JP4866576B2 - Medical support system - Google Patents

Medical support system Download PDF

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JP4866576B2
JP4866576B2 JP2005202326A JP2005202326A JP4866576B2 JP 4866576 B2 JP4866576 B2 JP 4866576B2 JP 2005202326 A JP2005202326 A JP 2005202326A JP 2005202326 A JP2005202326 A JP 2005202326A JP 4866576 B2 JP4866576 B2 JP 4866576B2
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problem
database
medical
diagnosis
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JP2007018460A (en
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功哲 吉原
泰志 松村
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インフォコム株式会社
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  The present invention relates to a medical care support information system used in the medical field, and in particular, information related to a problem including a diagnosis name or a patient state that may be affected based on medical care event information generated from medical information related to a patient, When the diagnosis name and its certainty are estimated based on the medical event information and the problem related information, and the treatment action for the estimated diagnostic name is judged to be undesirable in the system based on the medical event information and the problem related information The present invention relates to a medical support information system that also notifies a warning to a patient.

In general, in a hospital, in order to manage personal information for a patient, medical information for the patient is converted into information, and the information is stored in a database in an information system in the hospital. The medical information includes basic information such as name, age, sex, height, weight, etc., for example, medical record information obtained by conducting a medical examination on a patient, and nursing record information obtained during nursing Prescription information and injection information related to the medicine instructed to be administered, inspection item information and inspection result information related to the instructed inspection, image report information using radiation, endoscope, magnetic resonance, ultrasound, etc. Information on injury and illness such as information on surgery or treatment is included.
Various electronic medical information is stored in a database for the purpose of referring to a doctor for treatment judgment or simply leaving a record. Therefore, from the clinical standpoint, the current situation is that the various digitized medical information is not fully utilized.

Patent Document 1 discloses a medical diagnosis support system that automatically performs medical diagnosis of a patient using medical data accumulated in a network.
According to Patent Document 1, in the system, first, the patient himself selects subjective symptoms related to the wounded part. Next, a disease name estimated from the subjective symptoms is automatically diagnosed, and a list of possible disease names is displayed. Furthermore, a doctor's inquiry, a test | inspection, etc. are performed and those results etc. are displayed with the disease name with high possibility of the said disease name list. At that time, a medical prescription and precautions for use of the medicine are also displayed as medical treatment. Next, the medical treatment is ranked from the treatment results and accumulated in the database, and the treatment accuracy is improved as a reference material for the treatment policy.

JP 2004-118279 A

In the system in Patent Document 1, a disease name list is created by automatically estimating disease names from only the subjective symptoms of the patient himself, and contributes to the diagnosis of a doctor. However, the number of disease names listed in the list is relatively large, and the accuracy of estimated disease names must be lowered.
In addition, the system merely displays medical examination results such as medical examinations and examination results based on the estimated disease name, and medication information. For example, there are treatment actions that should not be performed depending on the examination results. Inappropriate medical measures may be taken due to carelessness of the doctor.

  In view of the above problems, the present invention estimates the problem related information including the diagnosis name and the patient state necessary for the actual medical practice based on the medical event information generated from the medical information of the patient with high accuracy. It is an object of the present invention to provide a system and method for warning precautions regarding prescription, injection, examination, etc. based on medical event information and problem related information.

(1) In order to achieve the above-described object, the medical assistance system of the present invention includes diagnostic name order information, medical examination record information obtained by a medical examination performed on a patient, and nursing medical record information obtained during nursing. Examination item information and examination result information relating to the instructed examination, prescription information and injection information relating to the medicine instructed to be administered, and image report information using any of radiation, endoscope, magnetic resonance, and ultrasound A medical information section having medical information including information on surgery or treatment;
Converting each of the medical information into information of a predetermined format, and converting information other than diagnosis name order information as medical event information;
And a problem related information extraction unit for estimating problem related information including a diagnosis name or a patient state which may be affected based on the converted medical event information.
(2) The information conversion unit in the system of the present invention can recognize a part of information in the medical record information, nursing record information or image report information, and convert similar information into unique medical event information. preferable.
(3) It is preferable that each information converted in the information conversion part of the system of this invention is linked | related with the information regarding the conversion date and the expiration date of the said information.
(4) In the information conversion unit of the system of the present invention, it is preferable that the medical event information includes a part of medical information that has not been converted.
(5) It is preferable that the information conversion unit of the system of the present invention generates medical event information based on medical information and basic patient information.
(6) The information conversion unit of the system of the present invention preferably includes a medical event information database capable of storing medical event information.
(7) In the problem related information extraction unit of the system of the present invention, it is preferable that the problem related information is associated with information related to date and time.
(8) It is preferable that the problem related information extraction unit of the system of the present invention includes a problem related information database capable of storing the extracted problem related information.
(9) The system of the present invention is an active problem extraction that estimates the latest problem related information based on the problem related information stored in the problem related information database in the problem related information extraction unit and the information related to the date and time associated therewith. It is preferable to further comprise a part.
(10) In the present invention, it is preferable that the active problem extraction unit includes an active problem database capable of storing the latest problem related information.
(11) It is preferable that the present invention further includes a necessary examination / finding extraction unit that estimates necessary examination items and findings information based on the latest problem-related information and medical event information.
(12) It is preferable that the system of the present invention includes a necessary inspection / finding database capable of storing the estimated required inspection items and finding information.
(13) The system according to the present invention provides a diagnosis name that may be affected based on at least one of the problem-related information and the medical event information, or at least one of the latest problem-related information and the medical event information. It is preferable to further include an automatic diagnosis unit that estimates information.
(14) In the system of the present invention, it is preferable that the automatic diagnosis unit also determines the certainty factor of the diagnosis name information.
(15) The system of the present invention preferably includes a diagnosis name information database capable of storing the estimated diagnosis name information or both the diagnosis name information and the certainty factor in the automatic diagnosis unit.
(16) The system of the present invention stores the diagnosis name information estimated by the automatic diagnosis unit in the problem related information database.
(17) The system of the present invention further includes a warning unit that extracts warning information based on at least one of problem-related information and medical event information, or based on at least one of the latest problem-related information and the medical event information. It is preferable to do.
(18) The system of the present invention preferably includes a warning record database capable of storing the extracted warning information in the warning section.

