CN111430036B - Medical information identification method and device for abnormal operation behaviors - Google Patents
Medical information identification method and device for abnormal operation behaviors Download PDFInfo
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Abstract
The invention discloses a medical information identification method and device for abnormal operation behaviors, and belongs to the field of medical information processing. According to the medical information identification method and device for abnormal operation behaviors, the medical list comprising the medical information of the patient is generated according to the medical settlement data, the medical list is processed according to the target operation type by adopting the correlation analysis algorithm, the frequent item set of the medical information with high frequency is obtained from the medical list, and the abnormal operation corresponding to the target operation type can be screened out from the medical list by utilizing the frequent item set, so that the purpose of accurately identifying the common operation behaviors caused by operation counterfeiting is realized, and the identification accuracy is high.
Description
Technical Field
The invention relates to the field of medical information processing, in particular to a medical information identification method and device for abnormal operation behaviors.
Background
With the rapid increase in the size and medical costs of national basic medical insurance funds, there are few fraud in cheating the abnormal behavior of medical insurance funds. The abnormal behavior means that: fraudulent fraud corresponding to normal behavior. For example: abnormal behaviors of examination and medicine use, standard hospitalization behaviors reduction, operation counterfeiting behaviors and the like.
The inventor of the invention finds in research that the existing medical insurance fund supervision method mainly analyzes all medical information of a patient, combines medical knowledge of professional medical personnel to identify cheating and insurance behaviors, and does not specially analyze operation counterfeiting behaviors. Because the abnormal behaviors of the patient have the characteristics of multiformity, variability and the like, the conventional medical insurance fund supervision method cannot accurately identify the abnormal behaviors, particularly the behaviors caused by surgery counterfeiting.
Disclosure of Invention
Aiming at the problem that the existing medical insurance fund supervision method cannot accurately identify operation counterfeiting, the medical information identification method and the medical information identification device which aim at accurately identifying abnormal operation behaviors of the operation counterfeiting are provided.
The invention provides a medical information identification method for abnormal operation behaviors, which comprises the following steps:
acquiring medical settlement data of patient objects in a medical database;
generating a medical list according to the medical settlement data, wherein the medical list comprises personal information, operation types and medical information of each patient object;
processing the medical information in the medical list by adopting an association analysis algorithm according to a target operation type to obtain a frequent item set associated with the target operation type;
and inquiring the medical list according to the target operation type and the frequent item set to obtain the patient object of the abnormal operation.
Preferably, the generating a medical list according to the medical settlement data includes:
and extracting the operation type, the medicine information and the diagnosis and treatment information of each patient object in the medical settlement data according to a preset form template to generate the medical list.
Preferably, the processing the medical information in the medical list by using an association analysis algorithm according to the target surgery type to obtain a frequent item set associated with the target surgery type includes:
extracting personal information and medical information of patient objects associated with the target surgery type in the medical manifest;
analyzing the medical information of all the patient objects by adopting the correlation analysis algorithm, and acquiring the support degree of the medical information of each patient object;
and comparing the support degree of the medical information with a first medical threshold value, acquiring the medical information with the support degree larger than the first medical threshold value, and generating the frequent item set.
Preferably, the querying the medical list according to the target operation type and the frequent item set to obtain the patient subject of the abnormal operation includes:
acquiring medical information of patient objects matched with the target operation type in the medical list item by item, and matching the frequent item set associated with the target operation type with the medical information;
if the medical information does not match the frequent itemset, carrying out abnormal identification on the patient object;
if the medical information is matched with the frequent item set, judging whether the medical information meets a preset condition, and if not, performing abnormal identification on the patient object; the preset condition is a preset range of the amount of the medical information.
Preferably, the medical checklist includes hospital information for each patient subject;
processing the medical information in the medical list by adopting an association analysis algorithm according to the target operation type to acquire a frequent item set associated with the target operation type, wherein the method comprises the following steps of:
extracting personal information, medical information and hospital information of the patient object associated with the target surgery type in the medical list;
analyzing the medical information of the corresponding patient object by adopting the correlation analysis algorithm according to the target hospital information to obtain the support degree of the medical information of each device object;
and comparing the support degree of the medical information with a second medical threshold value, acquiring the medical information with the support degree larger than the second medical threshold value, and generating a frequent item set associated with the target operation type according to the medical information.
Preferably, the querying the medical list according to the target operation type and the frequent item set to obtain the patient subject of the abnormal operation includes:
acquiring medical information of patient objects matched with the target operation type and the target hospital information in the medical list item by item, and matching the frequent item set associated with the target operation type with the medical information;
if the medical information does not match the frequent itemset, carrying out abnormal identification on the patient object;
if the medical information is matched with the frequent item set, judging whether the medical information meets a preset condition, and if not, performing abnormal identification on the patient object; wherein the preset condition is a preset range of the amount of the medical information.
