JP2014191638A - Information processing method, device and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To assist a patient in selecting an appropriate diagnosis and treatment department.SOLUTION: An information processing method includes the processes to: acquire one or more combination of a human body part and a symptom appearing thereon with respect to a patient (A); extract a name of a diagnosis and treatment department associated with data including one or more combination of the human body part and the symptom appearing thereon belonging to a predetermined similar field to the acquired one or more combination from a data storage section storing plural pieces of data including one or more combination of the human body part and the symptom appearing thereon as well as the name of the diagnosis and treatment department (B); and output the extracted name of the diagnosis and treatment department (C).

Description

  The present invention relates to a technique for recommending a department name to a patient.

  Conventionally, a patient determines which department to go to according to his / her symptoms, and searches for a medical institution having the corresponding department. In recent years, using the Internet, there are many options such as a nearby clinic or a remote hospital. When a patient visits a medical institution, it is common to first have his / her symptom entered at the reception desk. In relatively large hospitals that have multiple departments, rather than a clinic with only a specific department, an operation flow may be implemented in which the patient is guided to the desired department after completing the questionnaire. Many.

  However, since medical technology has a high level of expertise, patients often cannot accurately determine the department to be examined based on their symptoms. For this reason, there is a case where the first desired department is not diagnosed with the name of the wound, but is examined in a plurality of departments. In addition, if the medical institution you visit is a non-specialty symptom, it is difficult to perform appropriate medical practices (diagnosis, examination, judgment, treatment, etc.) for the patient, so refer the patient to another medical institution. There are cases where this will happen.

  In addition, if there is no medical department that the patient wants to see, he / she will determine which department the staff and nurses should consult based on the contents of the interview and the patient's condition, etc. In many cases, an operational flow of guidance is implemented.

  However, staff and nurses vary in their skills for judgment as described above, and it takes time to make judgments. A case occurs in which a medical examination is received at the clinical department.

  When these cases occur, there is a possibility that the patient will receive medical examinations at a plurality of departments, and appropriate treatment will be delayed and symptoms may worsen. This also leads to a decrease in patient satisfaction. Furthermore, on the medical institution side, doctors examine patients who are not their own specialty, so that in-hospital medical resources cannot be used effectively.

  There is a technique that presents an appropriate action guideline when a patient designates his / her site and a symptom for the site. This action guideline includes emergency medical services, recommended tests, appropriate types of doctors, specific doctors to consult with, and the like. However, what kind of database and processing is performed is unknown.

  There is also a technique for providing a list of hospitals and medical subjects that can be used to diagnose and treat illness candidates obtained by simple diagnosis by constructing a simple diagnosis database. However, a simple diagnosis database is appropriately constructed in advance by a doctor or the like.

  In addition, when the inquiry screen is sequentially expanded and displayed and the corresponding inquiry answer is received from the inquiry screen, the symptom code corresponding to the inquiry answer is stored, the stored symptom code and the pre-stored disease name determination data, There is also a technique for estimating disease names by comparing the two. However, the reason why the interview screens are sequentially developed is that a story is prepared in advance for narrowing down the name of the disease, and the answer is made in accordance with the story. Symptoms that deviate from the story are not considered. In addition, in order to create this story, a detailed examination by a doctor or the like is performed in advance.

  As described above, the conventional technology is premised on a large involvement by specialists such as doctors.

US Pat. No. 6,383,135 JP 2003-256580 A JP 2004-62563 A JP 2002-169885 A JP 2002-73807 A

  According to one aspect, the present invention provides a technique for assisting a patient in selecting an appropriate department.

  In the information processing method according to the present technology, (A) for a patient, one or more combinations of a body part and a symptom for the body part are acquired; and (B) a symptom for the body part and the body part From a data storage unit that stores a plurality of data including one or a plurality of combinations of the above and a department name, a body part belonging to a predetermined similar range from the one or a plurality of combinations acquired and a symptom for the body part It includes a process of extracting a department name for data including one or a plurality of combinations, and (C) outputting the extracted department name.

  According to one aspect, the patient can be helped to select an appropriate department.

