, CA19.9, CEA or any combination of the biomarkers.

13. The computer based system of claim 1, wherein said computation station comprises more than one trained neural network.

14. The computer based system of claim 1, further comprising an emergent warning procedure for detecting and reporting an abnormal situation in the computer based system.

15. The computer based system of claim 14, wherein the emergent warning procedure reports the abnormal situation through an audible signal.

16. The computer based system of claim 1, further comprising a second trained neural network;

and

wherein said output value produced by said trained neural network is introduced into said second trained neural network, which produces a second output value corresponding to the presence or the absence or the severity of the disease, or the likelihood of obtaining the disease.

17. The computer based system of claim 16, further comprising a communication station in communication with said computation station;

wherein said communication station transmits said patient data file to said computation station for introduction into said trained neural network;

wherein said second output value from said second trained neural network is stored in a result data file; and

wherein said communication station receives said result data file from said computation station.

18. The computer based system of claim 17, further comprising a remote computer in communication with said communication station;

wherein said remote computer transmits patient data to said communication station;

wherein said communication station stores said patient data in said patient data file prior to transmitting said patient data file to said computation station; and

wherein said communication station transmits said result data file received from said computation station to said remote computer.

19. The computer based system of claim 18, further comprising a second remote computer in communication with said communication station;

and

wherein said second remote computer receives said result data file from said computation station.

20. The computer based system of claim 1, wherein the patient biomarkers are demographic data, electrical diagnostic data, imaging diagnostic data, histologic diagnostic data, concentrations of biomarkers in a biological fluid, or a combination of these biomarkers.

21. In a networked computer environment comprising a communication station in communication with a computation station, said computation station comprising a trained neural network, a method for prognosing a disease, diagnosing a disease, determining a severity of a disease, screen for a disease, or a determine a likelihood of obtaining a disease in a patient, comprising the computer implemented steps of:

at said communication station, receiving patient data and storing said patient data in a patient data file;

at said computation station, receiving said patient data file from said communication station and introducing said patient data file into a trained neural network, wherein said trained neural network is trained with a method comprising;

preprocessing patient biomarkers, comprising;

selecting patient biomarkers associated with the disease process;

statistically and/or computationally testing discriminating power of the selected patient biomarkers individually in linear and/or non-linear combination for indicating the presence or the absence or the severity of the disease, or the likelihood of obtaining the disease;

applying statistical, mathematical, or computational tools, and/or expert knowledge for the derivation of secondary inputs 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; and

at said computation station, in response to said trained neural network processing said patient data file, receiving an output value from said trained neural network, said output value corresponding to the presence of the disease, the absence of the disease, the severity of the disease, or the likelihood of obtaining the disease.

22. Computer-readable media having stored thereon computer-executable instructions for performing the method of claim 21.

23. The method of claim 21, further comprising the steps of:

at said computation station, storing said output value in a result data file; and

from said computation station, transmitting said result data file to said communication station.

24. The method of claim 23, wherein said networked computer environment further comprises a remote computer in communication with said communication station;

and

wherein the method further comprises the steps of;

at said communication station, receiving patient data from said remote computer, and

from said communication station, transmitting said result data file received from said computation station to said remote computer.

25. Computer-readable media having stored thereon computer-executable instructions for performing the method of claim 24.

26. The method of claim 24, wherein said networked computer environment further comprises a first remote computer and a second remote computer in communication with said communication station;

and

wherein the method further comprises the steps of;

at said communication station, receiving patient data from said first remote computer, and

from said communication station, transmitting said result data file received from said computation station to said second remote computer.

27. The method of claim 21, wherein said patient data comprises patient biomarkers and patient demographic data.

28. The method of claim 21, wherein said trained neural network is trained to determine the likelihood of obtaining a disease, screen for the disease, determine the severity of the disease, or diagnose or prognose the disease, wherein the disease is selected from the group consisting of osteoporosis, osteopenia, breast cancer, ovarian cancer, colon cancer, prostate cancer, and testicular cancer.

29. The method of claim 28, wherein the disease is prostate cancer and the biomarkers are patient age, LASA-P®

, PAP, PSA, CK-MB, CK-MM, CK-BB or any combination of the biomarkers.

30. The method of claim 28, wherein the disease is osteoporosis and the biomarkers are patient age, serum calcium, serum phosphate, estradiol, progesterone, ALP, ALP Isoenzyme 1, ALP Isoenzyme 2, or any combination of the biomarkers.

31. The method of claim 28, wherein the disease is ovarian cancer and the biomarkers are patient age, LASA-P®

, CA125, CA12511, DM/70K, MCSF, OVX1, CA7-24, CA19-9 or any combination of the biomarkers.

32. The method of claim 28, wherein the disease is breast cancer and the biomarkers are patient age, LASA-P®

, CEA, CA15-3®

, HER2/neu, or any combination of the biomarkers.

33. The method of claim 28, wherein the disease is testicular cancer and the biomarkers are patient age, LASA-P®

, AFP, HCG-Beta, CA15-30 or any combination of the biomarkers.

34. The method of claim 28, wherein the disease is colon cancer and the biomarkers are patient age, LASA-P®

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

35. The method of claim 21, further comprising the steps of:

at said computation station, introducing said output value produced by said trained neural network into a second trained neural network, said second trained neural network producing a second output value corresponding to the presence or the absence or the severity of a disease, or the likelihood of obtaining a disease;

at said computation station, storing said second output value a result data file; and

at said communication station, receiving said result data file from said computation station.

36. Computer-readable media having stored thereon computer-executable instructions for performing the method of claim 35.

37. The method of claim 21, further comprising the steps of detecting an abnormal situation during processing in said computation station;

and reporting the abnormal situation through an audible signal.

38. Computer-readable media having stored thereon computer-executable instructions for performing the method of claim 37.

39. The method of claim 21, 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.