(1) The onset Ming, various medical information for the patient aggregate as medical event information as an element for determining the state of the diagnosis or the patient with a morbidity rate of diagnosis or patient a morbidity rate conditions Can be estimated, and based on the detected medical event information, problem-related information including a diagnosis name or a patient state that may be affected can be estimated.
Therefore, it is possible to construct a system for automatically estimating the problem related information associated with each medical event information with high accuracy by effectively utilizing the medical information for the patient in the medical information system of the hospital in a wide range.
(2) The onset Ming, examination record information, recognize some of the information in the nursing record information or image report information, since the information of synonymous can be converted to a unique medical event information, Ya physician It is possible to construct a system capable of creating a medical record, nursing record, image report, etc. without having to unify the expressions used for each nurse.
(3) The onset Ming, information converted by the information conversion unit, so associated with information about the expiration date of the conversion time and the information, the expiration date and the information created each medical event information A manageable system can be constructed.
(4) The onset Ming, the medical event information, since it contains some of the medical information that has not been converted, for example, directly stores numerical information to the inspection items such as the inspection result without conversion by the information conversion unit And an efficient system can be constructed.
(5) The onset Ming, information conversion unit, medical information, and because it produces a medical event information by the basic information of the patient, it is possible to collect more meaningful clinical event information, extracting Problem related information and It is possible to construct a system that contributes to improving the accuracy of certainty of diagnosis name information.
(6) The onset Akira Since comprises retractable medical event information database medical event information, it is possible to easily manage the accumulated information, it is possible to refer to the appropriate stored information.
(7) The onset Ming, since Problem related information is associated with information about the date and time, and problem-related information, for example, can be constructed manageable system for each date and time created.
(8) The onset Ming, since the extracted Problem related information comprises a Problem related information database capable of storing all the Problem related information estimated can be constructed easily manageable system.
(9) The onset Ming, since further comprises an active Problem extractor, when the medical event information or Problem related information is added, from the Problem related information stored in the Problem related information database, the latest date and time It is possible to construct a system that can extract only the problem related information created in
(10) The onset Akira Since comprises an active Problem database capable of storing the latest Problem related information, can be constructed easily manageable system the latest Problem related information.
(11) The onset Ming, since further comprising a necessary inspection and findings extractor for estimating the required test items and finding information, required tests and to be examined for each Problem related information stored in the active Problem database, If there is finding information for the required medical event information to be added, it is possible to construct a system capable of notifying it and adding the required order.
(12) The onset Akira Since comprises a necessary inspection and findings database, extracted the required inspection item information and findings information listed can be constructed capable of storing system.
(13) The onset Ming, since further comprising an automatic diagnosis unit that estimates a diagnosis information with morbidity, it is possible to construct a system that can extract diagnosis with morbidity in high accuracy.
(14) The onset Ming, is performed also confidence of the determination in the diagnosis information, it is possible to construct easily identifiable system a highly reliable diagnosis.
(15) The onset Akira Since comprises a diagnosis information database, the diagnosis was estimated, for example, can be constructed manageable system listing the patient every confidence order.
(16) The onset Ming, since stores diagnosis information estimated in the automatic diagnosis unit to the Problem related information database, for example, if you want to know all the problem-related information estimated, the Problem related information extraction section A recognizable system can be constructed by searching the problem related information database.
(17) The onset Akira Since comprises a warning unit for extracting alert information further example, or is input orders are taken is not appropriate therapeutic intervention against Problem related information already extracted, the extracted When a contraindicated prescription order is input for the problem related information, a system capable of warning that effect can be constructed.
(18) The onset Akira Since comprises a warning recording database can manage the alert record list, it is possible to construct a system which can be confirmed by the screen display on the display device or the like.

  The preferred embodiments of the present invention will be described with reference to the accompanying drawings.

  FIG. 1 is a schematic diagram showing the overall configuration of a medical assistance system according to the present invention. The system consists of a medical information section, an information conversion section, a problem related information extraction section, an active problem extraction section, a necessary examination / finding extraction section, an automatic diagnosis section, and a warning section, which are connected through a telecommunication line such as a network. Yes.

The medical information section in the system according to the present invention will be described. The medical information section includes a basic information database (not shown), a diagnosis name order database, a diagnosis and nursing record database, a test result database, a test order database, a prescription and injection order database, an image report database, and a surgery and treatment order database. The predetermined information described below is stored in each database.
Here, medical information in the present specification includes patient personal information, diagnosis name information, examination and nursing record information, examination order information including examination items, examination result information, prescription information, injection information, and image report information. It is a generic name including surgery information, treatment information, and the like.
The knowledge base in the present specification is a database having a structure including a condition part and a processing part. Its condition part is stored predetermined information as the matching condition, the processing unit, when the input information is appropriate to the predetermined match condition stored in the condition part, information of a predetermined format input information Is stored as predetermined information . In the following embodiments, the condition of the condition part is the basis for extracting the medical event information, extracting the problem related information, extracting the necessary examination / finding information, extracting the diagnosis name information, or extracting the warning information .

Information stored in each database of the medical information section in the system of the present invention will be described.
Although the basic information database is not shown in the figure, it stores personal information such as the patient's name, age, gender, height, weight, etc., hospitalization information such as the hospitalization date and time, the name of the primary doctor or the name of the doctor in charge, It is a database that can be accessed and referenced at any time when executing each logic described in detail below.

  The diagnosis name order database includes information on the diagnosis name for the patient, information on the presence / absence of “suspect” indicating the attribute, information on whether the diagnosis name is clinical or insurance claims The information is stored in numerical values, characters, sentences, or a selective code format, and the information is input to the diagnosis name order database by the hospital staff. Here, the diagnosis name information is not unified according to the diagnosed doctor, and as described above, a plurality of synonyms such as clinical or insurance claims may be input.

The medical examination and nursing record database stores the medical record information obtained from the medical examination conducted for the patient and the nursing record information obtained during nursing in numerical, text, text, or selective code formats. The information is input to the medical examination and nursing record database by hospital staff.
Information necessary for the findings and examinations required by the necessary examination / finding extraction unit is also stored in numerical values, characters, sentences, or a selective code format.

  The examination order database stores the information of the instructor of the examination order and the information of the examination item related to the examination instructed to the patient in numerical values, characters, sentences, or a selective code format. Is entered into the examination order database by hospital personnel.