Preferably, the medical information includes drug information and diagnosis and treatment information.
The invention also provides a medical information recognition device for abnormal operation behaviors, which comprises:
an acquisition unit for acquiring medical settlement data of a patient object in a medical database;
the generation unit is used for generating a medical list according to the medical settlement data, wherein the medical list comprises personal information, operation types and medical information of each patient object;
the analysis unit is used for processing the medical information in the medical list by adopting an association analysis algorithm according to a target operation type and acquiring a frequent item set associated with the target operation type;
and the identification unit is used for inquiring the medical list according to the target operation type and the frequent item set and acquiring the patient object of the abnormal operation.
The invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method when executing the computer program.
The present invention also provides a computer-readable storage medium having stored thereon a computer program characterized in that: which when executed by a processor implements the steps of the above-described method.
According to the medical information identification method and device for abnormal operation behaviors, the medical list comprising the medical information of the patient is generated according to the medical settlement data, the medical list is processed according to the target operation type by adopting the correlation analysis algorithm, the frequent item set of the medical information with high frequency is obtained from the medical list, and the abnormal operation corresponding to the target operation type can be screened out from the medical list by utilizing the frequent item set, so that the purpose of accurately identifying the normal operation behaviors caused by operation counterfeiting is realized, and the identification accuracy is high.
Drawings
Fig. 1 is a flowchart of an embodiment of a medical information identification method for abnormal surgical actions according to the present invention;
FIG. 2 is a flowchart of one embodiment of the present invention for querying a medical checklist for abnormal surgical patient objects based on a target surgical type and a frequent itemset;
FIG. 3 is a flow chart of another embodiment of the present invention for querying a medical checklist for patient objects for abnormal surgery based on the type of targeted procedure and frequent itemsets;
FIG. 4 is a block diagram of an embodiment of a medical information identification device for abnormal surgical activities according to the present invention;
fig. 5 is a hardware architecture diagram of one embodiment of the computer apparatus of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The medical information identification method and device for the abnormal operation behaviors provided by the invention can be suitable for the fields of medical services, insurance services and the like. According to the medical treatment settlement method and the medical treatment settlement system, the medical treatment list comprising the medical information of the patient is generated according to the medical treatment settlement data, the medical treatment list is processed according to the target operation type by adopting the correlation analysis algorithm, the frequent item set of the medical information with high frequency is obtained from the medical treatment list, and the abnormal operation corresponding to the target operation type can be screened out from the medical treatment list by utilizing the frequent item set, so that the purpose of accurately identifying the common operation behaviors caused by operation counterfeiting is realized, and the identification accuracy is high.
Example one
Referring to fig. 1, a method for identifying medical information of an abnormal surgical action according to the embodiment includes:
s1, acquiring medical settlement data of a patient object in a medical database;
in the present embodiment, the medical database is a database that stores medical settlement data of all patients (medical insurance patients, non-medical insurance patients). The medical settlement data includes personal information, hospital information, medical information, and the like of the patient. The diagnosis and treatment information can comprise medicine information, diagnosis and treatment information and the like.
S2, generating a medical list according to the medical settlement data, wherein the medical list comprises personal information, operation types and medical information of each patient object;
in this embodiment, the surgical types may include general surgery (e.g., appendicitis, liver abscess incision and drainage, rectal resection, etc.), orthopedic surgery (e.g., debridement and suture, joint replacement, etc.), neurosurgery (e.g., head tumor resection, intracranial hematoma removal, pituitary tumor resection, etc.), burn surgery, plastic surgery, cardiothoracic surgery, stomatological surgery, ophthalmic surgery (e.g., cataract surgery), cardiological surgery, etc. The medical information may include drug information and medical information. The drug information comprises the names of the social insurance three catalogs, the unified codes (the names and the unified codes of the drugs related to the basic medical insurance drug catalogs) and corresponding expense details. The medical information comprises three catalogue names of social security, unified codes (diagnosis and treatment item names and unified codes related to the diagnosis and treatment item catalogue, medical service names and unified codes related to the medical service facility catalogue and the like) and corresponding expense details.
In the embodiment, the field and the code format are unified by cleaning the medical settlement data in the medical database, so that the medical settlement data is converted into the data convenient to identify.
Further, step S2 generates a medical checklist according to the medical settlement data, including:
and extracting the operation type, the medicine information and the diagnosis and treatment information of each patient object in the medical settlement data according to a preset form template to generate the medical list.
In this embodiment, because the medical settlement data in the medical database has the situations of non-uniform codes and non-uniform fields, in order to facilitate the effective analysis of the data of each patient object and the identification of abnormal surgical behaviors by using the association analysis algorithm, the information of each patient object in the medical settlement data is extracted according to the preset form template to generate the medical list with uniform information format, thereby achieving the purpose of unifying the information of each patient.
