Method, system and computer product for prognosis of a medical disorder

• US 20040242972A1
• Filed: 05/28/2003
• Published: 12/02/2004
• Est. Priority Date: 05/28/2003
• Status: Abandoned Application

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Gorman, William Phillip, Adak, Sudeshna, Illouz, Kati
• Application Number:
  US10/446,494
• Publication Number:
  US 20040242972A1
• Time in Patent Office: Days
• US Class Current: 600/300
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Method, system and computer product for prognosis of a medical disorder. In one embodiment, a request to provide prognosis decision support for the patient is received. Medical data relevant to the patient such as longitudinal medical data is extracted. Predictive modeling techniques relevant to the patient from the longitudinal medical data are derived. The predictive modeling techniques are then used to predict a clinical outcome for the patient from the medical data. The predictive modeling techniques are formulated using data mining techniques that are capable of detecting correlations in repeated measurements associated with the longitudinal medical data. The predictive modeling techniques then utilize the correlations for determining the medical prognosis for the patient.

Claims

1. A method for determining a medical prognosis of a patient with a medical disorder, comprising:

receiving a request to provide prognostic decision support for the patient;

extracting a plurality of medical data relevant to the patient, wherein the plurality of medical data comprises a plurality of longitudinal medical data; and

using a plurality of predictive modeling techniques to predict at least one clinical outcome for the patient from the plurality of medical data, wherein the plurality of predictive modeling techniques are formulated using a plurality of data mining techniques that are capable of detecting correlations in repeated measurements associated with the plurality of longitudinal medical data and wherein the plurality of predictive modeling techniques utilize the correlations for determining the medical prognosis for the patient.

2. The method of claim 1, wherein the medical disorder comprises at least one of neurodegenerative disorders, cardiovascular disorders and cancer.

3. The method of claim 1, wherein the predictive modeling techniques comprise using a regression tree for longitudinal medical data with a plurality of rules to predict the clinical outcome.

4. The method of claim 1, wherein the predictive modeling techniques comprise using a neural network technique for longitudinal medical data that models a relationship between the plurality of longitudinal medical data and the clinical outcome.

5. The method of claim 1, further comprises categorizing the patient based on a degree of risk associated with the predicted clinical outcome, wherein the categorization identifies the patient as a high-risk, medium-risk or a low-risk patient.

6. The method of claim 5, wherein the degree of risk corresponds to a rate of decline in the patient'"'"'s condition based on the predicted clinical outcome.

7. The method of claim 1, further comprises displaying a plurality of outputs related to the predicted clinical outcome for the patient.

8. The method of claim 7, further comprising tracking and analyzing a trend in the predicted clinical outcome, wherein the trend represents a possible future course of the predicted clinical outcome.

9. The method of claim 8, wherein the tracking further comprises assisting in healthcare decision making and medical treatment planning.

10. The method of claim 7, wherein the plurality of outputs comprise displaying a time of occurrence of the predicted clinical outcome.

11. The method of claim 7, wherein the plurality of outputs comprise displaying a confidence measure for the predicted clinical outcome, wherein the confidence measure represents a degree of accuracy associated with the predicted clinical outcome.

12. The method of claim 1, further comprises validating the predicted clinical outcome over time.

13. The method of claim 1, further comprises acquiring new patient data for patient prognosis.

14. The method of claim 1, further comprises generating a plurality of clinical recommendations for a plurality of patients by identifying and comparing patients exhibiting similar prognosis.

15. A medical decision support system for prognosis of a medical disorder, comprising:

a data storage component configured to store a plurality of medical patient data comprising longitudinal medical data; and

a prediction engine component coupled to the data storage component comprising a plurality of predictive modeling tools that predict at least one clinical outcome for a patient, wherein the plurality of predictive modeling tools are formulated using a plurality of data mining tools that are capable of detecting correlations in repeated measurements associated with the plurality of longitudinal medical data and wherein the plurality of predictive modeling tools utilize the correlations for determining the medical prognosis for the patient

16. The system of claim 15, wherein the medical disorder comprises at least one of neurodegenerative disorders, AIDS, cardiovascular disorders and cancer.

17. The system of claim 15, wherein the plurality of predictive modeling tools comprise a regression tree technique for longitudinal medical data that uses a plurality of rules to predict the clinical outcome.

18. The system of claim 15, wherein the plurality of predictive modeling tools comprise a neural network technique for longitudinal medical data that models a relationship between the plurality of longitudinal medical data and the clinical outcome.

19. The system of claim 15, wherein the prediction engine component is further configured to categorize a patient based on a degree of risk associated with the predicted clinical outcome, wherein the categorization identifies the patient as a high-risk, a medium-risk or a low-risk patient.

20. The system of claim 19, wherein the degree of risk corresponds to a rate of decline in the patient'"'"'s condition based on the predicted clinical outcome.

21. The system of claim 15, wherein the prediction engine component comprises a confidence interval subcomponent configured to compute a confidence measure for the predicted clinical outcome that represents a degree of accuracy associated with the predicted clinical outcome.

22. The system of claim 15, wherein the prediction engine component comprises a prognosis display subcomponent configured to track and analyze a trend in the clinical outcome, wherein the trend represents a possible future course of the predicted clinical outcome.

23. The system of claim 22, wherein the prognosis display subcomponent is further configured to display the time of occurrence of the predicted clinical outcome.