  The test result database associates the information used by the tester and the test result information for the ordered test item in numerical, text, text, or selective code format, with the units used in the test result. The information is stored, and the information is input to the examination result database by the hospital staff.

  The prescription and injection order database includes prescription information and injection information related to a medicine instructed to be administered to a patient, including the order and administration period of the medicine, characters, sentences, numerical values indicating dosages, or selective codes. The information is stored in the form, and the information is input to the prescription and injection order database by the hospital staff.

  The image report database includes report information on images acquired by examination of radiation, endoscope, magnetic resonance, or ultrasound, including information on the person who performed the image acquisition and findings by the doctor, Stored in numerical or selective code format.

  The Surgery and Procedure Order Database stores information on the operator performing the procedure, the name of the procedure, the name of the procedure, or the name of the procedure in character, text, numerical value, or selective code format. Yes.

The information conversion unit in the system according to the present invention will be described.
The information conversion unit includes an information conversion logic unit corresponding to each medical information, each knowledge base (not shown) corresponding to each information conversion logic, and a medical event information database.
The information conversion logic unit is a diagnosis name order information conversion logic corresponding to each database of diagnosis name order, diagnosis and nursing record, test result, test order, prescription and injection order, image report, and operation and treatment order in the medical information unit. Diagnosis and nursing record information conversion logic, test result information conversion logic, test order information conversion logic, prescription and injection order information conversion logic, image report information conversion logic, and surgery and treatment order information conversion logic. . Each information conversion logic makes a mutual judgment with each knowledge base by using the input to each corresponding medical information database as a trigger, and outputs information so that medical event information registered in advance in each knowledge base is output when the input matches. Convert. Furthermore, each information conversion logic can associate time-managementlable information such as the date and time when the output is generated with each of the medical event information that has been output. By setting, it has a role of setting an expiration date for each medical event.
Next, although each knowledge base is not shown, the diagnosis name order information conversion knowledge base, the diagnosis and nursing record information conversion knowledge base, and the test result information conversion that are referred to when each information conversion logic is activated. A knowledge base, an examination order information conversion knowledge base, a prescription and injection order information conversion knowledge base, an image report information conversion knowledge base, and a surgery and treatment order information conversion knowledge base.
Finally, the medical treatment event information database is a database for collecting information converted by each information conversion logic other than the diagnosis name order information and storing them.
The information conversion unit plays a role of converting into an information format that can be interpreted by the problem related information extraction unit, and also has a filter function for extracting only a required word or phrase from information such as sentences.

Next, the diagnosis name order information conversion logic will be described.
The diagnosis name order information conversion logic extracts the information of the diagnosis name input to the diagnosis name order database. The extracted information such as the phrase or term of the diagnosis name is collated with information such as the word or the term registered in advance in the diagnosis name order information conversion knowledge base, and a plurality of synonyms are unified into one diagnosis name information. Is converted and output. The diagnosis name information output from the diagnosis name order information conversion logic is stored in the problem related information database in association with information indicating the date and time.

Next, the diagnosis and nursing record information conversion logic will be described.
The diagnosis and nursing record information conversion logic extracts the diagnosis and nursing record information in the form of characters, sentences, or selective codes input to the diagnosis and nursing record database. From the extracted information showing the examination and nursing record text, etc., the words or terms are extracted using the character recognition technology by the predetermined software, and the extracted words or terms information is the The word or phrase registered in the recorded information conversion knowledge base is collated with information registered in advance, and the matched word or phrase is converted into a predetermined information format and stored in the medical event database in association with information indicating the date and time. The

Next, the inspection order information conversion logic will be described.
The inspection order information conversion logic extracts inspection order information in the form of characters, sentences, or selective codes including inspection items input to the inspection order database. The extracted examination order information is collated with the examination order information registered in advance in the examination order information conversion knowledge base, and if it matches, the examination order information is converted into information in a predetermined format and associated with information indicating the date and time, etc. Stored in

Next, the inspection result information conversion logic will be described.
The inspection result information conversion logic extracts inspection result information for the inspection item of the inspection order input to the inspection result database. The extracted test result information is collated with the test result information registered in the test result information conversion knowledge base in the case of characters, sentences, or a selective code format. It is converted into information and stored in the medical event information database in association with information indicating the date and time. In practice, it is often input in a numerical format or a polarity code indicating negative positive. In this case, the information is not converted and the numerical format or the information of the polarity code is stored in the medical care event information database in association with the information indicating the date and time and the information indicating the unit. It is associated with the inspection item.
The test result information conversion logic is based on the test result information for the test item of the test order input in the test result database, or the basic information of the patient stored in the basic database and the basic information input by the input means. In addition, it has a function of generating and outputting medical event information (see Example 4-4).

Next, prescription and injection order information conversion logic will be described.
The prescription / injection order information conversion logic extracts information on a group of medicines and the like, a dosage amount, and an administration period information input to the prescription / injection order database. The information on the extracted medicine group, etc. is collated with the information on the medicine group, etc. registered in advance in the above prescription and injection order information conversion knowledge base. Is stored in the medical event information database. In addition, since the information on the dosage and the administration period of the medicine is often in a numerical format, it is stored in the medical care event database in association with information indicating the date and time and information indicating the unit directly without being converted. , Associated with information about the group of converted medicines, etc.

Next, the image report information conversion logic will be described.
Image report information conversion logic is text, text, or selective code format report information obtained from images of radiation, endoscope, magnetic resonance, ultrasound, etc. entered in the image report database. To extract. From the information indicating the sentences etc. in the extracted report, a word or term is extracted using a character recognition technology by a predetermined software, and the extracted word or term information is converted to the above image report information conversion. The matched phrase or term is collated with information such as a phrase or term registered in advance in the knowledge base, converted into a predetermined information format, and stored in the medical care event information database in association with information indicating date and time.

Next, the surgery and treatment order information conversion logic will be described.
The operation and treatment order information conversion logic extracts operation information regarding an operator who performs an operation, an operation method, and the like, and treatment information such as wound treatment and blood cell component removal, which are input to the operation and treatment order database. Information on the extracted surgical method and the like is collated with information on the surgical information and the treatment information registered in advance in the surgery and treatment order information conversion knowledge base, and if they match, it is converted into a predetermined information format, The medical event information database stores information related to the date and time.

