A standardized processing method of medical data files

Abstract

The embodiment of the present application relates to a kind of standardization processing methods of medical data file, the method comprises: the data table type of original medical data file is acquired, corresponding first type data is generated;The data table information of original medical data file is acquired, corresponding first data list is generated;According to the standard field corresponding table of pre-set, first data list is replaced with standard field name and is handled to generate corresponding second data list;According to the standard data list template matched with first type data of pre-set, second data list is set to missing item column and is handled to generate corresponding third data list;According to pre-set column data conversion rule, third data list is handled to column data conversion and generates corresponding fourth data list;Fourth data list is output as standardization processing result. By the present application, a large amount of manual operation time can be saved, standardization conversion error rate is reduced, and overall efficiency of standardization conversion is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a standardized processing method for medical data files. Background Technology

[0002] Tabular medical data files are used not only in hospital internal information systems for electronic medical records and medical record filing, but also in the reimbursement processes of various medical insurance programs. Because each insurance institution has different format standards for the medical data files used as reimbursement vouchers, the submitted medical data files are typically standardized manually. This approach has two drawbacks: firstly, manual efficiency limits the ability to efficiently convert batches of files, further delaying reimbursement reviews; secondly, human experience can lead to conversion errors, further causing reimbursement review failures. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of existing technologies by providing a standardized processing method, electronic device, and computer-readable storage medium for medical data files. This method creates corresponding standard field mapping tables, standard data list templates, and column data conversion rules based on the format standards of various insurance structures. The original medical data files' data tables are then standardized based on these mapping tables, templates, and rules. This invention can save significant manual operation time, reduce the error rate of standardization conversion, and improve the overall efficiency of standardization conversion.

[0004] To achieve the above objectives, a first aspect of the present invention provides a method for standardizing the processing of medical data files, the method comprising:

[0005] Obtain the data table type from the original medical data file and generate the corresponding first-type data;

[0006] Obtain the data table information from the original medical data file and generate the corresponding first data list;

[0007] Based on a preset standard field correspondence table, the first data list is processed by replacing the standard field names to generate the corresponding second data list;

[0008] Based on a preset standard data list template that matches the first type of data, the second data list is processed to set missing data columns to generate a corresponding third data list;

[0009] According to the preset column data conversion rules, the third data list is processed to generate the corresponding fourth data list;

[0010] The fourth data list is output as the result of the standardization process.

[0011] Preferably, the standard field correspondence table includes multiple standard field correspondence records; each standard field correspondence record includes standard field name information and multiple corresponding original field name information.

[0012] The standard data list template includes multiple template data column field names;

[0013] The first data list includes multiple first data columns; each first data column includes a first column field name and one or more first column data.

[0014] The second data list includes multiple second data columns; each second data column includes a second column field name and one or more second column data.

[0015] The third data list includes multiple third data columns; each third data column includes a third column field name and one or more third column data.

[0016] The fourth data list includes multiple fourth data columns; each fourth data column includes a fourth column field name and one or more fourth column data.

[0017] Preferably, the step of generating a corresponding second data list by replacing the standard field names of the first data list according to a preset standard field correspondence table specifically includes:

[0018] The first column field name of each of the first data columns in the first data list is recorded as the first field name to be matched;

[0019] For each of the first field names to be matched, a full match query is performed on the standard field name in the standard field correspondence table to obtain the corresponding first standard field name; the number of the first standard field names is unique;

[0020] If the first standard field name is not empty, then the first standard field name is used to replace the corresponding first column field name in the first data list; if the first standard field name is empty, then the corresponding first field name to be matched is recorded as the second field name to be matched.

[0021] For each of the second field names to be matched, a fuzzy matching query of the standard field names is performed in the standard field correspondence table to obtain the corresponding second standard field name; the number of the second standard field names may not be unique.

[0022] If the second standard field name is not empty and has a unique number, then the unique second standard field name is used to replace the corresponding first column field name in the first data list; if the second standard field name is empty, then the corresponding second field name to be matched is recorded as the third field name to be matched; if the number of second standard field names is not unique, then the corresponding second field name to be matched is recorded as the fourth field name to be matched.

[0023] For each of the third field names to be matched, a specified number of data from the first column corresponding to the first data column in the first data list are extracted to form a corresponding first column data group; and the third field name to be matched and the first column data group are input into a preset first standard field name forced matching interface for processing to generate a corresponding third standard field name; if the third standard field name is not empty, the third standard field name is used to replace the corresponding first column field name in the first data list.

[0024] For each of the fourth field names to be matched, a specified number of data from the first column corresponding to the first data column in the first data list are extracted to form a corresponding second column data group; and multiple corresponding second standard field names are combined to form a first approximate field name data group; the fourth field name to be matched, the second column data group, and the first approximate field name data group are input into a preset second standard field name forced matching interface for processing to generate a corresponding fourth standard field name; if the fourth standard field name is not empty, the fourth standard field name is used to replace the corresponding first column field name in the first data list;

[0025] The first data list, after all standard field name replacements have been completed, will be used as the second data list.

