KR20160123151A - System for providing medical information - Google Patents

System for providing medical information Download PDF

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KR20160123151A
KR20160123151A KR1020150053326A KR20150053326A KR20160123151A KR 20160123151 A KR20160123151 A KR 20160123151A KR 1020150053326 A KR1020150053326 A KR 1020150053326A KR 20150053326 A KR20150053326 A KR 20150053326A KR 20160123151 A KR20160123151 A KR 20160123151A
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attribute
unit
case
patient
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김기호
박상찬
우승순
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(주)솔트웍스
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Abstract

Disclosed is a system for providing medical information. The system for providing medical information includes an input part which receives the attribute information of a patient; a case extraction part which applies the received attribute information to an analytic hierarchy process (AHP), adds it to a weighed value for each attribute, compares the attribute information of the patient including the reflected weighted value with stored patient case information, and extracts a similar case; and a case analysis part which analyzes the similar case and provides expected disease information for the patient and optimization test information. So, the system can support clinical decision making.

Description

[0001] SYSTEM FOR PROVIDING MEDICAL INFORMATION [0002]

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to a medical information providing system, and more particularly, to a medical information providing system that provides basic knowledge necessary for diagnosis or treatment of a medical practitioner.

The Clinical Decision Support System is a system that allows the physician to provide the necessary foundation knowledge to determine and judge the diagnosis or treatment policy in the patient's care, . In addition to the subjective judgment of the physician in the examination of the patient by the doctor, the medical guideline is implemented by the computer and the result of the guideline of the patient's condition is informed to the physician to prevent misdiagnosis and enable more objective medical treatment do.

In this regard, Patent Registration No. 10-1066246 relates to a system and method for supporting a clinical decision support system, which is a system that is independent of a hospital information system, and which combines a knowledge- We are building a support system.

SUMMARY OF THE INVENTION The present invention provides a medical information providing system for extracting a case similar to a patient and providing information for supporting clinical decision making.

It is another object of the present invention to provide a medical information providing system capable of providing highly reliable information with an emphasis on an attribute item which is judged to be important by reflecting a weight on various attribute information of a patient.

It is another object of the present invention to provide a medical information providing system capable of providing customized information by giving high weight to a preferred medical care provider.

The present invention also provides a medical information providing system that can provide information that can be intuitively judged by numerically providing an expected prediction value of a disease and a recommended test value.

According to an aspect of the present invention, there is provided an information processing apparatus including an input unit for inputting attribute information of a patient; A case extracting unit for applying a hierarchical analysis method (AHP: Analytic Hierarchy Process) to the input attribute information to assign a weight for each attribute, and comparing the attribute information of the patient with the weighted patient case information and the stored patient case information to extract a similar case; And a case analysis unit for analyzing the similar cases and providing anticipated disease information and optimal test information for the patient.

The attribute information of the patient may be configured to include a human attribute, a clinical attribute, a test attribute, and a disease attribute.

The case extracting unit may include an attribute distance calculating unit for calculating a distance between the attribute information of the patient and the previously stored patient case information; A weight applying unit for assigning a weight for each attribute to the calculated distance between the attributes; A summation unit for summing the distance values of the weighted attributes; And a filtering unit for filtering the sum value by comparing the sum value with a reference value.

Wherein the weight applying unit comprises: a comparing unit for giving preference to the input attribute information through a pair comparison; A standardization unit for performing standardization on preference per property; And a calculation unit for calculating a weight for each attribute using the standard value calculated by the standardization unit.

The comparison unit may perform the pair comparison using the weight of each existing property stored in the storage unit.

The comparing unit may perform the twin comparison using the average value of the existing weight values given by the selected medical staff among the data stored in the storing unit.

The comparison unit may perform the pair comparison using the average value of the existing weight values given by the medical staff having a career or more of the data stored in the database.

The comparison unit may perform the pair comparison using the average value of the existing weight values given by the medical staff performing the tasks related to the disease information shown in the attribute information of the patient among the data stored in the database.

The comparison unit may perform the twin comparison using the average value of the existing weight values given by the medical staff engaged in the selected medical care out of the data stored in the database.

The case analyzer may provide a probability of occurrence of the anticipated disease information according to the sum value.

The case analyzer may provide a recommendation value of the optimal test information with reference to the progress of the similar case with respect to the optimal test information to be provided.