The problem related information extraction unit in the system according to the present invention will be described.
The problem-related information extraction unit includes a problem-related information extraction logic, a problem-related information extraction knowledge base that is referred to when the problem-related information extraction logic is activated, and a problem-related information database.
The problem-related information extraction logic is information stored in the medical care event information database and information registered in advance in the problem-related information extraction knowledge base and associated with information on a diagnosis name or a patient's condition that may be affected. Are matched, and if there is a match, the diagnosis name or patient status information that may be affected is extracted as problem-related information. The extracted problem related information is stored in the problem related information database in association with information indicating the date and the like together with the grounds for extracting the problem related information. Note that the information format in the medical care event information database matches the information format stored in the problem related information extraction knowledge base.

  Here, with respect to the extracted problem-related information, the patient status includes, for example, information that is not classified as a diagnosis name such as a decrease in kidney function or a decrease in liver function, and the diagnosis name includes acute renal failure and diabetes Diagnostic name information indicating the name of the injury or illness.

The active problem extraction unit in the system according to the present invention will be described.
The active problem extraction unit includes an active problem extraction logic and an active problem database. The active problem extraction logic extracts only information within the expiration date from the problem related information stored in the problem related information database, and if there are multiple pieces of the same type of problem related information records, the latest problem Extract only relevant information. The extracted latest problem related information is stored in the active problem database.

The necessary inspection / finding extraction unit in the system according to the present invention will be described.
Necessary examination indicates examination items and the like necessary for estimating certain problem related information with high accuracy, and findings indicate examination and nursing record information that can be medical event information for the certain problem related information. The necessary inspection / finding extraction unit includes a necessary inspection / finding extraction logic, a necessary inspection / finding extraction knowledge base to be referred to when the necessary inspection / finding extraction logic is activated, and a necessary inspection / finding database. Are provided. In the necessary inspection / finding knowledge base, necessary inspection items or inspection items and findings are associated with the problem related information. Necessary examination / finding extraction logic is required for each problem related information stored in the active problem database based on the necessary examination / finding knowledge base by referring to the active problem database using the input to the active problem database as a trigger. New inspection items or findings are extracted and stored in the necessary inspection / finding database together with information related to the date and time to prompt entry of inspection orders and findings. In addition, the necessary examination / finding extraction logic is necessary to refer to the active problem database together with the clinical event information database to determine whether the necessary examination has already been performed on the problem related information. It can also be designed not to output to the inspection / finding database.

The automatic diagnosis unit in the system according to the present invention will be described.
The automatic diagnosis unit includes an automatic diagnosis logic, an automatic diagnosis knowledge base (not shown) that is referred to when the automatic diagnosis logic is activated, and a diagnosis name list database. The automatic diagnosis logic refers to the problem related information stored in the active problem database and the medical event information stored in the medical event information database, and is associated with the diagnosis name information registered in advance in the automatic diagnostic knowledge base. The diagnosis name is extracted based on the problem related information, the medical event information, and the combination thereof. There may be a plurality of extracted diagnostic names. The extracted diagnosis name is also set with a certainty factor by a combination of the problem related information and the medical event information registered in advance in the automatic diagnosis knowledge base. Therefore, by combining the problem-related information registered in the automatic diagnosis knowledge base and the clinical event information, it is possible to set multiple certainty factors for the same diagnosis name. The certainty factor can be determined, and the estimation accuracy of the diagnosis name can be improved.
Next, the extracted diagnosis name and certainty factor are stored in the diagnosis name list database together with the date and time information, and at the same time in the problem related information database.
Here, an embodiment has been shown in which the automatic diagnosis unit of the system according to the present invention accesses the active problem extraction unit. However, the active problem extraction unit is omitted and the problem related information extraction unit is accessed as described above. In addition, it is obvious to those skilled in the art that the effect of acquiring the diagnosis name and the certainty factor with high accuracy can be obtained.

The warning unit in the system according to the present invention will be described.
The warning unit includes a warning logic, a warning knowledge base (not shown) that is referred to when the warning logic is activated, and a warning record database. The warning logic is determined based on the monitoring information of the examination results, prescriptions and injection orders, etc. registered in advance in the warning knowledge base, and the examination progress information, as judged by the input of the active problem database or the clinical event information database. The warning is notified, and the notified contents are stored in the warning database together with the date and time. In addition, the warning logic can manage the time limit of each information based on information related to the date and time associated with the medical event information and the active problem information.
Here, an embodiment has been shown in which the warning unit of the system according to the present invention accesses the active problem extraction unit. However, the active problem extraction unit is omitted and the problem related information extraction unit is accessed in the same manner as described above. It is obvious to those skilled in the art that the effect of warning is obtained.

Example 1
An embodiment for automatically diagnosing a diagnosis name when a patient complains of a headache using the medical assistance system according to the present invention will be described below.

First, a procedure for obtaining medical information using the system of the present invention together with a doctor's examination and inputting each medical information into a required database in the medical information section will be described.
The doctor receives a headache complaint from the patient through the examination, asks what kind of pain is involved, and enters “persistent headache” in the examination and nursing record information database. Then, the system determines that a detailed examination of the head is required, and prompts the doctor to do so. Next, the doctor orders a CT examination of the head, and “CT examination” is input to the examination instruction database in the medical care information section. Next, when a CT examination is performed and a mass-like shadow is recognized in the image, “a tumor-like shadow is recognized in the brain” is input to the image report database.
Through the above procedure, medical information for the patient is acquired in the medical information section, and automatic diagnosis is performed based on the medical information.
Hereinafter, a detailed process until the diagnosis support system according to the present invention determines that a head examination is required and a detailed process until the diagnosis name is automatically diagnosed will be described. In the following description, terms registered in each knowledge base, numerical values associated therewith, and the like are merely examples, and do not limit the system according to the present invention.