[0026] Furthermore, the step of performing a full match query on the standard field name in the standard field correspondence table for each of the first field names to be matched to obtain the corresponding first standard field name specifically includes:

[0027] In the standard field correspondence table, the original field name information that is completely consistent with the first field name to be matched is recorded as the fully matched field name information, and the standard field name information of the record corresponding to the standard field corresponding to the fully matched field name information is used as the corresponding first standard field name; if there is no original field name information in the standard field correspondence table that is completely consistent with the first field name to be matched, then the corresponding first standard field name is set to empty.

[0028] Furthermore, the step of performing a fuzzy matching query on the standard field name in the standard field correspondence table for each of the second field names to be matched to obtain the corresponding second standard field name specifically includes:

[0029] The process involves iterating through all records corresponding to the standard fields in the standard field correspondence table and recording the currently iterated record as the current record. A preset fuzzy matching algorithm is then used to match the standard field name information of the current record with the second field name to be matched, yielding a first matching result. If the first matching result is a match, the standard field name information of the current record is recorded as the corresponding second standard field name, and the process continues to iterate through the next record corresponding to the standard field. If the first matching result is a mismatch, the fuzzy matching algorithm is used to match each original field name information of the current record with the second field name to be matched, yielding a second matching result. If the second matching result is a match, the standard field name information of the current record is recorded as the corresponding second standard field name, and the process continues to iterate through the next record corresponding to the standard field. If the second matching result is a mismatch, the process continues to iterate through the next record corresponding to the standard field.

[0030] After the polling ends, if the number of the second standard field names obtained is 1, then the second field name to be matched is added as the new original field name information to the record corresponding to the standard field that corresponds to the unique first matching information.

[0031] Furthermore, the method also includes:

[0032] When the first standard field name forced matching interface receives the third field name to be matched and the first column data group, it outputs the third field name to be matched, the first column data group, and the standard field correspondence table to the reviewer; and takes the standard field name information that the reviewer uniquely confirms from the standard field correspondence table based on the data characteristics of the first column data group and that matches the third field name to be matched as the corresponding third standard field name.

[0033] Furthermore, the method also includes:

[0034] When the second standard field name forced matching interface receives the fourth field name to be matched, the second column data group, and the first approximate field name data group, it outputs the fourth field name to be matched, the second column data group, and the first approximate field name data group to the reviewer; and takes the second standard field name that the reviewer uniquely identifies from the first approximate field name data group, based on the data characteristics of the second column data group, matches the fourth field name to be matched as the corresponding fourth standard field name.

[0035] Preferably, the step of generating a corresponding third data list by setting missing data columns in the second data list according to a preset standard data list template that matches the first type of data specifically includes:

[0036] Based on the template data column field names in the standard data list template, check all the second column field names in the second data list; if the check finds that there is no second column field name in the second data list that matches the current template data column field name, then record the current template data column field name as the missing template data column field name;

[0037] For each of the missing template data column field names, add a new second data column corresponding to it in the second data list, and set the second column field name of the corresponding new second data column according to the current missing template data column field name;

[0038] Obtain a template field mapping relationship list corresponding to the standard data list template; and set the second column data of each of the new second data columns according to the template field mapping relationship list; the template field mapping relationship list includes multiple template field mapping relationship records; the template field mapping relationship record includes template column field name information and mapping relationship information; the mapping relationship information is specifically a relational expression, and the parameters of the relational expression include one or more of the standard field name information;

[0039] The second data list, which has completed the addition of all new data columns and the setting of column data, will be used as the corresponding third data list.

[0040] Preferably, the step of performing column data transformation processing on the third data list according to preset column data transformation rules to generate the corresponding fourth data list specifically includes:

[0041] The data types of each column in the third data list are confirmed to generate the corresponding first data type; the first data type includes at least text, date, numeric, and logical types.

[0042] Each of the third data columns is polled, and the currently polled third data column is recorded as the current data column; if the first data type corresponding to the current data column is logical, then the original content of each of the third data columns of the current data column is converted into logical values ​​according to the logical value conversion rules of the column data conversion rules; if the first data type corresponding to the current data column is date, then the original content of each of the third data columns of the current data column is converted into date values ​​according to the date value conversion rules of the column data conversion rules.

[0043] The third data list, which has completed the transformation of all corresponding column data according to the column data transformation rules, is used as the corresponding fourth data list.

[0044] A second aspect of the present invention provides an electronic device, including: a memory, a processor, and a transceiver;

[0045] The processor is used to couple with the memory, read and execute instructions in the memory to implement the steps of the method described in the first aspect above;

[0046] The transceiver is coupled to the processor, and the processor controls the transceiver to send and receive messages.

[0047] A third aspect of the present invention provides a computer-readable storage medium storing computer instructions that, when executed by a computer, cause the computer to perform the instructions described in the first aspect.

