SIGNIFICANCE PARAMETER EXTRACTION METHOD AND ITS CLINICAL DECISION SUPPORT SYSTEM FOR DIFFERENTIAL DIAGNOSIS OF ABDOMINAL DISEASES BASED ON ENTROPY ROUGH APPROXIMATION TECHNOLOGY

• US 20130226611A1
• Filed: 09/23/2011
• Published: 08/29/2013
• Est. Priority Date: 03/22/2011
• Status: Abandoned Application

• Current Assignee: Keimyung University
• Original Assignee: Keimyung University
• Inventors: Kim, Yoon Nyun, Son, Chang Sik, Park, Hee Joon, Seo, Suk Tae, Kim, Min Soo
• Application Number:
  US13/882,500
• Publication Number:
  US 20130226611A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• 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

A significance parameter extraction method for differential diagnosis of abnormal diseases based on entropy rough approximation technology, including the steps of: (a) calculating clinical reference values from two different groups of clinical data extracted from a database storing a plurality of clinical data for each check item using an entropy maximization measure; (b) evaluating a clinical difference between the two different groups of clinical data and extracting candidate check items; (c) based on a reference value of a check item calculated from one of the groups of clinical data, converting attribute values of the check item into nominal attribute values; and (d) extracting significance parameters for differential diagnosis from the candidate check items extracted in the step (b).

Claims

1. A significance parameter extraction method for differential diagnosis of abnormal diseases based on entropy rough approximation technology, comprising the steps of:

(a) calculating clinical reference values from two different groups of clinical data extracted from a database storing a plurality of clinical data for each check item using an entropy maximization measure;

(b) evaluating a clinical difference between the two different groups of clinical data and extracting candidate check items;

(c) based on a reference value of a check item calculated from one of the groups of clinical data, converting attribute values of the check item into nominal attribute values; and

(d) extracting significance parameters for differential diagnosis from the candidate check items extracted in the step (b).

2. The significance parameter extraction method according to claim 1, wherein the two different groups of clinical data include:

a group having one disease and a group having another disease;

or

a group having one disease and a group having other diseases.

3. The significance parameter extraction method according to claim 1, wherein the entropy maximization measure is calculated by:

4. The significance parameter extraction method according to claim 1, wherein the step (b) includes:

in case of a single reference value, extracting cases where reference values of the two different groups of clinical data for one check item are different, as candidate check items; and

in case of two reference values, extracting cases where one range of reference values is not included in another range of reference values, as candidate check items.

5. The significance parameter extraction method according to claim 1, wherein the step (c) includes:

in case of a single reference value, converting values of check items of two regions into nominal values based on the single reference value; and

in case of two reference values, converting values of check items of three regions into nominal values based on the two reference values.

6. The significance parameter extraction method according to claim 1, wherein the step (d) includes the steps of:

generating a decision table to be converted into the extracted candidate check items and the nominal values for each check item;

generating a discernibility matrix based on the decision table; and

extracting significance parameters for differential diagnosis by calculating a discernibility function from the discernibility matrix.

7. The significance parameter extraction method according to claim 6, wherein the discernibility matrix is generated by:

(c_ij)=aε

A;

a(x_i)≠

a(x_j),∃

i,j, for d_i≠

_j

where, A means the total set of input variables representing check items, and a means any element in the total set of input variables, x_i represents an i-th case, d_i represents an i-th output attribute value indicating a disease, c_ij means input variables having a difference in attribute value between two different cases, and N represents the total number of cases.

8. The significance parameter extraction method according to claim 7, wherein the discernibility function is expressed by:

9. The significance parameter extraction method according to claim 7, wherein at least one nominal value in the decision table is null, and unknown values can have all corresponding values.

10. An integrated clinical decision support system comprising:

a clinical information database including clinical data for each of a plurality of check items;

a database which stores disease information defined by clinical specialists from the clinical data;

a clinical decision support module which uses a method according to claim 1;

a knowledge database which stores temporary knowledge generated from the clinical decision support module, including clinical decision support information; and

an application interface module which acquires clinical decision support synthetic information generated through the knowledge database.

11. The integrated clinical decision support system according to claim 10, further comprising a core knowledge repository database which stores the information generated in the clinical decision support module and core knowledge obtained based on clinical information decided by clinical specialists.

12. The integrated clinical decision support system according to claim 10, wherein the clinical decision support module includes a significance parameter extraction module using a method according to claim 1, and a clinical decision model design module.

13. The integrated clinical decision support system according to claim 12, wherein the clinical decision model design module is designed to have a tree structure with application of all check items, which are determined by one reference value or two reference values applied to the significance parameter extraction method, to N groups of experiments and controls data collected by N random samplings from the clinical information database.