S3, processing the medical information in the medical list by adopting an association analysis algorithm according to a target operation type to obtain a frequent item set associated with the target operation type;
in this embodiment, an association analysis algorithm (Apriori) belongs to one of unsupervised algorithms, and is configured to extract a potential association relationship between information from data, so as to obtain a frequent item set with high frequency, which is composed of a drug name, and/or a diagnosis and treatment item name, and/or a medical service name, and/or a drug uniform code, and/or a diagnosis and treatment item uniform code, and/or a medical service uniform code, and the like, that is, the drug name, the diagnosis and treatment item name, and the medical service name (and/or the drug uniform code, the diagnosis and treatment item uniform code, and the medical service uniform code) necessary in a target surgery type are screened out in a medical list through the association analysis algorithm, so that factors with low occurrence frequency are filtered, identification interference is reduced, and identification accuracy of an abnormal surgery line is improved. The frequent item set is a set consisting of the medicine names, the diagnosis and treatment item names, the medical service names, the medicine unified codes, the diagnosis and treatment item unified codes and the elements with high occurrence frequency of the medical service unified codes in the medical list. Taking the target operation type as the appendicitis operation as an example, the elements in the frequent item set are medicines, apparatuses, medical items and the like applied to the appendicitis operation.
The step S3 of processing the medical information in the medical list by using an association analysis algorithm according to a target surgery type to obtain a frequent item set associated with the target surgery type includes:
extracting personal information and medical information of patient objects associated with the target surgery type in the medical manifest; analyzing the medical information of all the patient objects by adopting the correlation analysis algorithm, and acquiring the support degree of the medical information of each patient object; and comparing the support degree of the medical information with a first medical threshold value, acquiring the medical information with the support degree larger than the first medical threshold value, and generating the frequent item set.
The method comprises the steps of extracting personal information and medical information of patient objects related to the type from a medical list according to the type of a target operation, generating a data set comprising patient object names and medical information, analyzing the medical information of all the patient objects in the data set through an association analysis algorithm, obtaining the name of each medicine, the name of a diagnosis and treatment item and the support degree of the name of a medical service (or obtaining the support degree of a medicine unified code, a diagnosis and treatment item unified code and a medical service unified code), comparing the name of each medicine, the name of the diagnosis and treatment item and the support degree of the medical service unified code with a first medical threshold, and uniformly placing all elements of the medicine name, the name of the diagnosis and treatment item or the name of the medical service larger than the first medical threshold in a frequent item set. The support degree refers to the proportion of each medicine name, diagnosis and treatment item name or medical service name in the data set.
In practical application, in the application step S3, different frequent item sets can be respectively generated for different target surgery types by using a correlation analysis algorithm, and each frequent item set corresponds to one target surgery type.
In this embodiment, the medical manifest may include hospital information for each patient subject;
the hospital information may include, among other things, the name of the hospital, the date the patient was admitted, the date the patient was discharged, and the information of the attending physician.
Considering that different hospitals may have different diagnosis and treatment means for the same target operation type, the present embodiment can analyze medical information of different hospitals for the target operation type, respectively, so as to ensure accuracy of identification of an abnormal operation line.
Specifically, the step S3 of processing the medical information in the medical list by using an association analysis algorithm according to the target surgery type, and acquiring the frequent item set associated with the target surgery type may include:
extracting personal information, medical information and hospital information of the patient object associated with the target surgery type in the medical list; analyzing the medical information of the corresponding patient object by adopting the correlation analysis algorithm according to the target hospital information to obtain the support degree of the medical information of each device object; and comparing the support degree of the medical information with a second medical threshold value, acquiring the medical information with the support degree larger than the second medical threshold value, and generating a frequent item set associated with the target operation type according to the medical information.
In the embodiment, personal information, hospital information and medical information of a patient object associated with the type are extracted from a medical list according to the target operation type, and a target operation data set comprising the name of the patient object, the hospital information and the medical information is generated; and extracting the patient object name and the medical information associated with the target hospital information in the target operation data set according to the target hospital information to generate a target hospital data set. Analyzing the medical information of all patient objects in the target hospital data set through an association analysis algorithm, acquiring the support degree of each medicine name, diagnosis and treatment item name and medical service name (or acquiring the support degree of the medicine uniform code, the diagnosis and treatment item uniform code and the medical service uniform code), comparing the support degree with a second medical threshold value, and uniformly placing all the elements of the medicine name, the diagnosis and treatment item name or the medical service name which are larger than the second medical threshold value in a frequent item set. The support degree refers to the proportion of each medicine name, diagnosis and treatment item name or medical service name in the data set.
And S4, inquiring the medical list according to the target operation type and the frequent item set, and acquiring the patient object of the abnormal operation.
And S4, screening the medical information of each patient in the medical list according to the frequent item set, acquiring the medical information of the patient not matched with the frequent item set, and further identifying abnormal operation behaviors.