[0048] This invention provides a method for standardizing medical data files, an electronic device, and a computer-readable storage medium. Based on the format standards of various insurance structures, it creates corresponding standard field mapping tables, standard data list templates, and column data conversion rules. Based on the aforementioned standard field mapping tables, it performs standard field name replacement on the original medical data file's data tables to generate a standard field table, i.e., a second data list. Based on the aforementioned standard data list template, it performs missing data column setting on the second data list to generate a complete standard field table, i.e., a third data list. Finally, based on the aforementioned column data conversion rules, it performs column data conversion on the third data list to generate a complete standard field table with compliant data format, i.e., a fourth data list. This invention not only saves a significant amount of manual operation time but also reduces the error rate of standardization conversion and improves the overall efficiency of standardization conversion. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of a standardized processing method for medical data files provided in Embodiment 1 of the present invention;

[0050] Figure 2 This is a schematic diagram of the structure of an electronic device provided in Embodiment 2 of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0052] The terminal devices or servers of various insurance structure information processing systems use the standardized processing method for medical data files provided in Embodiment 1 of this invention to perform automated standard conversion processing on the data tables of the input original medical data files, thereby achieving high-speed batch conversion with no or minimal human intervention. Figure 1 This is a schematic diagram of a standardized processing method for medical data files provided in Embodiment 1 of the present invention, as shown below. Figure 1 As shown, this method mainly includes the following steps:

[0053] Step 1: Obtain the data table type of the original medical data file and generate the corresponding first type of data.

[0054] Here, because each type of medical data file has a corresponding data table type, such as the first type of data for a physical examination data file being the physical examination table type, and the first type of data for a laboratory test data file being the laboratory test report table type, and so on, and because the table structure corresponding to each table type is different, it is necessary to know the table type corresponding to the original medical data file, that is, the first type of data, when performing standardization conversion on the original medical data file. The first type of data will be used in subsequent steps to confirm the format template of the original medical data file, that is, the standard data list template.

[0055] Step 2: Obtain the data table information from the original medical data file and generate the corresponding first data list;

[0056] The first data list includes multiple first data columns; each first data column includes a first column field name and one or more first column data.

[0057] Here, because the medical data table consists of multiple data objects, each first data column corresponds to one data object, and the first column name of each first data column actually corresponds to the name of the data object. Since some data objects have only one specific object information, such as name, gender, age, address, contact information, examination type, etc., the first data column assigned to such data objects will only include one first column of data. Conversely, some data objects will have multiple records of information, such as daily measurement records of body temperature, blood pressure, etc. in a body temperature table, and the first column assigned to such data objects will include multiple first column data.

[0058] Step 3: Based on the preset standard field correspondence table, perform standard field name replacement processing on the first data list to generate the corresponding second data list;

[0059] The standard field correspondence table includes records corresponding to multiple standard fields; each record includes standard field name information and multiple corresponding original field name information; the second data list includes multiple second data columns; each second column includes second column field names and one or more second column data.

[0060] Here, the standard field mapping table is a table entry set according to the standard field definition rules of the current insurance institution. Each standard field in this table entry corresponds to one standard field in the corresponding record. The standard field name information is the name of the corresponding standard field. There can be multiple original field names, which are different expressions of the corresponding standard field name information. For example, if insurance institution 1 uses "insurance user identifier" to mark the user identity code data object, medical institution hospital 1 uses "ID card number" to mark the user identity code data object, and medical institution hospital 2 uses "ID document number" to mark the user identity code data object, then in the standard field mapping record used for the standard field of the corresponding user identity code data object, the standard field name information can be set to "insurance user identifier", and two original field name information can be assigned accordingly: original field name information 1 is "ID card number" and original field name information 2 is "ID document number".

[0061] Here, the current step is to standardize the field names of the first column of the first data column of all data objects in the first data list according to the standard field correspondence table, and to unify the field names of data tables from different data providers into the standard field names of this organization;

[0062] Specifically, this includes: Step 31, recording the first column field name of each first data column in the first data list as the first field name to be matched;

[0063] For example, if the first data list includes one first data column, named First Data Column 1 [First column field name "ID Number", first column data "xxx1"], then "ID Number" is the first field name to be matched.

[0064] For example, if the first data list includes one first data column, named First Data Column 1 [First column field name "Identity ID", first column data "xxx2"], then "Identity ID" is the first field name to be matched.

[0065] Step 32: For each first field name to be matched, perform a full match query on the standard field name in the standard field correspondence table to obtain the corresponding first standard field name;

[0066] Among them, the number of first standard field names is unique;

[0067] Specifically, this includes: recording the original field name information in the standard field correspondence table that is completely consistent with the first field name to be matched as the full match field name information; and using the standard field name information of the record corresponding to the standard field information that is completely consistent with the first field name to be matched as the corresponding first standard field name; if there is no original field name information in the standard field correspondence table that is completely consistent with the first field name to be matched, then setting the corresponding first standard field name to empty;

[0068] Here, if there is an original field name in the standard field correspondence table that is completely consistent with the first field name to be matched, then the standard field name information corresponding to the original field name information is naturally the most suitable standard field name information that matches the first field name to be matched. In subsequent step 33, the corresponding standard field name information is directly used to replace the corresponding first column field name in the first data list. If there is no original field name information in the standard field correspondence table that is completely consistent with the first field name to be matched, then in subsequent step 33, it will be transferred to the second field name to be matched, and in subsequent step 34, further fuzzy comparison will be performed in the standard field correspondence table in order to find the most suitable standard field name information that matches the second field name to be matched.