First, the diagnosis and nursing record information conversion logic in the information conversion unit is activated with the input of the diagnosis information “sustainable headache” to the diagnosis and nursing record information database in the diagnosis information unit. The diagnosis and nursing record information conversion logic starts predetermined software, extracts a “headache” using character recognition technology, and collates it with a diagnosis and nursing record information knowledge base not shown. If it matches with “S100-headache” registered in the knowledge base of the medical examination and nursing record information in advance, for example, “S100” is output, and “S100” is associated with the date and time information such as the expiration date and stored in the medical care event information database To do.
Next, in the problem related information extraction unit, “S100” stored in the medical care event database is input to the problem related information extraction logic and converted into related information “P100-headache” by the problem related information extraction knowledge base. The problem-related information database stores “P100”, the grounds for extracting the problem-related information, and information related to the date and time. Next, the active problem extraction unit confirms that there is no other relevant problem information related to the patient related to “headache”, and “P100”, the grounds for extracting the related information of the problem, and the information related to the date and time are the active problem. Stored in the database.
Here, with the input to the active problem database as a trigger, the necessary inspection / finding extraction logic is activated, and “inspection of the head CT” corresponding to “P100” is required based on the necessary inspection / finding extraction knowledge base (not shown). Refer to the clinical event database to confirm whether the information should be output. When it is confirmed that the head CT has not been inspected, the fact is recorded in the necessary inspection / finding database, and the user is prompted to perform head CT. However, if an inspection order for the head CT has already been made, recording to the necessary inspection / finding database is not performed.
Next, consider a case where head CT is prompted and a head CT examination is ordered for the patient and is input to a radiographic image report database based on the CT examination.
In this case, the image report information conversion logic is started by using the input of medical information “a mass-like shadow is recognized in the brain” to the image report database as a trigger, and a predetermined software is started and character recognition technology is used. Extract and extract the terms “in the brain”, “massage shadow”, and “recognized”, and if “recognized” is registered as a conversion target of “Yes”, convert it to “Yes” These are collated with “S110—with intracerebral tumor-like shadow” registered in advance in the image report information conversion knowledge base, and are associated with the date and time information and stored in the medical care event information database.
Next, in the problem related information extraction unit, the problem related information extraction logic is activated by using an input to the medical care event information database as a trigger. The problem related information extraction logic collates medical event information stored in the medical event information database with information associated with problem related information registered in advance in a problem related information extraction knowledge base (not shown). For example, when “P110—with a mass-like shadow in the brain” corresponding to “S110” in the medical treatment event database is registered in the problem-based information extraction knowledge base, the problem-related information extraction logic is , “P110” is stored in the problem related information database together with the grounds for extracting the problem related information and information related to the date and time.
Subsequently, in the active problem extraction unit, the active problem extraction logic confirms the date and time associated with the problem related information “P110” stored in the problem related information database of the problem related information extraction unit, and the problem related information “P110”. Is extracted. Further, the active problem extraction logic checks whether or not the same type of problem related information as the information “P110” stored in the problem related information database exists in the problem related information database. If there is no input information other than “P110”, it is output as it is with the date and time associated with “P110” as the active problem, and stored in the active problem database.
Next, in the automatic diagnosis unit, the automatic diagnosis logic is activated with the input to the active problem database as a trigger. The automatic diagnosis logic collates the latest problem related information “P110” stored in the active problem database with information associated with a diagnosis name registered in advance in an automatic diagnosis knowledge base (not shown). For example, if it is assumed that the automatic diagnosis knowledge base has knowledge that the condition part is “P110” and the processing part is “diagnosis name: brain tumor, certainty level: 90% or more”, the condition part and the active problem database are stored. The latest problem related information “P110” is matched, and “diagnosis name: brain tumor, certainty level: 90% or more” is output as an output.
As described above, the diagnosis name “brain tumor” is estimated with a considerably high degree of certainty.

(Example 2)
A diagnosis example of automatic diagnosis using the medical assistance system according to the present invention when rheumatoid arthritis is suspected will be described below.

The doctor enters an information record in the examination and nursing record database of the medical information department by the examination of the patient, “There is fever and local erythema is observed. Numbness of the finger joint and wrist joint pain are also accompanied”. Next, the diagnosis and nursing record information conversion logic is activated.
The diagnosis and nursing record information conversion logic extracts the words “fever”, “erythema”, “numbness”, and “wrist joint pain” using the character recognition technology of the predetermined software, and uses them for the above-mentioned diagnosis and nursing record knowledge Check against the base. For example, if “S200-fever”, “S210-erythema”, “S220-numbness”, “S230-wrist joint pain” registered in the knowledge base for examination and nursing record information conversion is matched, for example, “S200” , “S210”, “S220”, “S230” are output and stored in the medical care event information database.
Next, the problem-related information extraction logic of the problem-related information extraction unit uses the information of “S200”, “S210”, “S220”, “S230” stored in the medical event information database and the above-mentioned problem-related information extraction knowledge base. For example, information “P345-Rheumatoid arthritis” associated with information of “S210” and “S220” registered in advance and information “P001-suspect” that modifies this are extracted, and are used as problem related information. It is stored in the problem related information database together with the grounds for extracting the problem related information and information on the date and time.
Subsequently, in the active problem extraction unit, the active problem extraction logic confirms information related to the date and time associated with the problem related information stored in the problem related information database, and if necessary, extracts it as the latest problem related information. To do. In this case, since only the information “P345-Rheumatoid arthritis” and “P001-suspect” that modifies this are stored in the problem related information database, the active problem extraction logic recognizes the information and the active problem Store directly in the database.
Subsequently, the automatic diagnosis logic is activated, “Rheumatoid arthritis” is output as the diagnosis name, and the diagnosis name is stored in association with the date and time information in the diagnosis name list database. However, the certainty of the diagnosis name at this point is relatively low.
At the time of input to the active problem database, the necessary inspection / finding extraction logic is activated in the necessary inspection / finding extraction unit by using the input as a trigger. The necessary examination / finding extraction logic confirms the date and time associated with the latest active problem information stored in the active problem database, and if necessary, refers to the active problem database and the medical event information database (FIG. 1). In order to obtain the clinical events necessary for estimating “rheumatoid arthritis” with high accuracy, reference is made to a necessary examination / finding knowledge base (not shown) and the association with “rheumatoid arthritis” Check “existence of fever” that can be a test item or a medical event. However, since “fever” has already been stored in the medical event information database, it is not determined that an additional finding related to “fever” is necessary.
Next, the doctor confirms the inspection item and orders the required inspection. Test orders for “rheumatoid arthritis” include blood tests (red blood cells, white blood cells, hemoglobin, hematocrit, etc.), urine tests (protein, sugar, blood, etc.), inflammatory marker tests (CRP and blood sedimentation), enzyme activity tests (GOT, GPT). , LDH, etc.), presence / absence of antinuclear antibody, and rheumide factor test. When the doctor selects an examination item from among them, the examination order is entered in the examination order database, and after the examination, the examination result is entered into the examination result database together with the unit in a predetermined format such as a numerical format. The Next, using the input to the test result database as a trigger, the test result information conversion logic in the information conversion unit is activated, and the test result information is associated with the date and time and stored in the medical event information database. Next, as described above, in the problem related information extraction unit, new problem related information is acquired based on the medical event information including the newly added examination result information, and “P345-Rheumatoid Arthritis” is the problem. The related information database is stored in association with information related to the date and time together with the grounds for extracting the related information of the problem. Here, since it is already based on the result of the close inspection, “P001-suspect” is not attached. Next, the active program extracting logic of the active program extracting unit extracts the latest program related information from the program related information database, and stores it in the active program database together with the grounds for extracting the program related information and the date and time information.
Furthermore, automatic diagnosis logic is activated and refers to the active problem database and, if necessary, the medical event information database. According to the automatic diagnosis knowledge base, the automatic diagnosis logic diagnoses “rheumatoid arthritis” with a predetermined certainty factor. The diagnosis name at this time is acquired with a relatively high certainty as judged from the grounds for extracting the problem related information, and is stored in the diagnosis name list database in association with the acquisition date and time. Here, the necessary examination / finding extraction logic recognizes that there is no examination necessary to estimate “rheumatoid arthritis” because the examination order already performed is stored in the clinical event information database. Also does not work.