Further, referring to step S4 shown in fig. 2, querying the medical list according to the target operation type and the frequent item set, and acquiring a patient subject of an abnormal operation includes:
s401, acquiring medical information of patient objects matched with the target operation type in the medical list one by one, matching the frequent item sets associated with the target operation type with the medical information, judging whether the frequent item sets are matched or not, and if yes, executing a step S403; if not, executing step S402;
through step S401, medical information of a patient object matched with a target surgery type is screened out from a medical list, the acquired medical information is matched with a corresponding frequent item set, and whether the medical information includes all elements in the frequent item set is identified, that is: and (4) reversely deducing a document which does not contain a frequent item set in the completeness of the operation, and judging that the operation is incomplete, wherein the operation of the patient is abnormal.
S402, carrying out abnormity identification on the patient object;
s403, judging whether the medical information meets preset conditions or not, and if so, indicating that the medical information is normal; if not, executing step S404;
wherein the preset condition is a preset range of the amount of the medical information;
for the medical information including all elements matched with the frequent item set, the step S403 may be adopted to further analyze the cost details of each element matched with the frequent item set in the medical information, and respectively determine whether the cost corresponding to the drug name, the diagnosis and treatment item name or the medical service name of the patient meets the preset condition, if yes, it indicates that the surgical behavior of the patient is normal, and if no, it indicates that the surgical operation of the patient is abnormal.
When the amount of the medical information is not in the preset range, the medical information does not accord with the preset condition; and when the amount of the medical information is in the preset range, the medical information meets the preset condition.
S404, carrying out abnormity identification on the patient object.
Considering that different hospitals may have different diagnosis and treatment means for the same target operation type, the present embodiment can analyze medical information of different hospitals for the target operation type, respectively, so as to ensure accuracy of identification of an abnormal operation line.
Specifically, referring to step S4 shown in fig. 3, querying the medical list according to the target operation type and the frequent item set, and acquiring a patient object of an abnormal operation includes:
s411, acquiring medical information of patient objects matched with the target operation type and the target hospital information in the medical list one by one, and matching the frequent item set associated with the target operation type with the medical information; if yes, go to step S413; if not, go to step S412;
through step S411, medical information of a patient subject matching the target operation type and the target hospital information is screened out from the medical list, the acquired medical information is matched with a corresponding frequent item set, and it is identified whether the medical information includes all elements in the frequent item set, that is: and (4) reversely deducing a document which does not contain a frequent item set in the completeness of the operation, and judging that the operation is incomplete, wherein the operation of the patient is abnormal.
S412, carrying out abnormity identification on the patient object;
s413, judging whether the medical information meets preset conditions or not, and if so, indicating that the medical information is normal; if not, go to step S414;
wherein the preset condition is a preset range of the amount of the medical information;
for the medical information including all elements matched with the frequent item set, the step S413 may be further used to analyze the cost details of each element matched with the frequent item set in the medical information, and respectively determine whether the cost corresponding to the drug name, the diagnosis and treatment item name or the medical service name of the patient meets the preset condition, if yes, it indicates that the surgical behavior of the patient is normal, and if no, it indicates that the surgical operation of the patient is abnormal.
When the sum of the medical information is not in the preset range, the medical information does not accord with the preset condition; and when the amount of the medical information is in the preset range, the medical information meets the preset condition.
S414, carrying out abnormity identification on the patient object.
In the embodiment, the medical information identification method for abnormal surgical actions generates the medical list including the medical information of the patient according to the medical settlement data, the medical list is processed according to the target surgical type by adopting the association analysis algorithm, the frequent item set of the medical information with high frequency is obtained from the medical list, and the abnormal surgery corresponding to the target surgical type can be screened out from the medical list by utilizing the frequent item set, so that the purpose of accurately identifying the common surgical actions of surgery counterfeiting is achieved, and the identification accuracy is high. The medical information identification method of abnormal operation behaviors can screen out the combination of social security three catalogues with high relevance aiming at the medical modes of different disease types and different hospitals in a fine granularity manner, thereby finding abnormal documents and automatically, quickly and effectively identifying abnormal operations, such as: and obtaining a frequent item set with strong association between the operation and the project through an association analysis algorithm, and reversely deducing the doctor seeing case without the frequent item set. The difference between the embodiment and the traditional medical insurance wind control system is that the system is more efficient, more potential risk points can be mined, medical insurance data are classified, sorted and analyzed through an association analysis algorithm, whether an operation is complete or not is judged according to a frequent item set, and the operation needs to contain some necessary items.
Example two
Referring to fig. 4, the medical information identification apparatus 1 for abnormal surgical actions of the present embodiment includes: an acquisition unit 11, a generation unit 12, an analysis unit 13, and a recognition unit 14; wherein the content of the first and second substances,
an acquisition unit 11 for acquiring medical settlement data of a patient object in a medical database;
in the present embodiment, the medical database is a database storing medical settlement data of all patients (medical insurance patients, non-medical insurance patients). The medical settlement data includes personal information, hospital information, medical information, and the like of the patient. The diagnosis and treatment information can comprise medicine information, diagnosis and treatment information and the like.