[0069] For example, the standard field correspondence table includes one standard field corresponding record as follows: Standard field corresponding record 1 [Standard field name information "Insurance User Identifier", Original field name information 1 "ID Card Number", Original field name information 2 "ID Document Number"];

[0070] If the first field to be matched is "ID number", then the original field name information in the standard field corresponding table that is completely consistent with "ID number" is the original field name information 1. Then, the standard field corresponding record that is the complete match of the original field name information 1 is the standard field corresponding record 1, and the corresponding first standard field name is "insurance user identifier".

[0071] If the first field to be matched is named "Identity ID", then the original field name information that is completely consistent with "Identity ID" in the standard field corresponding table does not exist, so the first standard field name is empty;

[0072] Step 33: If the first standard field name is not empty, then use the first standard field name to replace the corresponding first column field name in the first data list; if the first standard field name is empty, then record the corresponding first field name to be matched as the second field name to be matched.

[0073] For example, the first data list includes a first data column 1 [first column field name "ID number", first column data "xxx1"], and the first standard field name is "insurance user identifier"; then, after replacing the first column field name of the first data column 1 in the first data list with "insurance user identifier", the first data list obtained is: first data column 1 [first column field name "insurance user identifier", first column data "xxx1"];

[0074] For example, if the first data list includes a first data column 1 [first column field name "Identity ID", first column data "xxx2"], and the first standard field name is empty, then the corresponding second field to be matched is named "Identity ID";

[0075] Step 34: For each second field name to be matched, perform a fuzzy matching query on the standard field name in the standard field correspondence table to obtain the corresponding second standard field name;

[0076] The number of second standard field names may not be unique;

[0077] Specifically, the process includes: Step 341, polling all records corresponding to standard fields in the standard field correspondence table, and recording the currently polled record as the current record; then, using a preset fuzzy matching algorithm, comparing the standard field name information of the current record with the second field name to be matched to obtain the corresponding first comparison result; if the first comparison result is a match, recording the standard field name information of the current record as the corresponding second standard field name, and continuing to poll the next record corresponding to the standard field; if the first comparison result is a mismatch, using a fuzzy matching algorithm, comparing each original field name information of the current record with the second field name to be matched to obtain the corresponding second comparison result; if the second comparison result is a match, recording the standard field name information of the current record as the corresponding second standard field name, and continuing to poll the next record corresponding to the standard field; if the second comparison result is a mismatch, continuing to poll the next record corresponding to the standard field.

[0078] Among them, fuzzy matching algorithms include at least the brute force (BF) algorithm;

[0079] Here, the current step polls the records corresponding to each standard field. If the standard field name information or original field name information of the current record can match the second field name to be matched during polling, the standard field name information of the current record is marked as the second standard field name and the process jumps to the next record corresponding to the standard field to continue matching until the last record corresponding to the standard field.

[0080] When comparing the standard field name information or the original field name information with the second field name to be matched according to the preset fuzzy matching algorithm, if the second field name to be matched is a part of the standard field name information, it is considered a match; otherwise, it is considered a mismatch. For example, if the standard field name information is "123", if the second field name to be matched is "1", "2", "3", "12", "23", "123", etc., which are specific substrings of "123", the corresponding comparison result, which is the first comparison result, is a match. If the second field name to be matched is "13", "21", "31", "32", "321", "34", etc., which are neither a substring nor the whole string of "123", the corresponding first comparison result is a mismatch.

[0081] The reason why we need to continue polling after finding the matching standard field name information is that the comparison method used in the current step is fuzzy matching. The standard field name information that is successfully matched in a single instance is not necessarily the most suitable information. Therefore, it is necessary to collect all the standard field name information that may be successfully matched for subsequent processing.

[0082] For example, if the standard field correspondence table contains two standard field names with "heart rate": standard field name 1 "average heart rate" and standard field name 2 "heart rate coefficient", and the second field to be matched is "heart rate", then after the polling is completed, two first matching information "average heart rate" and "heart rate coefficient" will be obtained;

[0083] Step 342: After the polling ends, if the number of second standard field names obtained is 1, then the second field name to be matched is added as new original field name information to the record corresponding to the standard field corresponding to the unique first matching information.

[0084] If the number of second standard field names obtained is 1, it means that there is only one standard field name information in the standard field correspondence table that matches the second field name to be matched. In this case, the embodiment of the present invention considers that the current standard field name information is the most suitable standard field name information that matches the second field name to be matched. Therefore, the second field name to be matched is added as a new original field name information to the standard field correspondence table for automatic expansion.