FIG. 1 is a diagram showing an overview of a system according to the present embodiment. FIG. 2 is a functional block diagram of the information processing apparatus. FIG. 3 is a diagram illustrating an example of data stored in the part DB (database). FIG. 4 is a diagram illustrating an example of data stored in the symptom DB. FIG. 5 is a diagram illustrating an example of data stored in the department name DB. FIG. 6 is a diagram showing a processing flow of processing according to the present embodiment. FIG. 7 is a diagram illustrating a process flow of the classification process. FIG. 8 is a diagram illustrating an example of the first group list. FIG. 9A is a diagram illustrating an example of a list of the 2-1 group. FIG. 9B is a diagram illustrating an example of a list of the 2-2 group. FIG. 9C is a diagram illustrating an example of a list of the 2-3 group. FIG. 10 is a diagram illustrating an example of the fourth group list. FIG. 11A is a diagram illustrating an example of a 3rd-1 / 9th list. FIG. 11B is a diagram illustrating an example of a 3rd-1 / 6 list. FIG. 12 is a diagram illustrating a processing flow of the ranking generation process. FIG. 13 is a diagram illustrating an example of data generated in the ranking generation process. FIG. 14 is a diagram illustrating an example of the overall similarity for each medical department. FIG. 15 is a diagram illustrating an example of the sorting result. FIG. 16 is a diagram showing a processing flow of processing according to the present embodiment. FIG. 17 is a diagram illustrating an example when the medical department ranking is changed. FIG. 18 is a diagram showing a processing flow of processing according to the present embodiment. FIG. 19 is a functional block diagram of a computer.

  An example of a system according to an embodiment of the present technology is illustrated in FIG. A plurality of terminal devices 7 (7a and 7b in FIG. 1) for patients are connected to the network 1 in the hospital by wire or wirelessly. To receive recommendations from the department. The network 1 is connected to an information processing apparatus 3 that executes main processing in the present embodiment and a plurality of doctor terminals 5 (5a and 5b in FIG. 1) operated by a doctor.

  For example, while communicating with the information processing apparatus 3, the terminal device 7 presents the body part to the patient, accepts designation of the problematic part, and inputs or selects a symptom for the designated part. This may be repeated to specify a plurality of combinations of parts and symptoms.

  The information processing device 3 performs processing described below to generate a list of medical department names and display the list on the terminal device 7. In accordance with the list of medical department names displayed on the terminal device 7, the patient selects a medical department to receive medical examination. The data of the combination of the part and the symptom is stored in the information processing apparatus 3 in association with the identifier of the user. When the doctor finally inputs the disease name using the doctor terminal 5, the name of the medical department to which the doctor belongs is determined. In association with the combination of the part and the symptom, it is stored as new data in the database of the information processing apparatus 3.

  A functional block diagram of the information processing apparatus 3 is shown in FIG. The information processing apparatus 3 includes an inquiry result acquisition unit 31, a part DB 32, a symptom DB 33, a first data storage unit 34, an extraction unit 35, a second data storage unit 36, a medical department name DB 37, and a DB update. Part 38.

  The inquiry result acquisition unit 31 communicates with the terminal device 7 to acquire an input pattern including one or more combinations of parts and symptoms (an individual combination is also referred to as an inquiry result), and associates it with a patient identifier. The data is stored in one data storage unit 34. At this time, the inquiry result acquisition unit 31 presents options and the like to the user using the data stored in the part DB 32 and the symptom DB 33.

  An example of data stored in the part DB 32 is shown in FIG. In the example of FIG. 3, the part identifier and the part name are associated with each other. In addition, you may make it hold | maintain the figure and image data corresponding to each site | part.

  An example of data stored in the symptom DB 33 is shown in FIG. In the example of FIG. 4, a symptom identifier and a symptom name are associated with each other.

  Further, the extraction unit 35 extracts from the clinical department name DB 37 the medical department name associated with the medical examination result similar to the medical examination result stored in the first data storage unit 34, and the second data storage unit 36. To store. Then, the extraction unit 35 transmits a list of medical department names to the terminal device 7. The department name DB 37 stores data as shown in FIG. 5, for example. In the example of FIG. 5, a record ID, one or a plurality of combinations of a part and a symptom, and a department name are associated with each other. When a patient visits a plurality of medical departments, the medical department to which the doctor who finally inputs the disease name belongs is registered.