(Example 3)
An example of the automatic diagnosis based on the information stored in the prescription and injection order database is shown. The automatic diagnosis is a certainty of extracting relevant information of a problem based on information on a drug administered for the diagnosis name or symptom when a drug that is used only for a patient having a specific diagnosis name or symptom is administered. An embodiment in which the specific gravity is increased is shown.

(Example 3-1)
For example, if a patient is prescribed “Sampiro” and an injection order, “Sampiro” can only be used for glaucoma. Therefore, the problem-related information extraction knowledge base includes “Sampiro” and “Glaucoma” as problem-related information. Are associated, and “Sampiro”, “Glaucoma”, and high confidence are associated with the automatic diagnosis knowledge base.
According to the system flow of the present invention, when “Sampiro” is stored in the diagnostic event information database in the information conversion unit after the prescription and the injection order are input, the problem related information extraction unit extracts “glaucoma” as the problem related information. Is extracted, and through the active problem extraction unit, the automatic diagnosis unit extracts “glaucoma” as a diagnosis name with a high certainty factor.

(Example 3-2)
Next, the case of diabetes will be described.
Diabetes is usually determined based on the blood test result by comparing test values such as blood glucose level and hemoglobin Alc to determine whether or not “diabetes” is true. However, when insulin or the like is administered to the patient and appropriate control is performed, the test value shows a normal value, so it is difficult to determine that the patient has diabetes.
In the system of the present invention, for example, when insulin prescription and injection order are made to a patient, the problem-related information extraction knowledge base associates “insulin” and “diabetes” as problem-related information. The knowledge base is associated with “insulin”, “diabetes” and high confidence.
According to the system flow of the present invention, when “insulin” is stored in the medical event information database in the information conversion unit after the prescription of “insulin” and the injection order are input, “diabetes” is stored in the problem related information extraction unit. Is extracted, input to the active problem extraction unit, and stored in the active problem database via the active problem extraction logic. Next, the automatic diagnosis unit extracts “diabetes” as a diagnosis name with a high certainty factor.

(Example 3-3)
Next, the case of liver dysfunction will be described.
If the test value of the PT value, which indicates the blood coagulation value based on the blood test result, is extended with reference to the normal reference value, “liver dysfunction” can be considered with high confidence as problem-related information . However, when warfarin is administered, the PT value is prolonged due to the effect of the administration, so the certainty of “liver dysfunction” is relatively low as the above-mentioned problem related information.
In the system of the present invention, for example, when blood test orders, warfarin prescriptions, and blood test results are input to the patient, each order and blood test results are transmitted in a predetermined format or numerical value via the information conversion unit. It is stored in the clinical event information database together with the unit in the form. Problem-related information extraction In the knowledge base, "liver dysfunction" is associated with "extended PT value" as problem-related information. For example, the problem-related information extraction logic is triggered by the input to the clinical event information database In this case, even if “extended PT value” is stored as a blood test result in the medical event information database, if administration of “warfarin” is permitted as prescription information, the problem related information extraction logic is “ Recognizing that the certainty of “liver dysfunction” is low, “liver dysfunction” is not extracted as problem-related information. Thus, it is possible to apply a filter so as to extract only the problem related information having a certain degree of certainty (preliminarily registered in the problem related information extraction knowledge base).

Example 4
The warning example in the warning part based on the basic information of a patient, the problem related information stored in the active problem database, and the medical event information stored in the medical event information database is shown.
(Example 4-1-1)
Example of warning based on the relationship between diagnosis name and drug information When "Glaucoma" is entered in the diagnosis name order in the medical information section, it is stored along with the date and time information in the problem related information database via the diagnosis name order information conversion logic. The Further, through the process in the active problem extraction unit, the information on the “glaucoma” and the date and time is stored in the active problem database. In this case, it is assumed that the doctor orders a prescription for squirrel modern. Then, in the system flow of the present invention, in the prescription and injection order information conversion logic in the information conversion unit from the input to the prescription and injection order database, the “rishy modern” prescription information is a group to which the drug belongs. It is stored in the clinical event information database in association with the “cholinergic drug”. Next, when the warning logic is activated and the presence of the “glaucoma” information in the active problem database and the “anticholinergic drug” information in the medical event database is confirmed, it is registered in the warning information knowledge base in advance. Contraindication information related to "glaucoma" and "anticholinergic drug" is extracted, and "rislith modern" of "anticholinergic drug" that is contraindicated for "glaucoma" is prescribed. Warning. The warning information is stored in the warning recording database together with the date and time when the warning information is issued, and is displayed by a screen display or the like.