[0085] Step 35: If the second standard field name is not empty and has a unique number, then use the unique second standard field name to replace the corresponding first column field name in the first data list; if the second standard field name is empty, then record the corresponding second field name to be matched as the third field name to be matched; if the number of second standard field names is not unique, then record the corresponding second field name to be matched as the fourth field name to be matched.

[0086] Here, if the second standard field name is not empty and the number is unique, it means that there is only one standard field name information in the standard field corresponding table that matches the second field name to be matched. In this case, the embodiment of the present invention considers the current second standard field name to be the most suitable standard field name information that matches the second field name to be matched, so it is directly used to replace the corresponding first column field name in the first data list.

[0087] If the second standard field name is empty, it means that there is no most suitable standard field name information that matches the second field name in the standard field correspondence table. In this case, the subsequent step 35 will transfer it to the third field name to be matched and the subsequent step 36 will perform further forced matching in the standard field correspondence table to confirm the most suitable standard field name information that matches the third field name.

[0088] If the number of second standard field names is not unique, it means that there are multiple standard field name information that match the second field name to be matched in the standard field correspondence table. In this case, the subsequent step 35 will transfer it to the fourth field name to be matched, and the subsequent step 37 will further force match among the multiple matching second standard field names to confirm the most suitable standard field name information that matches the fourth field name to be matched.

[0089] Step 36: For each third field name to be matched, extract a specified number of first column data from the corresponding first data column in the first data list to form a corresponding first column data group; and input the third field name to be matched and the first column data group into the preset first standard field name forced matching interface for processing to generate the corresponding third standard field name; if the third standard field name is not empty, use the third standard field name to replace the corresponding first column field name in the first data list.

[0090] Furthermore, when the first standard field name forced matching interface receives the third field name to be matched and the first column data group, it outputs the third field name to be matched, the first column data group, and the standard field correspondence table to the reviewer; and takes the standard field name information that the reviewer uniquely confirms from the standard field correspondence table based on the data characteristics of the first column data group as the corresponding third standard field name.

[0091] Here, the specified quantity is usually set to 5; the reviewer will confirm the field characteristics of the corresponding first data column based on the actual data characteristics of the first data group, and then select the standard field name information that best matches the field characteristics from the standard field correspondence table and return it to the first standard field name forced matching interface as the standard field name information that matches the third field name to be matched, which is the third standard field name; naturally, after obtaining the third standard field name, it can be directly used to replace the corresponding first column field name in the first data list;

[0092] Step 37: For each fourth field name to be matched, extract a specified number of first column data from the corresponding first data column in the first data list to form a corresponding second column data group; and form a first approximate field name data group from the corresponding multiple second standard field names; and input the fourth field name to be matched, the second column data group, and the first approximate field name data group into a preset second standard field name forced matching interface for processing to generate the corresponding fourth standard field name; if the fourth standard field name is not empty, then use the fourth standard field name to replace the corresponding first column field name in the first data list;

[0093] It should be noted that when the second standard field name forced matching interface receives the fourth field name to be matched, the second column data group, and the first approximate field name data group, it outputs the fourth field name to be matched, the second column data group, and the first approximate field name data group to the reviewer; and takes the second standard field name that the reviewer uniquely identifies from the first approximate field name data group based on the data characteristics of the second column data group and that matches the fourth field name to be matched as the corresponding fourth standard field name;

[0094] Here, the specified quantity is usually set to 5; the reviewer will confirm the field characteristics of the corresponding first data column based on the actual data characteristics of the second column data group, and then select one second standard field name that best matches the field characteristics from multiple second standard field names in the first approximate field name data group and return it to the first standard field name forced matching interface as the standard field name information for matching the fourth field name to be matched, that is, the fourth standard field name; naturally, after obtaining the fourth standard field name, it can be directly used to replace the corresponding first column field name in the first data list;

[0095] Step 38: Use the first data list, which has undergone all standard field name replacements, as the second data list.

[0096] Here, all field names in the second data list have been standardized to the standard field names of this organization.

[0097] Step 4: Based on the preset standard data list template that matches the first type of data, process the missing data column settings of the second data list to generate the corresponding third data list;

[0098] The standard data list template includes multiple template data column field names; the third data list includes multiple third data columns; and each third column includes a third column field name and one or more third column data.

[0099] Here, the standard data list template is a format template corresponding to the current data table type. This format template specifies the mandatory standard fields required by the current organization for this type of data table. Each template data column field name corresponds to a mandatory standard field. The current step is to standardize the mandatory standard fields of the second data list according to the standard data list template.

[0100] Specifically, this includes: Step 41, checking all the second column field names in the second data list according to the template data column field names in the standard data list template; if the check finds that there is no second column field name in the second data list that matches the current template data column field name, then the current template data column field name is recorded as the missing template data column field name;

[0101] Here, the current step first checks whether there are any missing required standard fields in the second data list. If so, the template data column field names corresponding to the missing required standard fields are marked.