  For example, when the doctor inputs the disease name as a result of examining the patient in cooperation with the electronic medical record system, the DB update unit 38 receives the identifier of the patient and the identifier of the clinical department to which the doctor belongs from the doctor terminal 5, From the first data storage unit 34, one or a plurality of combinations of a part and a symptom stored in association with the identifier of the patient is read out and registered in the medical department name DB 37.

  Next, the operation of the system shown in FIG. 1 will be described with reference to FIGS. For example, a patient or hospital receptionist enters the terminal device 7 and enters the patient identifier of a patient whose medical department is unknown. The patient identifier may be input after being newly given. If it does so, the terminal device 7 will receive the input of a patient identifier, and will transmit to the information processing apparatus 3 (step S1). When receiving the patient identifier (step S3), the inquiry result acquisition unit 31 of the information processing device 3 generates site selection display data using the data stored in the site DB 32 and transmits it to the requesting terminal device 7. (Step S5).

  On the other hand, when receiving the part selection display data from the information processing device 3, the terminal device 7 displays the data on the display device of the terminal device 7 (step S7). For example, selectable parts are listed, or parts can be selected and illustrated.

  The patient then selects the body part that is the reason for the visit. The terminal device 7 receives the part selection by the patient, and transmits the identifier of the selected part to the information processing apparatus 3 (step S9). The inquiry result acquisition unit 31 of the information processing device 3 receives a part identifier as a part selection result from the terminal device 7, and stores the part identifier in association with the patient identifier in the first data storage unit 34 (step S11).

  Further, the inquiry result acquisition unit 31 of the information processing device 3 generates symptom candidate display data using data stored in the symptom DB 33 and transmits the symptom candidate display data to the requesting terminal device 7 (step S13). The terminal device 7 receives the symptom candidate display data from the information processing device 3 and displays it on the display device (step S15). The symptom candidate display data includes, for example, a list of selectable symptoms.

  The patient selects the appropriate symptom. At this time, it is further instructed whether to input a combination of a part and a symptom or to complete the input. Then, the terminal device 7 receives selection of the corresponding symptom, and transmits an instruction indicating whether or not to continue the input and the identifier of the symptom to the information processing device 3 as a symptom selection result (step S17).

  The inquiry result acquisition unit 31 of the information processing device 3 receives the symptom identifier as a symptom selection result from the terminal device 7 together with an instruction indicating whether or not to continue the input, and the symptom identifier is associated with the patient identifier in the first manner. The data is stored in the data storage unit 34 (step S19). And the inquiry result acquisition part 31 confirms the instruction | indication showing whether it is an input continuation, and judges whether the input of the combination of a site | part and a symptom is performed further (step S21). Furthermore, when inputting the combination of a site | part and a symptom, a process returns to step S5. On the other hand, when the input is completed without continuing the input for the combination of the part and the symptom, the inquiry result acquisition unit 31 instructs the extraction unit 35 to perform the process.

  The extraction unit 35 uses the inquiry result stored in the first data storage unit 34 in response to an instruction from the inquiry result acquisition unit 31, and records (also referred to as data blocks) stored in the medical department name DB 37. The classification process is executed (step S23). The classification process will be described with reference to FIGS. 7 to 11B.

  Here, it is assumed that the input pattern includes n combinations of parts and symptoms. In addition, it is assumed that there are N combinations of parts and symptoms in the record to be compared.

  First, the extraction unit 35 selects one unprocessed record in the medical department name DB 37 and reads the record (FIG. 7: step S31). Then, the extraction unit 35 determines whether or not the combination of the part and the symptom in the selected record completely matches the input pattern (step S33). It is determined whether the number n of combinations of parts and symptoms in the input pattern matches the number N of combinations of parts and symptoms in the selection record, and the combination itself is completely matched.

  When the input pattern completely matches the combination of the part and the symptom in the selection record, the extraction unit 35 classifies the selection record into the first group (step S35). Then, the process proceeds to step S47 via the terminal B. For example, in the second data storage unit 36, the selected record is added to the list of the first group. For example, the first group of data as shown in FIG. 8 is stored in the second data storage unit 36. In the example of FIG. 8, the record ID is associated with the medical department name of the record.