The medical information providing system of the present invention can provide information for supporting clinical decision making by extracting a case similar to a patient.

In addition, reliable information can be provided with an emphasis on the attribute items which are judged to be important by reflecting the weight on the various attribute information of the patient.

In addition, it is possible to give a high weight to a preferred medical care provider, thereby providing customized information.

In addition, it is possible to provide information that can be intuitively judged by providing numerical predictions of expected disease outbreak predictions and recommendation values of recommended tests.

1 is a block diagram of a medical information providing system according to an embodiment of the present invention;
2 is a block diagram of a case extracting unit according to an embodiment of the present invention;
3 is a block diagram of a weight applying unit according to an embodiment of the present invention.
FIG. 4 is a conceptual diagram of attribute information of a patient according to an embodiment of the present invention;
FIG. 5 is a conceptual diagram of patient case information according to an embodiment of the present invention;
FIG. 6 is a conceptual diagram of distances according to an embodiment of the present invention,
FIG. 7 is a conceptual diagram of a similar case extraction process according to an embodiment of the present invention; FIG.
8 is an analysis conceptual diagram of a case analysis unit according to an embodiment of the present invention;
9 is an analysis conceptual diagram of a case analysis unit according to another embodiment of the present invention.

The present invention is capable of various modifications and various embodiments, and specific embodiments are illustrated and described in the drawings. It should be understood, however, that the invention is not intended to be limited to the particular embodiments, but includes all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.

The terms including ordinal, such as second, first, etc., may be used to describe various elements, but the elements are not limited to these terms. The terms are used only for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the second component may be referred to as a first component, and similarly, the first component may also be referred to as a second component. And / or < / RTI > includes any combination of a plurality of related listed items or any of a plurality of related listed items.

It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between.

The terminology used in this application is used only to describe a specific embodiment and is not intended to limit the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In the present application, the terms "comprises" or "having" and the like are used to specify that there is a feature, a number, a step, an operation, an element, a component or a combination thereof described in the specification, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the contextual meaning of the related art and are to be interpreted as either ideal or overly formal in the sense of the present application Do not.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings, wherein like or corresponding elements are denoted by the same reference numerals, and redundant description thereof will be omitted.

1 is a block diagram of a medical information providing system according to an embodiment of the present invention.

Referring to FIG. 1, a medical information providing system 10 according to an embodiment of the present invention includes an input unit 100, a case extracting unit 200, a case analyzing unit 300, a storage unit 400, and an update unit 500 ). ≪ / RTI >

The input unit 100 can receive the attribute information of the patient. Patient attribute information may include human attributes, clinical attributes, test attributes, and disease attributes. Personal attributes may include information about a person's personal information such as gender, age, height, weight, department, country, smoking, drinking, coffee, exercise, sleeping time, etc. required for clinical decision making have. Clinical attributes are information about the tests performed for the diagnosis or treatment of the patient and may include hematological information, serological information, biochemical information, physiological information, bacteriological information, urine and feces information, and image information have. The test attributes may include numerical information about the various tests the patient has received. The disease attribute may include information about the disease the patient has.

The case extracting unit 200 applies a hierarchical analysis method (AHP: Analytic Hierarchy Process) to the input attribute information, assigns weights to the attributes, compares the attribute information of the patient with the weight with the stored case information, Can be extracted.

When the patient's attribute information is input, the case extracting unit 200 compares the attribution information, which is weighted by the attribute, with the patient case information stored in the storage unit 400, and extracts a similar case. The storage unit 400 may store patient information of other medical institutions that have a convention other than the patient information of a specific medical institution, and patient information may be stored in an integrated server operated by a plurality of medical institutions.

FIG. 2 is a block diagram of a case extracting unit according to an embodiment of the present invention.

2, the case extracting unit 200 according to an embodiment of the present invention includes an attribute distance calculating unit 210, a weight applying unit 220, a summing unit 230, and a filtering unit 240 .

The distance calculating unit 210 can calculate the distance between the attribute information of the patient and the attribute of the previously stored patient case information. The distance calculator 210 compares input attribute information of the patient with previously stored patient case information to calculate the distance between the attributes.

The weight applying unit 220 may assign a weight for each attribute to the computed distance between the attributes. The weight means a magnification given to an attribute determined to be important by a medical staff, and a hierarchical analysis method can be applied to input attribute information to assign a weight for each attribute.

