MEDICAL DIAGNOSIS DERIVED FROM PATIENT DRUG HISTORY DATA

• US 20130226618A1
• Filed: 04/01/2013
• Published: 08/29/2013
• Est. Priority Date: 09/19/2006
• Status: Active Grant

• Current Assignee: 3M Innovative Properties Company (3M Company)
• Original Assignee: 3M Innovative Properties Company (3M Company)
• Inventors: Eisenhandler, Jon, Averill, Richard F., Goldfield, Norbert I.
• Granted Patent: US 8,814,790 B2
• Time in Patent Office: Days
• US Class Current: 705/3
• CPC Class Codes: A61B 5/00 Measuring for diagnostic purposes radiation diagnosis A61B6/00; diagnosis by ultrasonic, sonic or infrasonic waves A61B8/00; Identification of persons
• CPC Class Codes: A61B 5/72 Signal processing specially adapted for physiological signals or for diagnostic purposes character recognition, recognising digital ink, document-oriented image-based pattern recognition G06V30/00; recognition or understanding of postal images G06V30/424
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• 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: Y10S 128/92 Computer assisted medical diagnostics
• CPC Class Codes: Y10S 707/99941 Database schema or data structure

Abstract

In general the invention is directed to systems, methods, and/or software modules which may analyze a patient'"'"'s drug history, and optionally other data, and derive a probable diagnosis for the patient based on rules.

Claims

1. A computer-based system having a memory and a processor which estimates a patient'"'"'s future demand for healthcare services, comprising a software module running on a computer which analyzes a patient'"'"'s drug history sex, and age, and based on this analysis infers a diagnosis for the patient;

wherein the analysis done by the software modules comprises applying at least one rule which defines a relationship among a disease, drug usage expected to be associated with the disease, the sex of a person expected to be associated with the disease, and an age range of a person expected to be associated with the disease; and

,

wherein the at least one rule is applied to the patient'"'"'s drug history based on a hierarchy of diseases, the hierarchy being a function, at least in part, of the severity of the disease to the patient'"'"'s health, and further wherein the hierarchy of diseases is applied such that if the application of the rules yields a plurality of possible diseases, the most severe disease is associated with the patient.

2. The computer-based system of claim 1, wherein the patient'"'"'s drug history includes information additionally defining time periods in which a drug was taken or received by a patient.

3. The computer-based system of claim 1, wherein the applied rule includes a span rule, which defines a period of time for which the patient must have been taking a drug for a disease to be associated with the patient.

4. The computer-based system of claim 3, wherein the span rule includes gap rules, which define gaps in the period of time for which a patient must have been taking a drug for a disease to be associated with the patient.

5. The computer-based system of claim 1, wherein the computer further analyzes at least a portion of the patient'"'"'s genetic data, and wherein the applied rule includes a genetic rule that specifies a genetic feature of the patient that must be present for the disease to be associated with the patient.

6. The computer-based system of claim 5, wherein the genetic rule is indicative of a predisposition to the disease.

7. The computer-based system of claim 5, wherein the genetic rule is indicative of a predisposition to the disease only when a drug usage is present in a person'"'"'s drug history.

8. The computer-based system of claim 1, wherein associating the most severe disease with the patient comprises:

assigning the disease to the patient based on selecting the highest diagnosis in the hierarchy that is consistent with the applied rule.

9. The computer-based system of claim 1, wherein the patient'"'"'s drug history identifies more than one drug taken by the patient, and wherein applying the rule further comprises applying the drug diagnosis rule to the second drug taken by the patient.

10. A computer-implemented method, the method implemented on a computer having at least one processor and a memory, comprising:

receiving patient data comprising the patient'"'"'s drug history, sex, and age;

analyzing, using the at least one processor of the computer, the patient data, wherein the analysis comprises applying at least one rule which defines a relationship among a disease, drug usage expected to be associated with the disease, the sex of a person expected to be associated with the disease, and an age range of a person expected to be associated with the disease, and further wherein the at least one rule is applied to the patient'"'"'s drug history based on a hierarchy of diseases, the hierarchy being a function, at least in part, of the severity of the disease to the patient'"'"'s health, and further wherein the hierarchy of diseases is applied such that if the application of the rules yields a plurality of possible diseases, the most severe disease is associated with the patient; and

,

based on the analysis, inferring a diagnosis for the patient.

11. The computer-implemented method of claim 10, wherein the patient data additionally includes information additionally defining time periods in which a drug was taken or received by a patient.

12. The computer-implemented method of claim 10, wherein the applied rule includes a span rule, which defines a period of time for which the patient must have been taking a drug for a disease to be associated with the patient.

13. The computer-implemented method of claim 12, wherein the span rule includes gap rules, which define gaps in the period of time for which a patient must have been taking a drug for a disease to be associated with the patient.

14. The computer-implemented method of claim 10, wherein the computer further analyzes at least a portion of the patient'"'"'s genetic data, and wherein the applied rule includes a genetic rule that specifies a genetic feature of the patient that must be present for the disease to be associated with the patient.

15. The computer-implemented method of claim 14, wherein the genetic rule is indicative of a predisposition to the disease.

16. The computer-implemented method of claim 14, wherein the genetic rule is indicative of a predisposition to the disease only when a drug usage is present in a person'"'"'s drug history.

17. The computer-implemented method of claim 10, wherein associating the most severe disease with the patient comprises:

assigning the disease to the patient based on selecting the highest diagnosis in the hierarchy that is consistent with the applied rule.

18. The computer-implemented method of claim 10, wherein the patient'"'"'s drug history identifies more than one drug taken by the patient, and wherein applying the rule further comprises applying the drug diagnosis rule to the second drug taken by the patient.