  On the other hand, when the input pattern does not completely match the combination of the part and the symptom in the selection record, the extraction unit 35 determines whether or not the combination of the part and the symptom in the selection record includes the input pattern (step) S37). The number N of combinations of parts and symptoms in the selection record is larger than the number n of combinations of parts and symptoms in the input pattern, that is, N> n, and all combinations of parts and symptoms in the input pattern are It is determined whether or not it is included in the combination of the part and the symptom in the selection record. For example, if the combination of the part (for example, A) and the symptom (for example, a) in the input pattern is “Aa”, “Bb”, and “Cc”, the combination of the part and symptom in the selected record is “Aa”, “Bb”, and “Cc”. "Dd", this condition is satisfied.

  When the combination of the part and the symptom in the selection record includes the input pattern, the extraction unit 35 classifies the selection record into the 2-x group according to the number x of the extra inquiry results in the selection record ( Step S39). Then, the process proceeds to step S47 via the terminal B.

  For example, data as illustrated in FIGS. 9A to 9C is stored in the second data storage unit 36. FIG. 9A shows an example of the list of the 2-1 group in which the number x of the extra inquiry results is “1”. In the example of FIG. 9A, a record ID, an extra inquiry result (one combination of a part and a symptom), and a clinical department name are registered. When x = 1, since it is used for later processing, an extra inquiry result is registered. FIG. 9B shows an example of the list of the 2-2 group in which the number x of the extra inquiry results is “2”. In the example of FIG. 9B, a record ID and a medical department name are registered. FIG. 9C shows an example of the list of the 2-3rd group in which the number x of extra inquiry results is “3”. In the example of FIG. 9C, the record ID and the name of the medical department are registered. If there are records where x is 4 or more, a list for each corresponding record is generated.

  On the other hand, when the combination of the part and the symptom in the selection record does not include the input pattern, the extraction unit 35 determines whether or not the inquiry results partially match between the selection record and the input pattern. (Step S41). It is determined whether at least one combination among n combinations of the input pattern matches at least one combination among N combinations in the selected record, or does not match at all.

  If the inquiry results for the selected record and the input pattern do not match at all, the extraction unit 35 classifies the selected record into the fourth group (step S45). Since the fourth group is hardly used in later processing, only the number of records may be counted, or a list of corresponding records as shown in FIG. 10 may be used. Then, the process proceeds to step S47.

  On the other hand, if the inquiry results for the selected record and the input pattern partially match, the extraction unit 35 selects the selected record according to the hit rate y calculated by the match rate for the input pattern * match rate for the selected record. Classification into the 3-y group (step S43).

  For example, if the combination of the part and symptom in the input pattern is “Aa”, “Bb”, and “Cc”, and the combination of the part and symptom in the selection record is “Cc”, “Dd”, and “Ee”, the combination “Cc "" Will match. In this case, since n = 3 and there is one matching combination in the input pattern, the matching rate for the input pattern is 1/3. On the other hand, since N = 3 and there is one matching combination in the selected record, the matching rate for the input pattern is 1/3. Therefore, the hit rate is 1/3 × 1/3 = 1/9. Therefore, with such an input pattern and selection record, the selection record is classified into a group with a hit rate of 1/9.

  If N = 4 and the selected record includes a combination of “Bb”, “Cc”, “Dd”, and “Ee”, the matching rate for the selected record is 2/4 = 1/2. The coincidence rate for 1/3 * 1/2 = 1/6.

  In the case of n = 3, in the case of the 2nd-1 group, the matching rate of the input pattern is 1, and the matching rate for the selected record is 3 / (3 + 1) = 3/4. 3/4. In the case of the 2-2nd group, the matching rate for the selected record is 3 / (3 + 2) = 3/5, and the hit rate is also 3/5.

  In the following processing, the record appearance probability according to the hit rate is weighted, and thus the classification according to the hit rate is performed in this way.

  For example, data as illustrated in FIGS. 11A and 11B is stored in the second data storage unit 36. That is, the record ID and the department name are registered in the third 3/9 group. In addition, the record ID and the name of the medical department are also registered in the third １ ／ group.