3 is a block diagram of a weight applying unit according to an embodiment of the present invention.

Referring to FIG. 3, the weight applying unit 220 according to an embodiment of the present invention may include a comparing unit 221, a normalizing unit 222, and a calculating unit 223.

The comparing unit 221 can give a preference through a pair comparison between inputted attribute information.

The comparator 221 may calculate the comparative advantage between the attribute and the attribute using the preference value for each attribute input at the time of inputting the attribute information, and give preference according to the result of the comparative advantage.

Or the comparing unit 221 may perform the twin comparison using the weight values of the existing attributes stored in the storage unit 400. [ The comparing unit 221 may assign the weights of the attributes stored in the storage unit 400 to the received attribute information, calculate the comparative advantage between the attribute and the attribute, and give preference to each attribute according to the result of the comparative advantage.

Or the comparing unit 221 may perform the pair comparison using the average value of the existing weight values given by the selected medical caregiver among the data stored in the storage unit 400. [ The comparator 221 calculates an average value of the existing weight values given by the medical staff selected by the medical staff in charge and substitutes the input average value into the attribute information inputted to calculate the comparative advantage between the attribute versus the attribute, can do.

Alternatively, the comparing unit 221 may perform the twin comparison using the average value of the existing weight values given by the medical staff having a career history or more, out of the data stored in the storage unit 400. The comparator 221 calculates an average value of existing weight values given by a medical staff having a predetermined career or more and substitutes the calculated average value into the inputted attribute information, calculates the comparative advantage between the attribute versus attributes, and gives preference to each attribute according to the comparative advantage result .

Alternatively, the comparison unit 221 may perform the dual comparison using the average value of the existing weight values given by the medical staff performing the tasks related to the disease information shown in the attribute information of the patient among the data stored in the storage unit 400 . The comparator 221 calculates an average value of existing weight values given by the medical staff performing the tasks related to the disease information shown in the patient attribute information, substitutes the input average value into the attribute information, calculates the comparative advantage between the attribute values Depending on the result of comparative advantage, preference can be given for each attribute.

Alternatively, the comparison unit 221 may perform the twin comparison using the average value of the existing weight values given by the medical staff engaged in the selected medical care out of the data stored in the storage unit 400. The comparator 221 calculates an average value of existing weight values given by the medical staff engaged in the selected department of the medical staff and assigns the average value to the inputted attribute information, calculates the comparative advantage between the attribute and the attribute, It is possible to give a star preference.

The normalization unit 222 may perform standardization of preference by attribute. The normalization unit 222 may calculate the standard value by computing the eigenvector value of the preference value for each attribute and performing standardization on the eigenvector value for each attribute.

The calculating unit 223 can calculate a weight for each attribute by using the standard value calculated by the normalizing unit 222. [

The comparing unit 221 may give preference to the matrix concept of Equation (1), for example.

[Equation 1]

Figure pat00001

Attributes for calculating weights include human attributes, clinical attributes, and disease attributes, and the numbers listed in the matrix represent the preferences among the attributes. The higher the value in the matrix is, the higher the value is compared with the relative attribute, and the smaller the value, the smaller the preference.

 The attribute information for determining the weight can be largely classified into human attribute, clinical attribute, inspection attribute, and disease attribute. In one embodiment of the present invention, the weight calculation process according to the classification will be described. However, it is to be understood that the attribute information is not limited to the above, and that the weights can be calculated on the basis of the detailed attributes belonging to the human attribute, the clinical attribute, the test attribute, and the disease attribute.

As described above, the comparing unit 221 can use weight values of existing attributes stored in the storage unit 400 in assigning the preferences.

Next, the normalization unit 222 computes an eigenvector according to Equation (2) below.

&Quot; (2) "

Figure pat00002

Next, the normalization unit 222 performs standardization according to Equation (3) below and calculates a standard value according to Equation (4).

&Quot; (3) "

Figure pat00003

&Quot; (4) "

Figure pat00004

Next, the calculating unit 223 calculates a weight value for each attribute using the standardized value according to Equation (5).

&Quot; (5) "

Figure pat00005

Figure pat00006

Figure pat00007

The summation unit 230 may sum up the weighted inter-attribute distance values. The total value means a value for judging the similarity between the patient attribute information and the pre-stored patient case information. The smaller the sum, the higher the degree of similarity between the patient's attribute information and the pre-stored patient case information.