A generating unit 12, configured to generate a medical list according to the medical settlement data, where the medical list includes personal information, operation type, and medical information of each patient object;
in this embodiment, the surgical types may include general surgery (e.g., appendicitis, liver abscess incision and drainage, rectal resection, etc.), orthopedic surgery (e.g., debridement and suture, joint replacement, etc.), neurosurgery (e.g., head tumor resection, intracranial hematoma removal, pituitary tumor resection, etc.), burn surgery, plastic surgery, cardiothoracic surgery, stomatological surgery, ophthalmic surgery (e.g., cataract surgery), cardiological surgery, etc. The medical information may include drug information and medical information. The drug information comprises the name of the three-catalog of social insurance, a unified code (the name and the unified code of the drug related to the basic medical insurance drug catalog) and corresponding expense details. The medical information comprises three catalogue names of social security, unified codes (diagnosis and treatment item names and unified codes related to the diagnosis and treatment item catalogue, medical service names and unified codes related to the medical service facility catalogue and the like) and corresponding expense details.
In the embodiment, the field and the code format are unified by cleaning the medical settlement data in the medical database, so that the medical settlement data is converted into the data convenient to identify.
Further, the generating unit 12 is configured to extract the operation type, the drug information, and the diagnosis and treatment information of each patient object in the medical settlement data according to a preset form template, and generate the medical list.
In this embodiment, because the medical settlement data in the medical database has the situations of non-uniform codes and non-uniform fields, in order to facilitate the effective analysis of the data of each patient object and the identification of abnormal surgical behaviors by using the association analysis algorithm, the information of each patient object in the medical settlement data is extracted according to the preset form template to generate the medical list with uniform information format, thereby achieving the purpose of unifying the information of each patient.
The analysis unit 13 is configured to process the medical information in the medical list by using an association analysis algorithm according to a target surgery type, and acquire a frequent item set associated with the target surgery type;
in this embodiment, an association analysis algorithm (Apriori) belongs to one of unsupervised algorithms, and is configured to extract a potential association relationship between information from data, so as to obtain a frequent item set with high frequency, which is composed of a drug name, and/or a diagnosis and treatment item name, and/or a medical service name, and/or a drug uniform code, and/or a diagnosis and treatment item uniform code, and/or a medical service uniform code, and the like, that is, the drug name, the diagnosis and treatment item name, and the medical service name (and/or the drug uniform code, the diagnosis and treatment item uniform code, and the medical service uniform code) necessary in a target surgery type are screened out in a medical list through the association analysis algorithm, so that factors with low occurrence frequency are filtered, identification interference is reduced, and identification accuracy of an abnormal surgery line is improved. The frequent item set is a set consisting of the medicine names, the diagnosis and treatment item names, the medical service names, the medicine unified codes, the diagnosis and treatment item unified codes and the elements with high occurrence frequency of the medical service unified codes in the medical list. Taking the target operation type as the appendicitis operation as an example, the elements in the frequent item set are medicines, apparatuses, medical items and the like applied to the appendicitis operation.
The analysis unit 13 is used for extracting the personal information and medical information of the patient object associated with the target operation type in the medical list; analyzing the medical information of all the patient objects by adopting the correlation analysis algorithm to obtain the support degree of the medical information of each patient object; and comparing the support degree of the medical information with a first medical threshold value, acquiring the medical information with the support degree larger than the first medical threshold value, and generating the frequent item set.
The method comprises the steps of extracting personal information and medical information of patient objects related to the type from a medical list according to the type of a target operation, generating a data set comprising patient object names and medical information, analyzing the medical information of all the patient objects in the data set through an association analysis algorithm, obtaining the name of each medicine, the name of a diagnosis and treatment item and the support degree of the name of a medical service (or obtaining the support degree of a medicine unified code, a diagnosis and treatment item unified code and a medical service unified code), comparing the name of each medicine, the name of the diagnosis and treatment item and the support degree of the medical service unified code with a first medical threshold, and uniformly placing all elements of the medicine name, the name of the diagnosis and treatment item or the name of the medical service larger than the first medical threshold in a frequent item set. The support degree refers to the proportion of each medicine name, diagnosis and treatment item name or medical service name in the data set.
In practical applications, the application analysis unit 13 may generate different frequent itemsets for different target surgery types by using a correlation analysis algorithm, where each frequent itemset corresponds to one target surgery type.
In this embodiment, the medical checklist may include hospital information for each patient subject;
the hospital information may include, among other things, the name of the hospital, the date the patient was admitted, the date the patient was discharged, and the information of the attending physician.