[0102] Step 42: For each missing template data column field name, add a new second data column corresponding to it in the second data list, and set the second column field name of the corresponding new second data column according to the current missing template data column field name;

[0103] Here, a second data column is created in the second data list for each missing required standard field;

[0104] Step 43: Obtain the template field mapping relationship list corresponding to the standard data list template; and set the second column data of each new second data column according to the template field mapping relationship list;

[0105] The template field mapping relationship list includes multiple template field mapping relationship records; each template field mapping relationship record includes template column field name information and mapping relationship information; the mapping relationship information is specifically a relational expression, and the parameters of the relational expression include one or more standard field name information.

[0106] Furthermore, the second column data of each new second data column is set according to the template field mapping relationship list. Specifically, each new second data column is polled, and the missing template data column field name corresponding to the currently polled second data column is recorded as the current field name. The relational expression in the template field mapping relationship list that matches the current field name is recorded as the current expression. The second column data of other second data columns corresponding to the parameters of the current expression are substituted into the current expression for assignment and expression calculation. The second column data of the currently polled second data column is set according to the calculation result.

[0107] For example, the second column of the new second data column is named "Duration of Hospitalization". The corresponding current expression is "Discharge Time" - "Admission Time". The second column data of the second data column named "Discharge Time" extracted from the second data list is 2021-1-10, and the second column data of the second data column named "Admission Time" extracted from the second data list is 2021-1-9. Then, substituting 2021-1-10 and 2021-1-9 into "Discharge Time" - "Admission Time" will give a calculation result of 9. Therefore, the second column data of the new second data column named "Duration of Hospitalization" will be set to 9.

[0108] Step 45: The second data list, which has completed the addition of all new data columns and the setting of column data, will be used as the corresponding third data list.

[0109] Here, all fields in the resulting third data list meet the mandatory standard field requirements of the organization for this type of data list.

[0110] Step 5: According to the preset column data conversion rules, perform column data conversion processing on the third data list to generate the corresponding fourth data list;

[0111] The fourth data list includes multiple fourth data columns; each fourth data column includes a fourth column field name and one or more fourth column data.

[0112] Here, because each data provider may have certain differences in how they express certain data types, for example, date data may have multiple expressions such as "year / month / day", "year-month-day", and "month-day", and logical data may have multiple expressions such as "true / false", "T / F", and "yes / no"; the current step is to standardize the numerical conversion of each data type that may have multiple expressions by using pre-defined column data conversion rules.

[0113] Specifically, this includes: Step 51, confirming the column data type corresponding to each third data column in the third data list and generating the corresponding first data type;

[0114] The first data type includes at least text, date, numeric, and logical types;

[0115] Step 52: Poll through each third data column and record the currently polled third data column as the current data column; if the first data type corresponding to the current data column is logical, then perform logical value conversion on the original content of each third column data of the current data column according to the logical value conversion rules of the column data conversion rules; if the first data type corresponding to the current data column is date, then perform date value conversion on the original content of each third column data of the current data column according to the date value conversion rules of the column data conversion rules.

[0116] Here, because there are fewer ambiguities in text and numerical data, this embodiment of the invention first converts date and logical data. In addition, the column data conversion rules can be customized with new rules and modified with existing rules. In specific implementation, the column data conversion rules can be adjusted at any time to standardize the data types of the third data list generated from original medical data files of different types, even at different times, in different regions, and from different institutions.

[0117] When performing logical value conversion processing on logical data, the logical value conversion rule customized in this embodiment of the invention is that if the logical data is "true", "T", "True" or "yes", it is uniformly converted to 1, and otherwise it is uniformly converted to 0. That is, when the original content of the third column data is a specified information such as "true", "T", "True" or "yes" that indicates logical truth, the original content is modified to 1, and when the original content of the third column data is a specified information such as "false", "fake", "F", "False" or "no" that indicates logical false, the original content is modified to 1.

[0118] When performing date value conversion processing on logical data, the date value conversion rule customized in this embodiment of the invention is to uniformly convert multiple specified date formats (e.g., "year / month / day" format, "year-month-day" format, or "month-day" format) into a standard date value, which is the number of days the current date is offset from January 1, 1900;

[0119] Step 53: The third data list, which has completed the transformation of all corresponding column data according to the column data transformation rules, is used as the corresponding fourth data list.

[0120] Here, the data types of all fields in the resulting fourth data list meet the organization's standard data type requirements for this type of data list.

[0121] Step 6: Output the fourth data list as the result of the standardization process.

[0122] Figure 2This is a schematic diagram of an electronic device provided in Embodiment 2 of the present invention. This electronic device can be the aforementioned terminal device or server, or it can be a terminal device or server connected to the aforementioned terminal device or server that implements the method of the embodiments of the present invention. Figure 2 As shown, the electronic device may include: a processor 301 (e.g., CPU), a memory 302, and a transceiver 303; the transceiver 303 is coupled to the processor 301, and the processor 301 controls the transmission and reception operations of the transceiver 303. The memory 302 may store various instructions for performing various processing functions and implementing the processing steps described in the foregoing method embodiments. Preferably, the electronic device involved in the embodiments of the present invention further includes: a power supply 304, a system bus 305, and a communication port 306. The system bus 305 is used to realize communication connections between components. The communication port 306 is used for communication between the electronic device and other peripherals.