The filtering unit 240 may filter the sum value by comparing the sum value with a reference value. The reference value may be changed by an external setting, and the filtering unit 240 filters the sum value having a value larger than the reference value and selects only the sum value having a value smaller than the reference value.

The filtering unit 240 extracts filtered and selected patient case information as similar cases and provides the filtered case information to the case analyzing unit 300 in order of increasing similarity.

The similar case extraction process of the case extracting unit described above can be performed according to the following equation (6).

&Quot; (6) "

Figure pat00008

here,

Figure pat00009
Is the property information of each patient
Figure pat00010
And the attributes of the pre-stored patient case information
Figure pat00011
Quot;
Figure pat00012
Denotes a weight for each attribute,
Figure pat00013
Means each attribute from 1 to n,
Figure pat00014
Means the sum of weights for each property.

therefore

Figure pat00015
The patient's attribute information
Figure pat00016
And pre-stored patient case information
Figure pat00017
, Which indicates the similarity between the patient's attribute information and the pre-stored patient case information.

Referring again to FIG. 1, the case analysis unit 300 may analyze the similar case to provide the expected disease information and the optimal examination information for the patient.

At this time, the case analyzing unit 300 can provide the probability of occurrence of the estimated disease information according to the sum value calculated by the case extracting unit 200. The case analysis unit 300 can substitute the disease information displayed in the similar case and the sum value calculated from the similar case to provide a numerical conversion of the probability that the disease occurring in the similar case may occur in the patient.

 In addition, the case analyzer 300 can provide a recommendation value of the optimal test information with reference to the progress of the similar case with respect to the optimal test information. The case analyzer 300 may provide a recommendation value by converting the suitability of the test to a numerical value using the test information performed in the similar case and the progress in the similar case after the test. The progress of similar cases can be entered by the medical staff in charge after the examination.

The storage unit 400 stores patient case information. In addition to the patient information of the specific medical institution, the storage unit 400 may store patient information of other medical institutions having a convention, and may mean an integrated server operated by a plurality of medical institutions.

In addition, the storage unit 400 stores weight information for each attribute. The storage unit 400 stores the existing weight information generated by the medical staff or calculated by the calculation unit 223, and can refer to the weighting information when the comparison unit 221 calculates the preferences.

The update unit 500 may update the patient case information and the weight information of the storage unit 400. [ The update unit 500 may update the information added or changed in the existing patient case information or the information of the new patient by inputting the information of the new patient and may be updated by the calculating unit 223 And the existing weight information can be updated using the calculated weight information.

4 is a conceptual diagram of attribute information of a patient according to an embodiment of the present invention. In FIG. 4, the sex and age of the patient refers to human attributes, respiration and pulse to clinical attributes, test 1 to test attributes, and disease 1 to disease attributes. Various attribute information can be input in addition to the attribute information shown in FIG.

5 is a conceptual diagram of patient case information according to an embodiment of the present invention. In Figure 5, patient case information was set as 100 samples. Case 1 to 100 indicate a serial number for patient case information, and the order is random.

FIG. 6 is a conceptual diagram of distances according to an embodiment of the present invention. In FIG. 6, the distance between the respective attributes means a distance value before the weight is given. In the case of sex and disease, '1' is indicated for matching, and '0' is indicated for non-matching.

7 is a conceptual diagram of a similar case extraction process according to an embodiment of the present invention. In FIG. 7, the order of similarity is ranked from the lowest value to the lowest value, and caes2 and case99 are selected as similar cases according to the reference value (0.100).

8 is an analysis conceptual diagram of a case analysis unit according to an embodiment of the present invention. In FIG. 8, the case analyzer provides the estimated disease information for the patient using the selected similar case, and provides the probability of the expected disease information together with the sum of the similar case and the similar case.

9 is an analysis conceptual diagram of a case analysis unit according to another embodiment of the present invention. In FIG. 9, the case analyzer provides the optimal test information using the selected similar case, and provides the recommended value of the optimal test information with reference to the progress of the similar case.