Considering that different hospitals may have different diagnosis and treatment means for the same target operation type, the present embodiment can analyze medical information of different hospitals for the target operation type, respectively, so as to ensure accuracy of identification of an abnormal operation line.
Specifically, the analysis unit 13 may further extract personal information, medical information, and hospital information of the patient subjects associated with the target surgery type in the medical checklist; analyzing the medical information of the corresponding patient object by adopting the correlation analysis algorithm according to the target hospital information to obtain the support degree of the medical information of each device object; and comparing the support degree of the medical information with a second medical threshold value, acquiring the medical information with the support degree larger than the second medical threshold value, and generating a frequent item set associated with the target operation type according to the medical information.
In the present embodiment, personal information, hospital information, and medical information of a patient subject associated with a type are extracted in a medical checklist according to the target surgery type, and a target surgery data set including a patient subject name, hospital information, and medical information is generated; and extracting the patient object name and the medical information related to the target hospital information in the target operation data set according to the target hospital information to generate a target hospital data set. And analyzing the medical information of all patient objects in the target hospital data set through a correlation analysis algorithm, acquiring the support degree of each medicine name, diagnosis and treatment item name and medical service name (or acquiring the support degree of the medicine unified code, the diagnosis and treatment item unified code and the medical service unified code), comparing the support degree with a second medical threshold value, and uniformly placing all elements of the medicine name, the diagnosis and treatment item name or the medical service name which are larger than the second medical threshold value in a frequent item set. The support degree refers to the proportion of each medicine name, diagnosis and treatment item name or medical service name in the data set.
And the identification unit 14 is used for inquiring the medical list according to the target operation type and the frequent item set and acquiring the patient object of the abnormal operation.
The identification unit 14 screens the medical information of each patient in the medical list according to the frequent item set, acquires the medical information of the patient not matched with the frequent item set, and further identifies abnormal operation behaviors.
Further, referring to fig. 2, the step of querying the medical list by the identification unit 14 according to the target operation type and the frequent item set, and acquiring the patient subject of the abnormal operation may include:
s401, acquiring medical information of patient objects matched with the target operation type in the medical list one by one, matching the frequent item set associated with the target operation type with the medical information, judging whether the frequent item set is matched with the medical information or not, and if yes, executing a step S403; if not, executing step S402;
through step S401, medical information of a patient object matched with a target surgery type is screened out from a medical list, the acquired medical information is matched with a corresponding frequent item set, and whether the medical information includes all elements in the frequent item set is identified, that is: and (4) the completeness of the operation is realized, a document which does not contain a frequent item set is deduced reversely, and if the operation is judged to be incomplete, the operation of the patient is abnormal.
S402, carrying out abnormity identification on the patient object;
s403, judging whether the medical information meets preset conditions or not, and if so, indicating that the medical information is normal; if not, executing step S404;
wherein the preset condition is a preset range of the amount of the medical information;
for the medical information including all the elements matched with the frequent itemset, the step S403 may be adopted to further analyze the cost details of each element matched with the frequent itemset in the medical information, and respectively determine whether the cost corresponding to the drug name, the diagnosis and treatment item name or the medical service name of the patient meets the preset condition, if so, it indicates that the operation behavior of the patient is normal, and if not, it indicates that the operation of the patient is abnormal.
When the amount of the medical information is not in the preset range, the medical information does not accord with the preset condition; and when the amount of the medical information is in the preset range, the medical information meets the preset condition.
S404, carrying out abnormity identification on the patient object.
Considering that different hospitals may have different diagnosis and treatment means for the same target operation type, the present embodiment can analyze medical information of different hospitals for the target operation type, respectively, so as to ensure accuracy of identification of an abnormal operation line.
Specifically, the step of querying the medical list according to the target operation type and the frequent item set by referring to the identification unit 14 in fig. 3 to obtain the patient subject of the abnormal operation includes:
s411, acquiring medical information of patient objects matched with the target operation type and the target hospital information in the medical list one by one, and matching the frequent item set associated with the target operation type with the medical information; if yes, go to step S413; if not, go to step S412;
through step S411, medical information of the patient subject matched with the target operation type and the target hospital information is screened out from the medical list, the acquired medical information is matched with the corresponding frequent item set, and whether the medical information includes all elements in the frequent item set is identified, that is: and (4) reversely deducing a document which does not contain a frequent item set in the completeness of the operation, and judging that the operation is incomplete, wherein the operation of the patient is abnormal.
S412, carrying out abnormity identification on the patient object;
s413, judging whether the medical information meets preset conditions, if so, indicating that the medical information is normal; if not, go to step S414;
wherein the preset condition is a preset range of the amount of the medical information;
for the medical information including all the elements matched with the frequent itemset, the step S413 may be adopted to further analyze the cost details of each element matched with the frequent itemset in the medical information, and respectively determine whether the cost corresponding to the drug name, the diagnosis and treatment item name or the medical service name of the patient meets the preset condition, if so, it indicates that the operation behavior of the patient is normal, and if not, it indicates that the operation of the patient is abnormal.