  After step S43 or S45, the extraction unit 35 determines whether or not an unprocessed record exists in the medical department name DB 37 (step S47). If there is an unprocessed record, the process returns to step S31. If there is no unprocessed record, the process returns to the caller process.

  By performing such processing, record classification as preprocessing is completed.

  Returning to the description of the processing in FIG. 6, the extraction unit 35 executes ranking generation processing using data stored in the second data storage unit 36 (step S <b> 25). Thereafter, the processing shifts to the processing in FIG. The ranking generation process will be described with reference to FIG.

  The extraction unit 35 calculates an appearance probability for each department for each group (FIG. 12: step S51). The total number of records is obtained by summing up the number of records in all groups. Therefore, the probability of appearance can be obtained by counting the number of records associated with the same department name in each group and dividing by the total number of records. For example, data (partial) as shown in FIG. 13 is generated. In the example of FIG. 13, the first group includes records of clinical department names “A”, “B”, and “C”, which are 10 records, 5 records, and 5 records, respectively. If it is 100, the appearance probability is 10%, 5%, and 5%.

  The second group includes an extra combination of one group and two groups, each of which is classified by clinical department name, and the appearance probability is calculated. The hit rate is calculated as described above. The third group is classified for each hit rate, and in this example, only when the hit rate is 1/3, it is classified into the department names “B” and “E”. Note that the similarity is not calculated at this stage.

  Then, the extraction unit 35 calculates the similarity by multiplying each of the calculated appearance probabilities by the hit rate corresponding to the belonging group (step S53). Similarity is calculated as shown in FIG. The hit rate for the first and second x groups may be calculated at this stage.

  Further, the extraction unit 35 adds the similarities for each department and calculates the total similarity (step S55). When the numerical values in the similarity column in FIG. 13 are added for each department, data as shown in FIG. 14 is obtained.

  Thereafter, the extraction unit 35 sorts the departments in descending order of overall similarity (step S57). Then, a sort result as shown in FIG. 15 is obtained. Note that only the upper predetermined number of medical departments determined in advance may be extracted. Then, the process returns to the caller process.

  If it does so, it will transfer to the process of FIG. 16 via the terminal A, and the extraction part 35 will classify the record contained in 2nd-1 group (namely, when x = 1) by the combination of a site | part and a symptom ( Step S61). For example, it is assumed that the first classification “Aa” “Bb” “Cc” “Dd” and the second classification “Aa” “Bb” “Cc” “Ee” are obtained.

  And the extraction part 35 specifies one unprocessed classification (step S63). Further, the extraction unit 35 sets the specified classification as a temporary input pattern (step S65). When the first classification is specified, “Aa”, “Bb”, “Cc”, and “Dd” are provisional input patterns. And the extraction part 35 performs a classification process about a temporary input pattern (step S67), and also performs a ranking production | generation process (step S69). These processes are the same as those described above except that the process is for a temporary input pattern. Then, a ranking result as shown in FIG. 15 is obtained separately from FIG.

  Then, the extraction unit 35 determines whether there is an unprocessed classification (step S71). If there is an unprocessed classification, the process returns to step S63. On the other hand, when there is no unprocessed classification, the extraction unit 35 determines the upper predetermined number of medical departments in the ranking result generated in step S25 and the upper predetermined number of medical departments in the ranking result generated in step S69. Then, it is determined whether there is a classification with a change (step S73). For example, when considering up to 3rd place, if the ranking result up to 3rd place for the second classification is as shown in FIG. 17, the 2nd place and the 3rd place are changed, so it changed. Judged to be.

  If there is no classification that changes in the upper predetermined number of medical departments, the process proceeds to the process of FIG. On the other hand, when there is a classification in which there is a change in the upper predetermined number of medical departments, the processing shifts to the processing in FIG.

  The extraction unit 35 generates a question regarding the difference from the input pattern for the classification with the change, and transmits it to the terminal device 7 (FIG. 18: step S75). For example, if the input pattern is “Aa”, “Bb”, “Cc” and the first classification, a combination of a part “Dd” and a symptom is obtained, and therefore “part“ D ”is a symptom“ d ””. Isn't it? Is generated. This makes it possible to give awareness to the patient.