As used in this embodiment, the term " portion " refers to a hardware component such as software or an FPGA (field-programmable gate array) or ASIC, and 'part' performs certain roles. However, 'part' is not meant to be limited to software or hardware. &Quot; to " may be configured to reside on an addressable storage medium and may be configured to play one or more processors. Thus, by way of example, 'parts' may refer to components such as software components, object-oriented software components, class components and task components, and processes, functions, , Subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. The functions provided in the components and components may be further combined with a smaller number of components and components or further components and components. In addition, the components and components may be implemented to play back one or more CPUs in a device or a secure multimedia card.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the present invention as defined by the following claims It can be understood that

100: Input unit
200: Case extraction unit
210: Property distance calculating unit
220: weight application unit
230: summing unit
240:
300: Case Analysis Department
400:
500: Update section

Claims (11)

An input unit for receiving property information of a patient;
A case extracting unit for applying a hierarchical analysis method (AHP: Analytic Hierarchy Process) to the input attribute information to assign a weight for each attribute, and comparing the attribute information of the patient with the weighted patient case information and the stored patient case information to extract a similar case; And
And a case analysis unit for analyzing the similar case to provide predicted disease information and optimal examination information for the patient.
The method according to claim 1,
Wherein the attribute information of the patient includes a human attribute, a clinical attribute, a test attribute, and a disease attribute.
The apparatus of claim 1, wherein the case extracting unit
Wherein the case extracting unit comprises: an attribute distance calculating unit for calculating a distance between the attribute information of the patient and the attribute of the previously stored patient case information;
A weight applying unit for assigning a weight for each attribute to the calculated distance between the attributes;
A summation unit for summing the distance values of the weighted attributes; And
And a filtering unit that compares the sum value with a reference value and filters the combined value.
4. The apparatus of claim 3, wherein the weight applying unit
A comparing unit for giving a preference through a pair comparison between inputted attribute information;
A standardization unit for performing standardization on preference per property; And
And a calculation unit for calculating a weight for each attribute using the standard value calculated by the standardization unit.
5. The apparatus of claim 4, wherein the comparing unit
And performing pair comparison using weight values of existing attributes stored in the storage unit.
6. The apparatus of claim 5, wherein the comparing unit
And performs a pair comparison using an average value of existing weight values given by a selected medical caregiver among the data stored in the storage unit.
6. The apparatus of claim 5, wherein the comparing unit
And performing a pair comparison using an average value of existing weight values given by a medical staff having a career or more of the data stored in the database.
6. The apparatus of claim 5, wherein the comparing unit
And performing a pair comparison using an average value of existing weight values given by a medical staff performing a task related to disease information shown in the attribute information of the patient among the data stored in the database.
6. The apparatus of claim 5, wherein the comparing unit
And performs a pair comparison using an average value of existing weight values given by a medical staff engaged in a selected medical care department among data stored in the database.
The method of claim 3,
And the case analyzer provides probability of occurrence of the predicted disease information according to the sum value.
The method of claim 3,
Wherein the case analyzer provides a recommendation value of the optimal test information with reference to progress of the similar case with respect to the optimal test information to be provided.
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Cited By (3)

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KR101962105B1 (en) * 2017-10-13 2019-03-26 주식회사 유비케어 Device and method for recording medical charts
CN111489061A (en) * 2020-03-23 2020-08-04 天津大学 Interactive control method for improving safety of automobile product based on virtual reality
KR20230156245A (en) * 2022-05-04 2023-11-14 주식회사 타이로스코프 A method of prescribing aid providing test set by using interview content

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JP2002063278A (en) * 2000-08-22 2002-02-28 Shotaro Katsuki Health degree evaluation system, its recording medium and health degree evaluating method
KR100794516B1 (en) * 2007-12-03 2008-01-14 한국정보통신대학교 산학협력단 System and method for diagnosis and clinical test selection using case based machine learning inference
JP6316546B2 (en) * 2013-06-05 2018-04-25 キヤノンメディカルシステムズ株式会社 Treatment plan formulation support device and treatment plan formulation support system

Cited By (3)

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Publication number Priority date Publication date Assignee Title
KR101962105B1 (en) * 2017-10-13 2019-03-26 주식회사 유비케어 Device and method for recording medical charts
CN111489061A (en) * 2020-03-23 2020-08-04 天津大学 Interactive control method for improving safety of automobile product based on virtual reality
KR20230156245A (en) * 2022-05-04 2023-11-14 주식회사 타이로스코프 A method of prescribing aid providing test set by using interview content

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