When the amount of the medical information is not in the preset range, the medical information does not accord with the preset condition; and when the sum of the medical information is in the preset range, the medical information meets the preset condition.
S414, carrying out abnormity identification on the patient object.
In this embodiment, the medical information identification apparatus 1 for abnormal surgical actions generates a medical list including medical information of a patient according to medical settlement data, and processes the medical list according to a target surgical type by using a correlation analysis algorithm, so as to obtain a frequent item set of medical information with high frequency from the medical list, and by using the frequent item set, an abnormal operation corresponding to the target surgical type can be screened out from the medical list, thereby achieving the purpose of accurately identifying common surgical actions of surgery counterfeiting, and having high identification accuracy. The medical information identification device 1 for abnormal surgical actions can screen out a combination of social security three directories with high relevance according to different disease types and medical modes of different hospitals in a fine granularity manner, so that abnormal documents can be found, and abnormal surgeries can be automatically, quickly and effectively identified, for example: and obtaining a frequent item set with strong association between the operation and the items through an association analysis algorithm, and reversely deducing the doctor seeing case without the frequent item set. The difference between the embodiment and the traditional medical insurance wind control system is that the system is more efficient, more potential risk points can be mined, medical insurance data is classified, sorted and analyzed through an association analysis algorithm, whether an operation is complete or not is judged according to a frequent item set, and the operation needs to contain some necessary items.
EXAMPLE III
In order to achieve the above object, the present invention further provides a computer device 2, the computer device 2 includes a plurality of computer devices 2, the components of the medical information identification apparatus 1 for abnormal surgical activities according to the second embodiment may be dispersed in different computer devices 2, and the computer device 2 may be a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server, or a cabinet server (including an independent server or a server cluster formed by a plurality of servers) for executing programs, or the like. The computer device 2 of the present embodiment includes at least, but is not limited to: the memory 21, the processor 23, the network interface 22, and the medical information identification apparatus 1 for abnormal surgical behavior (refer to fig. 5) which are communicably connected to each other through a system bus. It is noted that fig. 5 only shows the computer device 2 with components, but it is understood that not all of the shown components are required to be implemented, and more or less components may be implemented instead.
In this embodiment, the memory 21 includes at least one type of computer-readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 21 may be an internal storage unit of the computer device 2, such as a hard disk or a memory of the computer device 2. In other embodiments, the memory 21 may also be an external storage device of the computer device 2, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like provided on the computer device 2. Of course, the memory 21 may also comprise both an internal storage unit of the computer device 2 and an external storage device thereof. In this embodiment, the memory 21 is generally used to store an operating system installed in the computer device 2 and various types of application software, such as a program code of the medical information identification method for abnormal surgical behavior according to the first embodiment. Further, the memory 21 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 23 may be a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor, or other data Processing chip in some embodiments. The processor 23 is typically used for controlling the overall operation of the computer device 2, such as performing control and processing related to data interaction or communication with the computer device 2. In this embodiment, the processor 23 is configured to operate the program code stored in the memory 21 or process data, such as the medical information identification apparatus 1 for operating the abnormal surgical operation.
The network interface 22 may comprise a wireless network interface or a wired network interface, and the network interface 22 is generally used to establish a communication link between the computer device 2 and other computer devices 2. For example, the network interface 22 is used to connect the computer device 2 to an external terminal through a network, establish a data transmission channel and a communication connection between the computer device 2 and the external terminal, and the like. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System of Mobile communication (GSM), wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, bluetooth (Bluetooth), wi-Fi, and the like.
It is noted that fig. 5 only shows the computer device 2 with components 21-23, but it is to be understood that not all shown components are required to be implemented, and that more or less components may be implemented instead.
In this embodiment, the medical information identification apparatus 1 of the abnormal surgical behavior stored in the memory 21 may be further divided into one or more program modules, which are stored in the memory 21 and executed by one or more processors (in this embodiment, the processor 23) to complete the present invention.
Example four
To achieve the above objects, the present invention also provides a computer-readable storage medium including a plurality of storage media such as a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, which when executed by a processor 23, implements corresponding functions. The computer-readable storage medium of the present embodiment is used for the medical information identification apparatus 1 that stores abnormal surgical actions, and when executed by the processor 23, implements the medical information identification method of the abnormal surgical actions of the first embodiment.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (9)
1. A medical information identification method for abnormal operation behaviors is characterized by comprising the following steps:
acquiring medical settlement data of patient objects in a medical database;
generating a medical list according to the medical settlement data, wherein the medical list comprises personal information, operation types and medical information of each patient object;
the medical manifest includes hospital information for each patient subject;
hospital information may include hospital name, patient subject admission date, discharge date, and attending physician information;
processing the medical information in the medical list by adopting an association analysis algorithm according to a target operation type to acquire a frequent item set associated with the target operation type, wherein the method comprises the following steps: extracting personal information, medical information and hospital information of the patient object associated with the target surgery type in the medical list;
analyzing the medical information of the corresponding patient object by adopting the correlation analysis algorithm according to the target hospital information to obtain the support degree of the medical information of each device object;
comparing the support degree of the medical information with a second medical threshold value, acquiring the medical information with the support degree larger than the second medical threshold value, and generating a frequent item set associated with the target operation type according to the medical information;
and inquiring the medical list according to the target operation type and the frequent item set to obtain the patient object of the abnormal operation.