  When receiving the inquiry data from the information processing device 3, the terminal device 7 displays the data on the display device (step S77). Then, the patient inputs an answer to the question with Yes or No. Then, the terminal device 7 receives an input of an answer to the question and transmits answer data to the information processing device 3 (step S79).

  The extraction unit 35 of the information processing device 3 receives an answer to the question from the terminal device 7 (step S81). Then, the extraction unit 35 determines whether the answer to the question is “Yes” and the combination of the part of the difference from the input pattern and the symptom combination has been selected (step S83). When the combination of the difference part and the symptom with the input pattern is selected, the extraction unit 35 stores the input pattern stored in the first data storage unit 34 so as to add the combination of the difference part and the symptom. Is updated (step S85). Further, the extraction unit 35 reads out the ranking result (only the upper predetermined number) corresponding to the classification relating to the selected difference from the second data storage unit 36 and transmits it to the terminal device 7 (step S87).

  On the other hand, if the answer to the question is “No” and the combination of the difference between the input pattern and the symptom is not selected, the extraction unit 35 determines the ranking result (upper predetermined number) generated in step S25. Is read from the second data storage unit 36 and transmitted to the terminal device 7 (step S89).

  The terminal device 7 receives the ranking result from the information processing device 3 and displays it on the display device (step S91). This allows the patient to obtain a list of departments that are preferred to visit from the site and symptoms. Therefore, the department can be selected by this.

  Thereafter, a doctor visits the selected medical department, but the first medical department may perform a medical examination to identify the name of the disease, or may require a different medical department. In any case, finally, a doctor inputs a disease name in the electronic medical record system. In response to the input of the disease name by the doctor, the doctor terminal 5 transmits the name of the medical department of the input doctor in association with the patient identifier to the information processing device 3 (step S93).

  When the DB update unit 38 of the information processing device 3 receives the patient identifier and the department name from the doctor terminal 5, the DB update unit 38 reads the input pattern associated with the patient identifier from the first data storage unit 34, and sets the name to the department name. Correspondingly, it is registered in the clinical department name DB 37 (step S95). By executing such processing, the contents of the medical department name DB 37 are enriched, so that a more appropriate medical department name is presented to the patient. That is, since data is automatically accumulated and the doctor's involvement is only the disease name input when additionally registering in the department name DB 37, the involvement of the specialist is minimized.

  By executing the process as described above, the names of clinical departments ranked by the overall similarity according to the appearance probability of cases weighted according to the degree of coincidence with cases are presented regardless of the patient's response order. It becomes like this.

  It also contributes to improving patient satisfaction. That is, the waiting time of the patient is reduced by the smooth guidance from the staff and nurses to the medical department. In addition, the number of patient visits can be reduced. Specifically, the name of the wound can be accurately determined with a small number of examinations.

  In addition, hospital medical resources can be used effectively. Specifically, the number of patients examined by a doctor can be kept to a minimum, and the number of patients that can be examined by a single doctor increases, thereby improving the profits of the hospital.

  As described above, the appearance probability of the record is weighted by the product (hit rate) of the coincidence degree in the input pattern and the coincidence degree in the record of the medical department name DB 37. As a result, the more records with a high degree of coincidence, the higher the reliability of the record, the higher the degree of similarity, and the higher the ranking of the department for such a record. In addition, although the example which calculates a hit rate by a product was shown, you may make it calculate with a sum or another function.

  Although the embodiment of the present technology has been described above, the present technology is not limited to this. For example, the functional block diagram is an example, and may not match the program module configuration or the file configuration. Further, the inquiry result acquisition unit 31, the site DB 32, and the symptom DB 33 may be provided in the terminal device 7 so that only the final inquiry result is transmitted to the information processing device 3. The information processing apparatus 3 may be implemented by a plurality of computers.

  Further, regarding the processing flow, as long as the processing result does not change, the order of the steps may be changed or a plurality of steps may be executed in parallel.

  Although not additionally registered in the medical department name DB 37, if data is accumulated to some extent in the medical department name DB 37, for example, it may be used by a patient other than the visiting patient on the Internet.

  Further, the process for generating the question may be omitted.