[0123] exist Figure 2 The system bus 305 mentioned can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, it is represented by only one thick line in the figure, but this does not indicate that there is only one bus or one type of bus. The communication interface is used to enable communication between the database access device and other devices (e.g., clients, read-write libraries, and read-only libraries). Memory may include Random Access Memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0124] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), graphics processing units (GPUs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0125] It should be noted that the embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when run on a computer, cause the computer to perform the methods and processes provided in the above embodiments.

[0126] This invention also provides a chip for executing instructions, which is used to perform the processing steps described in the foregoing method embodiments.

[0127] This invention provides a method for standardizing medical data files, an electronic device, and a computer-readable storage medium. Based on the format standards of various insurance structures, it creates corresponding standard field mapping tables, standard data list templates, and column data conversion rules. Based on the aforementioned standard field mapping tables, it performs standard field name replacement on the original medical data file's data tables to generate a standard field table, i.e., a second data list. Based on the aforementioned standard data list template, it performs missing data column setting on the second data list to generate a complete standard field table, i.e., a third data list. Finally, based on the aforementioned column data conversion rules, it performs column data conversion on the third data list to generate a complete standard field table with compliant data format, i.e., a fourth data list. This invention not only saves a significant amount of manual operation time but also reduces the error rate of standardization conversion and improves the overall efficiency of standardization conversion.

[0128] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0129] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented in hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0130] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of standardizing processing of medical data files, characterized by, The method includes: Obtain the data table type from the original medical data file and generate the corresponding first-type data; Obtain the data table information from the original medical data file and generate the corresponding first data list; Based on a preset standard field correspondence table, the first data list is processed by replacing the standard field names to generate the corresponding second data list; Based on a preset standard data list template that matches the first type of data, the second data list is processed to set missing data columns to generate a corresponding third data list; According to the preset column data conversion rules, the third data list is processed to generate the corresponding fourth data list; The fourth data list is output as the result of the standardization process. The missing data column setting process specifically includes: Based on the field names of each template data column in the standard data list template, check all the field names of the second column in the second data list; if the check finds that there is no second column field name in the second data list that matches the current template data column field name, then record the current template data column field name as the missing field name; In the second data list, add a corresponding second data column for each of the missing field names, and set the second column field name of each newly added data column to the corresponding missing field name; Obtain a template field mapping relationship list corresponding to the standard data list template; and set the second column data of each newly added data column according to the template field mapping relationship list; the template field mapping relationship list includes multiple template field mapping relationship records; the template field mapping relationship record includes template column field name information and mapping relationship information; the mapping relationship information is a relational expression, whose parameters include one or more standard field name information; The second data list, after all new data columns have been added and column data settings have been configured, will be used as the corresponding third data list. The step of setting the second column data of each newly added data column according to the template field mapping relationship list specifically involves: polling each newly added data column, recording the missing field name corresponding to the currently polled second data column as the current field name, and recording the relation expression in the template field mapping relationship list that matches the current field name as the current expression; substituting the second column data of other second data columns corresponding to the parameters of the current expression into the current expression for assignment and expression calculation, and setting the second column data of the currently polled second data column according to the calculation result.

2. The standardized processing method for medical data files according to claim 1, characterized in that, The standard field correspondence table includes multiple records corresponding to standard fields; each standard field correspondence record includes standard field name information and multiple corresponding original field name information. The standard data list template includes multiple template data column field names; The first data list includes multiple first data columns; each first data column includes a first column field name and one or more first column data. The second data list includes multiple second data columns; each second data column includes a second column field name and one or more second column data. The third data list includes multiple third data columns; each third data column includes a third column field name and one or more third column data. The fourth data list includes multiple fourth data columns; each fourth data column includes a fourth column field name and one or more fourth column data.