24. The system of claim 22, wherein the prognosis display subcomponent is further configured to display a confidence measure associated with the predicted clinical outcome.

25. The system of claim 22, wherein the prognosis display subcomponent is further configured to provide assistance in healthcare decision making and medical treatment planning.

26. The system of claim 15, wherein the prediction engine component comprises a prognosis similarity search subcomponent configured to generate a plurality of clinical recommendations for a plurality of patients exhibiting similar prognosis.

27. The system of claim 15, further comprises a monitoring and validation component coupled to the prediction engine component and configured to monitor and validate the predicted clinical outcome over time.

28. The system of claim 15, further comprises a data acquisition component coupled to the prediction engine component and configured to acquire new patient data for patient prognosis.

29. A computer-readable medium storing computer instructions for instructing a computer system to determine a medical prognosis of a patient with a medical disorder, the computer instructions comprising:

receiving a request to provide prognostic decision support for the patient;

extracting a plurality of medical data relevant to the patient;

wherein the plurality of medical data comprises a plurality of longitudinal medical data; and

using a plurality of predictive modeling techniques to predict at least one clinical outcome for the patient from the plurality of medical data, wherein the plurality of predictive modeling techniques are formulated using a plurality of data mining techniques that are capable of detecting correlations in repeated measurements associated with the plurality of longitudinal medical data and wherein the plurality of predictive modeling techniques utilize the correlations for determining the medical prognosis for the patient

30. The computer-readable medium of claim 29, wherein the medical disorder comprises at least one of neurodegenerative disorders, AIDS, cardiovascular disorders and cancer.

31. The computer-readable medium of claim 29, wherein the predictive modeling techniques comprise processing instructions for using a regression tree for longitudinal medical data with a plurality of rules to predict the clinical outcome.

32. The computer-readable medium of claim 29, wherein the predictive modeling techniques comprise processing instructions for using a neural network technique for longitudinal medical data that models a relationship between the plurality of longitudinal medical data and the clinical outcome.

33. The computer-readable medium of claim 29, further comprises instructions for categorizing the patient based on a degree of risk associated with the predicted clinical outcome, wherein the categorization identifies the patient as a high-risk, medium-risk or a low-risk patient.

34. The computer-readable medium of claim 33, wherein the degree of risk corresponds to a rate of decline in the patient'"'"'s condition based on the predicted clinical outcome.

35. The computer-readable medium of claim 29, further comprises instructions for displaying a plurality of outputs related to the predicted clinical outcome for the patient.

36. The computer-readable medium of claim 35, further comprising instructions for tracking and analyzing a trend in the predicted clinical outcome, wherein the trend represents a possible future course of the predicted clinical outcome.

37. The computer-readable medium of claim 36, wherein the tracking further comprises instructions for assisting in healthcare decision making and medical treatment planning.

38. The computer-readable medium of claim 35, wherein the plurality of outputs comprise instructions for displaying a time of occurrence of the predicted clinical outcome.

39. The computer-readable medium of claim 35, wherein the plurality of outputs comprise instructions for displaying a confidence measure for the predicted clinical outcome, wherein the confidence measure represents a degree of accuracy associated with the predicted clinical outcome.

40. The computer-readable medium of claim 29, further comprises instructions for validating the predicted clinical outcome over time.

41. The computer-readable medium of claim 29, further comprises instructions for acquiring new patient data for patient prognosis.

42. The computer-readable medium of claim 29, further comprises instructions for generating a plurality of clinical recommendations for a plurality of patients by identifying and comparing patients exhibiting similar prognosis.

43. A method for determining a medical prognosis of a patient with a medical disorder, comprising:

extracting a plurality of medical data relevant to the patient, wherein the plurality of medical data comprises a plurality of longitudinal medical data;

using a plurality of predictive modeling techniques to predict at least one clinical outcome for the patient from the plurality of medical data, wherein the plurality of predictive modeling techniques are formulated using a plurality of data mining techniques that are capable of detecting correlations in repeated measurements associated with the plurality of longitudinal medical data and wherein the plurality of predictive modeling techniques utilize the correlations for determining the medical prognosis for the patient

44. The method of claim 43, wherein the predictive modeling techniques comprise using a regression tree for longitudinal medical data with a plurality of rules to predict the clinical outcome.

45. The method of claim 43, wherein the predictive modeling techniques comprise using a neural network technique for longitudinal medical data that models a relationship between the plurality of longitudinal medical data and the clinical outcome.

46. A computer-readable medium storing computer instructions for instructing a computer system to determine a medical prognosis of a patient with a medical disorder, comprising:

extracting a plurality of medical data relevant to the patient, wherein the plurality of medical data comprises a plurality of longitudinal medical data;

using a plurality of predictive modeling techniques to predict at least one clinical outcome for the patient from the plurality of medical data, wherein the plurality of predictive modeling techniques are formulated using a plurality of data mining techniques that are capable of detecting correlations in repeated measurements associated with the plurality of longitudinal medical data and wherein the plurality of predictive modeling techniques utilize the correlations for determining the medical prognosis for the patient.

47. The computer-readable medium of claim 46, wherein the predictive modeling techniques comprise processing instructions for using a regression tree for longitudinal medical data with a plurality of rules to predict the clinical outcome.

48. The method of claim 46, wherein the predictive modeling techniques comprise processing instructions for using a neural network technique for longitudinal medical data that models a relationship between the plurality of longitudinal medical data and the clinical outcome.