(Example 4-1-2)
Example of warning based on relationship between state name and drug that may be affected When doctor orders blood test for patient and obtains the test result, in the system of the present invention, the test order and test result It passes through the information part and is stored in the medical care event information database of the information conversion part. Next, the problem related information extraction logic is activated, the problem related information is extracted based on the blood test result information such as GOP, GPT, γ-GTP, etc., and the extracted problem related information is passed through the active problem extraction unit to the active problem database. To store. For example, if the problem related to the liver is included in the information related to the problem stored in the active problem database, and the doctor orders the prescription for “Melvin tablet”, the “Melvin tablet” From the input to the prescription and injection order database, the information of the “Melvin tablet” is stored in the medical event information database through the prescription and injection order logic of the information conversion unit. At that time, when the warning logic is activated and the presence of “Melvin tablets” and “liver dysfunction” is confirmed by referring to the clinical event information database and the active problem database, it is registered in advance in the warning information knowledge base. Contraindication information in which “tablet” and “liver dysfunction” are associated is extracted, and a warning is given that “melvin tablet”, which is a contraindicated drug for “liver dysfunction”, is about to be administered. The warning information is stored in the warning recording database together with the date and time when the warning information is issued, and is displayed by a screen display or the like.

(Example 4-2)
An example of warning based on an active problem and an inspection order is shown.
In order for a doctor to carry out an examination when “sub-dural blood species” is extracted in the problem-related information by the active problem extraction part via the medical information part, the information conversion part and the problem-related information extraction part Suppose you have an inspection order for “lumbar puncture”. At this time, as the system flow of the present invention, the information on the “lumbar puncture” is stored in the clinical event information database from the input to the test order database through the test order information conversion logic in the information conversion unit. Next, the warning logic is activated, and the presence of “subdural blood type” information and “lumbar puncture” information is confirmed by referring to the active problem database and the medical event information database. Warning logic extracts warning information that associates “subdural blood species” and “lumbar puncture” registered in advance in the above warning information knowledge base, and if “lumbar puncture” is performed, it can become “brain hernia” Warning that there is a sex. The warning information is stored in the warning record database together with the date on which the warning information is issued, and is displayed by screen display or the like.

(Example 4-3)
Examples of warnings for prescription drugs are shown.
For example, information such as drug name (or drug group name), dose, administration period, and administration time interval related to a drug prescribed by a doctor for a certain problem-related information is passed through the medical information section to the medical event of the information conversion section. When stored in the information database, each medical event information can be related to date and time easily in each information conversion logic unit, so that the deadline management can be easily performed. The warning logic refers to the medical event information database, and the warning Administration of the drug by comparing the standard dose and the standard administration time interval according to, for example, age, etc., with the dose and administration time interval of the drug actually ordered for prescription registered in the information knowledge base If it is determined that the amount is out of the standard or the administration time interval is out of the specified range, a warning to that effect is given. Moreover, the warning logic can manage whether or not the administration period is correctly administered by monitoring the administration date and time with respect to the drug with reference to the medical event information database. If a prescription order for the drug has been issued beyond the prescribed administration period, a warning to that effect is given. The warning information is stored in the warning recording database together with the date and time when the warning information is issued, and is displayed by a screen display or the like.

(Example 4-4)
An example of warning of renal function decline based on test results and basic information is shown.
As an index indicating normal or abnormal renal function, there is a measurement method called creatinine clearance (CCR value). Creatinine clearance is originally calculated by using a technique such as a 24-hour method for storing urine, but by using a simple formula, an approximate value can be obtained from gender, age, weight, and CR value. it can. However, the CR value is a value indicating the amount of creatinine in blood.
In the system flow of the present invention, when a doctor orders a blood test and the blood test result is input to the test result database, the test result information conversion logic is activated. The test result information conversion logic includes the CR value extracted from the test results stored in the test result database and the individual sex, age and weight of the patient extracted from the basic information database or directly input by the input means. Based on this, a CCR value is calculated and generated. The generated CCR value is converted in a predetermined format or stored in the medical event information database as it is without being converted. On the other hand, the warning logic refers to a predetermined logic registered in advance in the warning information knowledge base, for example, a logic for detecting an abnormality based on, for example, comparison of differences in creatinine clearance values calculated in the past, A value warns that the patient's renal function is impaired. The warning information is stored in the warning recording database together with the date and time when the warning information is issued, and is displayed by a screen display or the like. Further, as a modification, it is possible to design the CCR calculation process so as to be performed by warning logic.

(Example 5)
An example of extracting specific problem related information is shown.
An example of extracting problem-related information using the creatinine clearance (CCR value) will be described.
As shown in Example 4-4, after the CCR value is calculated by the test result information conversion logic and stored in the medical event information database, the problem related information extraction logic is triggered by the input to the medical event information database. It is activated. The problem related information extraction logic determines whether the CCR value is normal or abnormal based on, for example, a general CCR normal value range registered in advance in the above problem related information extraction knowledge base. Can output “renal dysfunction” as problem-related information and store it in the problem-related information database.

(Example 6)
A specific example of extracting a diagnosis name from a plurality of pieces of active problem information in the automatic diagnosis unit will be described.
For example, as active problem information,
“P611-Difficulty breathing during work”
“P621 – Listening to the La Sounds by Auscultating the Breathing Sound”
"P634-Heart expansion"
"P642-Congestion in the lung field"
In the case of a patient having the above, in the automatic diagnosis unit, “left heart failure” and a predetermined certainty factor are extracted as a diagnosis name with reference to data in the automatic diagnosis knowledge base, and are written in a diagnosis name list database together with information on date and time.

As described above, the medical care support system according to the present invention is based on the various medical information stored in the medical care information section, the problem related information including the diagnosis name or the state name that may be affected, and the diagnosis showing the certainty level. Since a warning based on the correlation between the name and various medical event information can be output, the diagnosis name can be obtained with high accuracy and medical errors are unlikely to occur.
In addition, by performing automatic diagnosis of the diagnosis name multiple times, it is possible to request a necessary examination based on the outputted problem related information and add new medical information, so the accuracy of diagnosis name estimation is improved. Can be improved.