  The information processing device 3 and the doctor terminal 5 described above are computer devices, and as shown in FIG. 19, a memory 2501, a CPU (Central Processing Unit) 2503, and a hard disk drive (HDD: Hard Disk Drive). A bus 2519 connects a display control unit 2507 connected to 2505, a display device 2509, a drive device 2513 for the removable disk 2511, an input device 2515, and a communication control unit 2517 for connecting to a network. An operating system (OS) and an application program for executing the processing in this embodiment are stored in the HDD 2505, and are read from the HDD 2505 to the memory 2501 when executed by the CPU 2503. The CPU 2503 controls the display control unit 2507, the communication control unit 2517, and the drive device 2513 according to the processing content of the application program, and performs a predetermined operation. Further, data in the middle of processing is mainly stored in the memory 2501, but may be stored in the HDD 2505. In an embodiment of the present technology, an application program for performing the above-described processing is stored in a computer-readable removable disk 2511 and distributed, and installed from the drive device 2513 to the HDD 2505. In some cases, the HDD 2505 may be installed via a network such as the Internet and the communication control unit 2517. Such a computer apparatus realizes various functions as described above by organically cooperating hardware such as the CPU 2503 and the memory 2501 described above and programs such as the OS and application programs. .

  The terminal device 7 also has the configuration shown in FIG. 19 simply by using a flash memory instead of the HDD 2505 or by using a removable flash memory drive instead of the drive device 2513.

  The above-described embodiment can be summarized as follows.

  In the information processing method according to the present embodiment, (A) for a patient, one or more combinations of a body part and a symptom for the body part are acquired, and (B) the body part and the body part are obtained. Symptoms on a body part and the body part that are the same as or similar to one or more combinations acquired from a data storage unit that stores a plurality of data blocks including one or more combinations of symptoms and a department name And (C) a process of outputting the extracted clinical department name for a data block including one or a plurality of combinations.

  As a result, the patient can know the name of an appropriate medical department and can efficiently receive a medical examination.

  Note that the data stored in the data storage unit can be stored in association with the acquired data after the final clinical department name is determined at the time of medical examination by a doctor. It is limited to the limit.

  The process of extracting the department name described above (b1) extracts a data block including one or a plurality of combinations that completely or partially match the acquired one or a plurality of combinations, and (b2) is extracted The degree of similarity calculated by weighting the appearance probability of the data block by the ratio of the combination that matches among the acquired one or a plurality of combinations and the ratio of the combination that matches in the extracted data block, You may make it include for every name, and (b3) the process which extracts the 1st medical department name of an upper predetermined number at least in order with the high similarity calculated. By performing such calculation, the appearance probability of the data block can be appropriately weighted.

  Furthermore, before the information processing method described above outputs the extracted department name, (D) a data block including one or more acquired combinations and an extra second combination is included. Extracting from the data storage unit, (E) Assuming that the second combination is acquired in addition to the acquired one or a plurality of combinations, a process for extracting the department name is executed and the second combination is executed. A process of extracting a department name and (F) outputting a question regarding the second combination when there is a change in at least a part between the first department name and the second department name may be included. . In this way, the patient can be aware of the combination of forgotten parts and symptoms.

  Note that the information processing method described above is an answer that (G) the second combination is additionally acquired in the acquired one or more combinations in response to the question regarding the second combination. In this case, a process for outputting the second medical department name may be further included. Since the ranking changes, a more appropriate department name can be selected by presenting the changed ranking.

  Note that a program for causing a computer to execute the processing described above can be created, and the program includes, for example, a flexible disk, an optical disk such as a CD-ROM, a magneto-optical disk, and a semiconductor memory (for example, ROM). Or a computer-readable storage medium such as a hard disk or a storage device. Note that data being processed is temporarily stored in a storage device such as a RAM.

  The following supplementary notes are further disclosed with respect to the embodiments including the above examples.

(Appendix 1)
For a patient, obtain one or more combinations of body parts and symptoms for the body parts,
It belongs to a predetermined similar range from the one or more combinations acquired from the data storage unit that stores a plurality of data including one or more combinations of a body part and a symptom for the body part and a department name. , Extract the department name for data containing one or more combinations of body parts and symptoms for the body parts,
A program for causing a computer to execute a process of outputting the extracted department name.