3. The standardized processing method for medical data files according to claim 2, characterized in that, The step of generating a corresponding second data list by replacing the standard field names of the first data list according to a preset standard field correspondence table specifically includes: The first column field name of each of the first data columns in the first data list is recorded as the first field name to be matched; For each of the first field names to be matched, a full match query is performed on the standard field name in the standard field correspondence table to obtain the corresponding first standard field name; the number of the first standard field names is unique; If the first standard field name is not empty, then the first standard field name is used to replace the corresponding first column field name in the first data list; if the first standard field name is empty, then the corresponding first field name to be matched is recorded as the second field name to be matched. For each of the second field names to be matched, a fuzzy matching query of the standard field names is performed in the standard field correspondence table to obtain the corresponding second standard field name; the number of the second standard field names may not be unique. If the second standard field name is not empty and has a unique number, then the unique second standard field name is used to replace the corresponding first column field name in the first data list; if the second standard field name is empty, then the corresponding second field name to be matched is recorded as the third field name to be matched; if the number of second standard field names is not unique, then the corresponding second field name to be matched is recorded as the fourth field name to be matched. For each of the third field names to be matched, a specified number of data from the first column corresponding to the first data column in the first data list are extracted to form a corresponding first column data group; and the third field name to be matched and the first column data group are input into a preset first standard field name forced matching interface for processing to generate a corresponding third standard field name; if the third standard field name is not empty, the third standard field name is used to replace the corresponding first column field name in the first data list. For each of the fourth field names to be matched, a specified number of data from the first column corresponding to the first data column in the first data list are extracted to form a corresponding second column data group; and multiple corresponding second standard field names are combined to form a first approximate field name data group; the fourth field name to be matched, the second column data group, and the first approximate field name data group are input into a preset second standard field name forced matching interface for processing to generate a corresponding fourth standard field name; if the fourth standard field name is not empty, the fourth standard field name is used to replace the corresponding first column field name in the first data list; wherein, when the fourth standard field name is not empty, it is a second standard field name selected from the first approximate field name data group; The first data list, after all standard field name replacements have been completed, will be used as the second data list.

4. The standardized processing method for medical data files according to claim 3, characterized in that, The step of performing a fuzzy matching query on the standard field name in the standard field correspondence table for each of the second field names to be matched specifically includes: The process involves iterating through all records corresponding to the standard fields in the standard field correspondence table and recording the currently iterated record as the current record. A preset fuzzy matching algorithm is then used to compare the standard field name information of the current record with the second field name to be matched, yielding a first comparison result. If the first comparison result is a match, the standard field name information of the current record is recorded as the corresponding second standard field name, and the process continues to iterate through the next record corresponding to the standard field. If the first comparison result is a mismatch, the fuzzy matching algorithm is then applied to the current record. Each of the original field name information is compared with the second field name to be matched to obtain the corresponding second comparison result; if the second comparison result is a match, the standard field name information of the current record is recorded as the corresponding second standard field name, and the next record corresponding to the standard field is polled; if the second comparison result is a mismatch, the next record corresponding to the standard field is polled; wherein, when performing matching comparison based on the fuzzy matching algorithm, if the second field name to be matched is the standard field name information or a part of the original field name information, it is considered a match, otherwise it is a mismatch; After the polling ends, if the number of the second standard field names obtained is 1, then the second field name to be matched is added as the new original field name information to the record corresponding to the standard field that corresponds to the unique first matching information.

5. The standardized processing method for medical data files according to claim 3, characterized in that, The step of performing a full match query on the standard field name in the standard field correspondence table for each of the first field names to be matched specifically includes: In the standard field correspondence table, the original field name information that is completely consistent with the first field name to be matched is recorded as the fully matched field name information, and the standard field name information of the record corresponding to the standard field corresponding to the fully matched field name information is used as the corresponding first standard field name; if there is no original field name information in the standard field correspondence table that is completely consistent with the first field name to be matched, then the corresponding first standard field name is set to empty.

6. The standardized processing method for medical data files according to claim 3, characterized in that, The method further includes: When the first standard field name forced matching interface receives the third field name to be matched and the first column data group, it outputs the third field name to be matched, the first column data group, and the standard field correspondence table to the reviewer; and takes the standard field name information that the reviewer uniquely confirms from the standard field correspondence table based on the data characteristics of the first column data group and that matches the third field name to be matched as the corresponding third standard field name.

7. The standardized processing method for medical data files according to claim 3, characterized in that, The method further includes: When the second standard field name forced matching interface receives the fourth field name to be matched, the second column data group, and the first approximate field name data group, it outputs the fourth field name to be matched, the second column data group, and the first approximate field name data group to the reviewer; and takes the second standard field name that the reviewer uniquely identifies from the first approximate field name data group, based on the data characteristics of the second column data group, matches the fourth field name to be matched as the corresponding fourth standard field name.

8. The standardized processing method for medical data files according to claim 2, characterized in that, The step of performing column data transformation processing on the third data list according to preset column data transformation rules to generate a corresponding fourth data list specifically includes: The data types of each column in the third data list are confirmed to generate the corresponding first data type; the first data type includes at least text, date, numeric, and logical types. Each of the third data columns is polled, and the currently polled third data column is recorded as the current data column; if the first data type corresponding to the current data column is logical, then the original content of each of the third data columns of the current data column is converted into logical values ​​according to the logical value conversion rules of the column data conversion rules; if the first data type corresponding to the current data column is date, then the original content of each of the third data columns of the current data column is converted into date values ​​according to the date value conversion rules of the column data conversion rules. The third data list, which has completed the transformation of all corresponding column data according to the column data transformation rules, is used as the corresponding fourth data list.

9. An electronic device, characterized in that, include: Memory, processor, and transceiver; The processor is configured to be coupled to the memory, read and execute instructions in the memory to implement the method according to any one of claims 1-8; The transceiver is coupled to the processor, and the processor controls the transceiver to send and receive messages.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a computer, cause the computer to perform the method described in any one of claims 1-8.

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