Overall configuration diagram of a system according to the present invention

Claims (20)

  1. A condition part that stores predetermined information that is a matching condition with the input information, and a predetermined information that is output corresponding to the condition part when the input information corresponds to a matching condition that is stored in the condition part. It has a knowledge base consisting of stored processing units,
    Diagnostic name order information, examination record information obtained by examination conducted on the patient, nursing record information obtained at the time of nursing, examination item information and examination result information regarding the instructed examination, and administration are instructed. When medical information including any of prescription information and injection information related to medicine, image report information using any of radiation, endoscope, magnetic resonance, and ultrasound, and information related to surgery and treatment is input, With reference to the condition part of the knowledge base, when the input medical information corresponds to the matching condition of the condition part, the processing unit of the knowledge base is referred to and the medical information other than the diagnosis name order information Information converting means for outputting as diagnostic event information including predetermined information of the processing unit corresponding to the condition unit and storing it in the medical event information database ;
    When the diagnosis event information is input, the condition part of the knowledge base is referred to, and when the stored medical event information corresponds to the matching condition of the condition part, the processing unit of the knowledge base is changed. A program related information extracting means for extracting program related information including a diagnosis name or a patient state that may be affected as predetermined information of the processing unit corresponding to the condition unit and storing the extracted information in a program related information database When,
    Hints, to the outputted medical event information, clinical support system for the information on the outputted date or time, characterized in that the information and is associated about medical event information specific expiration date.
  2. When the diagnosis name order information is input, the information conversion means refers to the condition part of the knowledge base, and when the input diagnosis name order information satisfies the matching condition of the condition part, Refer to the processing unit, and output as the problem related information indicating the information of the diagnosis name which is the predetermined information of the processing unit corresponding to the condition unit, and the output problem related information relates to the output date or time The system according to claim 1, wherein the information is associated with information related to an expiration date unique to the problem related information.
  3. The information conversion means recognizes at least a part of character information in medical examination record information, nursing record information or image report information using a character recognition technique, and inputs the recognized at least part of character information. the system according to claim 1 or 2, wherein the knowledge base refers to the, and outputs the information of synonymous as a unique clinical event information.
  4. For at least one of the examination result information, prescription information, and injection information of the medical information, the information conversion means outputs the same information as each inputted medical information as medical event information in the case of a numerical format or a polarity code. The system according to any one of claims 1 to 3, wherein
  5. It said information converting means A system according to any one of claims 1 to 4, characterized in that to produce a medical event information by the medical information and basic information of the patient.
  6. The Problem related information extracting means, the system according to any one of claims 1 to 5, characterized in that associating the Problem related information on the information on the date or time and the expiration date.
  7. And Problem related information extracted by the Problem related information extracting means, on the basis of the information about the date or time and the expiration date associated therewith, claim further comprising an active Problem extracting means to extract the latest Problem related information 1 The system in any one of 6 thru | or 6 .
  8. The system according to claim 7 , wherein the active problem extracting unit has a function of storing the latest problem related information in an active problem database.
  9. When the latest Problem related information is input, queries the condition part of the knowledge base, the latest Problem related information that is the stored, said stored by being medical event information, or combinations thereof The case where the patient does not exist corresponds to the matching condition of the condition part, and the predetermined information of the processing part corresponding to the condition part is referred to the processing part of the knowledge base . The system according to claim 7 or 8 , further comprising necessary inspection / finding extraction means for outputting inspection items or findings information.
  10. The system according to claim 9 , wherein the necessary inspection / finding extraction unit has a function of storing the output required inspection items or findings information in a necessary inspection / finding database.
  11. When at least one of the problem related information and the medical event information is input, the stored knowledge related information, the stored medical event information, or these are referred to the condition part of the knowledge base When the combination of the above corresponds to the matching condition of the condition part, the processing unit of the knowledge base is referred to, and diagnosis name information having a possibility of being illness is predetermined information of the processing part corresponding to the condition part. a system according to any one of claims 1 to 10, characterized by further comprising an automatic diagnosis unit which extracts.
  12. The system according to claim 11 , wherein the automatic diagnosis unit has a function of storing the extracted diagnosis name information in a diagnosis name information database.
  13. When at least one of the problem-related information and the medical event information is input , the automatic diagnosis means refers to the condition part of the knowledge base, stores the stored problem-related information, and stores the stored medical information. When the event information or a combination thereof corresponds to the matching condition of the condition part, the susceptibility which is the predetermined information of the processing part corresponding to the condition part with reference to the processing part of the knowledge base The system according to claim 11 or 12 , wherein the certainty level is output together with diagnosis name information associated with a certain diagnosis name.
  14. The system according to claim 13 , wherein the automatic diagnosis unit has a function of storing the output diagnosis name information or both the diagnosis name information and the certainty factor in a diagnosis name information database.
  15. The automatic diagnosis unit, the system according to any one of claims 11 to 14, characterized in that it has a function of storing the diagnosis information to the Problem related information database.
  16. When at least one of the problem-related information and the medical event information is input , the stored knowledge- related information, the stored medical event information, or their information is referred to the condition part of the knowledge base . When a combination corresponds to the matching condition of the condition part, the information processing apparatus further includes warning means for referring to the processing part of the knowledge base and extracting warning information that is predetermined information of the processing part corresponding to the condition part. The system according to any one of claims 1 to 15 , characterized in that:
  17. The system according to claim 16 , wherein the warning means has a function of storing the extracted warning information in a warning record database.
  18. The warning means refers to the condition part of the knowledge base when the clinical event information related to the dose or administration time interval of the actually instructed administration is input, and refers to the reference dose or reference administration for the drug. If the time interval is compared with the dose or time interval of the drug that was actually instructed to be administered, and it is determined that the drug dose is out of the standard or the administration time interval is outside the specified range When the matching condition of the condition part is met, warning information which is predetermined information of the processing part corresponding to the condition part is output with reference to the processing part of the knowledge base. Item 18. The system according to Item 16 or 17 .
  19. Said warning means, when the problem-related information, or at least one of the plurality of information of the medical event information is input, the knowledge with reference to the base, contraindication information associated with inter-input information mutually, between the input information mutually Warning information indicating that there is a possibility of inducing a diagnosis name or a patient's state associated with, and an abnormal value by comparing the input information of 1 and the calculated value information based on other input information system according warning information output, and, in any one of claims 16 and outputs at least one of warning information indicating that the input information is out of the reference value 18 if the.
  20. The problem-related information extracting means is for outputting problem-related diagnosis name or patient status related information related to at least one medical event information among a plurality of input medical event information. However, if at least one of other diagnosis event information related not to output the diagnosis name or the problem related information is input , the problem related diagnosis name or the patient related problem related information The system according to any one of claims 1 to 19 , wherein no extraction is performed.
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