(Appendix 2)
The process of extracting the department name is
Extracting data including one or more combinations that completely or partially match the acquired one or more combinations;
Similarity calculated by weighting the appearance probability of the extracted data by the proportion of matching combinations among the acquired one or more combinations and the proportion of matching combinations in the extracted data For each department name,
The program according to supplementary note 1, including a process of extracting at least a predetermined number of first medical department names in descending order of the calculated similarity.

(Appendix 3)
Before outputting the extracted department name,
Extracting the data including the acquired one or a plurality of combinations and including an extra second combination from the data storage unit;
Assuming that the second combination is acquired in addition to the acquired one or more combinations, the second department name is extracted by executing the process of extracting the department name,
The supplementary note 2 for causing the computer to further execute a process of outputting a question regarding the second combination when there is a change in at least a part between the first medical department name and the second medical department name program.

(Appendix 4)
When the second combination is an answer that is additionally acquired to the acquired one or a plurality of combinations in response to the question relating to the second combination, the name of the second medical department The program according to appendix 3, for causing the computer to further execute a process of outputting.

(Appendix 5)
For a patient, obtain one or more combinations of body parts and symptoms for the body parts,
It belongs to a predetermined similar range from the one or more combinations acquired from the data storage unit that stores a plurality of data including one or more combinations of a body part and a symptom for the body part and a department name. , Extract the department name for data containing one or more combinations of body parts and symptoms for the body parts,
An information processing method including a process of outputting the extracted department name and executed by a computer.

(Appendix 6)
For a patient, an acquisition unit that acquires one or a plurality of combinations of a body part and symptoms for the body part;
It belongs to a predetermined similar range from the one or more combinations acquired from the data storage unit that stores a plurality of data including one or more combinations of a body part and a symptom for the body part and a department name. An extraction unit that extracts a department name about data including one or a plurality of combinations of a body part and a symptom for the body part, and outputs the extracted department name;
An information processing apparatus.

31 Interview Result Acquisition Unit 32 Site DB
33 Symptom DB
34 First data storage unit 35 Extraction unit 36 Second data storage unit 37 Clinical department name DB
38 DB update section

Claims (6)

For a patient, obtain one or more combinations of body parts and symptoms for the body parts,
The data storage unit stores a plurality of data including one or a plurality of combinations of a body part and a symptom for the body part and a department name, and belongs to a predetermined similar range to the one or a plurality of combinations acquired. , Extract the department name for data containing one or more combinations of body parts and symptoms for the body parts,
A program for causing a computer to execute a process of outputting the extracted department name. The process of extracting the department name is
Extracting data including one or more combinations that completely or partially match the acquired one or more combinations;
Similarity calculated by weighting the appearance probability of the extracted data by the proportion of matching combinations among the acquired one or more combinations and the proportion of matching combinations in the extracted data For each department name,
The program according to claim 1, further comprising: extracting at least a predetermined number of first medical department names in descending order of the similarity. Before outputting the extracted department name,
Extracting the data including the acquired one or a plurality of combinations and including an extra second combination from the data storage unit;
Assuming that the second combination is acquired in addition to the acquired one or more combinations, the second department name is extracted by executing the process of extracting the department name,
3. The method for causing the computer to further execute a process of outputting a question regarding the second combination when there is a change in at least a part between the first clinical department name and the second clinical department name. Program. When the second combination is an answer that is additionally acquired to the acquired one or a plurality of combinations in response to the question relating to the second combination, the name of the second medical department The program according to claim 3, for causing the computer to further execute a process of outputting. For a patient, obtain one or more combinations of body parts and symptoms for the body parts,
It belongs to a predetermined similar range from the one or more combinations acquired from the data storage unit that stores a plurality of data including one or more combinations of a body part and a symptom for the body part and a department name. , Extract the department name for data containing one or more combinations of body parts and symptoms for the body parts,
An information processing method including a process of outputting the extracted department name and executed by a computer. For a patient, an acquisition unit that acquires one or a plurality of combinations of a body part and symptoms for the body part;
It belongs to a predetermined similar range from the one or more combinations acquired from the data storage unit that stores a plurality of data including one or more combinations of a body part and a symptom for the body part and a department name. An extraction unit that extracts a department name about data including one or a plurality of combinations of a body part and a symptom for the body part, and outputs the extracted department name;
An information processing apparatus.

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