2. The method for identifying medical information of abnormal surgical behavior according to claim 1, wherein the generating of the medical manifest from the medical settlement data includes:
and extracting the operation type, the medicine information and the diagnosis and treatment information of each patient object in the medical settlement data according to a preset form template to generate the medical list.
3. The method for identifying the medical information of the abnormal operation behavior according to claim 1, wherein the processing the medical information in the medical list by using an association analysis algorithm according to the target operation type to obtain a frequent item set associated with the target operation type comprises:
extracting personal information and medical information of patient objects associated with the target surgery type in the medical manifest;
analyzing the medical information of all the patient objects by adopting the correlation analysis algorithm to obtain the support degree of the medical information of each patient object;
and comparing the support degree of the medical information with a first medical threshold value, acquiring the medical information with the support degree larger than the first medical threshold value, and generating the frequent item set.
4. The method for identifying medical information of abnormal operation behavior according to claim 1, wherein the querying the medical list according to the target operation type and the frequent item set to obtain the patient object of the abnormal operation comprises:
acquiring medical information of patient objects matched with the target operation type in the medical list item by item, and matching the frequent item set associated with the target operation type with the medical information;
if the medical information is not matched with the frequent item set, carrying out abnormal identification on the patient object, and generating the patient object of the abnormal operation according to the abnormal identification;
if the medical information is matched with the frequent item set, judging whether the medical information meets a preset condition, and if not, performing abnormal identification on the patient object; the preset condition is a preset range of the amount of the medical information.
5. The method for identifying medical information of abnormal surgical behavior according to claim 1, wherein the querying the medical list according to the target surgical type and the frequent item set to obtain the patient object of the abnormal surgical operation comprises:
acquiring medical information of patient objects matched with the target operation type and the target hospital information in the medical list item by item, and matching the frequent item set associated with the target operation type with the medical information;
if the medical information does not match the frequent item set, carrying out abnormal identification on the patient object;
if the medical information is matched with the frequent item set, judging whether the medical information meets a preset condition, and if not, performing abnormal identification on the patient object; wherein the preset condition is a preset range of the amount of the medical information.
6. The method for identifying medical information of abnormal surgical behavior according to claim 1, wherein the medical information includes drug information and diagnosis and treatment information.
7. A medical information recognition apparatus for abnormal surgical behavior, comprising:
an acquisition unit for acquiring medical settlement data of a patient object in a medical database;
the generation unit is used for generating a medical list according to the medical settlement data, wherein the medical list comprises personal information, operation types and medical information of each patient object;
the medical manifest includes hospital information for each patient subject;
hospital information may include hospital name, patient subject admission date, discharge date, and attending physician information;
the analysis unit is used for processing the medical information in the medical list by adopting an association analysis algorithm according to a target operation type and acquiring a frequent item set associated with the target operation type, and comprises: extracting personal information, medical information and hospital information of the patient objects associated with the target surgery type in the medical checklist; analyzing the medical information of the corresponding patient object by adopting the correlation analysis algorithm according to the target hospital information to obtain the support degree of the medical information of each device object; comparing the support degree of the medical information with a second medical threshold value, acquiring the medical information with the support degree larger than the second medical threshold value, and generating a frequent item set associated with the target operation type according to the medical information;
and the identification unit is used for inquiring the medical list according to the target operation type and the frequent item set and acquiring the patient object of the abnormal operation.
8. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of any one of claims 1 to 6 when the computer program is executed.
9. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program when executed by a processor implements the steps of the method of any one of claims 1 to 6.
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| CN112349399B (en) * | 2020-11-12 | 2022-05-24 | 杭州火树科技有限公司 | Operation omission automatic detection method based on correlation algorithm |
| CN113035341A (en) * | 2021-03-26 | 2021-06-25 | 贵州和瑞医疗科技有限公司 | Medical consumable inventory statistical system |
| CN113077906B (en) * | 2021-04-28 | 2022-12-13 | 上海德衡数据科技有限公司 | Metadata-based medical information acquisition method, system, device and medium |
| CN116469548B (en) * | 2023-06-20 | 2023-09-12 | 中国人民解放军总医院 | Intelligent medical risk identification early warning system |
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