HEALTH-RISK METRIC DETERMINATION AND/OR PRESENTATION

• US 20110173027A1
• Filed: 10/06/2009
• Published: 07/14/2011
• Est. Priority Date: 10/10/2008
• Status: Abandoned Application

• Current Assignee: Koninklijke Philips N.V.
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Olszewski, Mark E., Walker, Matthew J.
• Application Number:
  US13/120,664
• Publication Number:
  US 20110173027A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: A61B 6/508 for non-human patients
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• 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 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A synthesizer that determines health-risk metrics includes a metric determiner that generates a first health-risk metric based on health related information, wherein the first health-risk metric indicates a first health state of a first local region of interest of a subject.

Claims

2. (canceled)

14. A method, comprising:

obtaining information indicative of a health state of a subject;

synthesizing at least a sub-set of the information;

generating at least one health-risk metric for the subject based on the synthesis; and

presenting the at least one health-risk metric.

15. The method of claim 14, further including dynamically updating the at least one health-risk metric in response to obtaining additional information.

16. The method of claim 14, further including at least one of screening, diagnosing, treatment planning, treating, or post-treatment monitoring of the subject based on the at least one health-risk metric.

17. The method of claim 14, wherein the at least one health-risk metric is local to a sub-region of tissue of interest of the subject.

18. The method of claim 14, wherein the at least one health-risk metric is global to the subject.

20. The method of claim 14, further including presenting the at least one health-risk metric via at least one of a visual, an audible, or a tactile presentation.

22. The method of claim 14, further including generating the at least one health-risk metric using information about a pharmaceutical and predicting an effectiveness of the pharmaceutical for the subject based on the at least one health-risk metric.

24. The method of claim 14, further including generating the at least one health-risk metric using information about an implant and predicting an effectiveness of the implant for the subject based on the at least one health-risk metric.

25. The method of claim 24, further including monitoring an effect of the implant implanted in the subject based on a health-risk metric determined after the implantation of the implant in the subject with information available after the implantation.

26. The method of claim 14, further including:

generating at least one second health-risk metric for the subject at a different moment in time; and

presenting a difference between the at least one metric and the at least one second health-risk metric.

28. The method of claim 26, further including:

comparing the first and second health-risk metrics; and

predicting whether the pharmaceutical will increase or decrease the health-risk metric.

19. (canceled)

21-1. The method of claim 21, wherein the mapping includes mapping indicia that shows a relative degree of risk associated with the at least one health-risk metric.

27. A method of predicting an effectiveness of a pharmaceutical, comprising:

generating a first health-risk metric for a subject based on information about a health state of a subject;

generating a second health-risk metric for the subject based on information about a health state of a subject and a known health related affect of the pharmaceutical; and

predicting the effectiveness of the pharmaceutical based on the first and second health-risk metrics.

29. A method of predicting an effectiveness of an implant, comprising:

generating a first health-risk metric for a subject based on information about a health state of a subject;

generating a second health-risk metric for the subject based on information about a health state of a subject and a known health related affect of the implant; and

predicting the effectiveness of the implant based on the first and second health-risk metrics.

30. The method of claim 29, further including:

comparing the first and second health-risk metrics; and

predicting whether the implant will increase or decrease the health-risk metric.

31. A method for selecting a treatment for a patient, comprising:

simulating a plurality of health-risk metrics for a subject, wherein each metric is based on information corresponding to a different treatment; and

selecting a treatment for the subject based on the simulated plurality of health-risk metrics.

32. A method, comprising:

determining a health-risk metric for a local region of a subject; and

using the health-risk metric to automatically guide an instrument to the local region of the subject for a procedure.