CN112700825A - Medical data processing method and device and storage medium - Google Patents
Medical data processing method and device and storage medium Download PDFInfo
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Abstract
The present disclosure relates to a processing method of medical data, a processing apparatus of medical data, and a computer-readable storage medium, the processing method including acquiring original medical data containing a plurality of pieces of surgical information each having a code; based on the encoding, first sorting the plurality of surgical information according to a first sorting rule; second sorting the first sorted surgical information according to a second sorting rule based on the encoding; performing a third ordering of the second ordered surgical information according to a third ordering rule based on the encoding; target medical data having a surgical ordering feature is generated. Through the embodiments of the present disclosure, complete medical data can be accurately and efficiently standardized, and ordered operation information can be sorted out.
Description
Technical Field
The present disclosure relates to the field of medical data intelligent processing technology, and in particular, to a medical data processing method, a medical data processing apparatus, and a computer-readable storage medium.
Background
In the processing of medical data, the order of coding of the procedures is required in relation to the interpretation of the procedure information, in particular the determination of the first procedure information, which is directly related to the outcome of the DRG packet. The clinician is typically not aware of the procedure sequence when writing the procedure or the sequence of the procedure code is wrong.
Disclosure of Invention
The present disclosure is intended to provide a medical data processing method, a medical data processing apparatus, and a computer-readable storage medium, which can accurately and efficiently perform standardized processing on complete medical data and sort out ordered operation information.
According to one aspect of the present disclosure, there is provided a method for processing medical data, including:
acquiring original medical data, wherein the original medical data comprises a plurality of operation information respectively provided with codes;
based on the encoding, first sorting the plurality of surgical information according to a first sorting rule;
second sorting the first sorted surgical information according to a second sorting rule based on the encoding;
performing a third ordering of the second ordered surgical information according to a third ordering rule based on the encoding;
target medical data having a surgical ordering feature is generated.
In some embodiments, the first, second, and third ordering rules are configured in a surgical information ordering table, wherein the surgical information ordering table includes:
standard surgical information, standard coding, and attribute information corresponding to different ordering rules.
In some embodiments, the attribute information includes:
surgical type information corresponding to a first ordering rule;
surgical grade information corresponding to the second ordering rule;
surgical expense information corresponding to the third ranking rule.
In some embodiments, the first and second light sources, wherein,
the generation mode of the operation grade information comprises the following steps: classifying according to standard operation grades to determine grade parameters of each piece of operation information in an operation information sequencing list;
the generation mode of the operation expense information comprises the following steps: and (5) carrying out normalization coding on the operations related to the charging items to determine the corresponding cost.
In some embodiments, wherein the first ranking the plurality of surgical information according to the first ranking rule comprises:
the operation type information is used as interventional therapy, therapeutic operation and diagnostic operation to be ordered.
In some embodiments, the determining of the encoding of the surgical information in the raw medical data comprises:
matching the original operation information with the standard words of the standard operation information table to obtain a matching relation;
screening out to-be-processed operation information respectively comprising original operation information and standard words based on the matching relationship;
and carrying out normalization processing on the operation information to be processed through a classification algorithm to obtain a standard code of the operation information.
In some embodiments, the normalizing the surgical information to be processed by the classification algorithm includes:
inputting the operation information to be processed into a neural network classification model;
analyzing whether the original operation information in the operation information to be processed corresponds to the standard words or not;
according to the analysis result, normalization processing is carried out on the corresponding original operation information and the standard words;
and outputting standard operation information which corresponds to the original operation information and contains standard codes.
In some embodiments, wherein the raw medical data comprises medical record data comprising:
medical record text information; and/or
And (5) first page information of medical records.
According to one aspect of the present disclosure, there is provided a processing apparatus of medical data, comprising:
an acquisition unit configured to acquire raw medical data containing a plurality of pieces of procedure information each having a code;
a ranking module configured to first rank the plurality of surgical information according to a first ranking rule based on the encoding; second sorting the first sorted surgical information according to a second sorting rule based on the encoding; performing a third ordering of the second ordered surgical information according to a third ordering rule based on the encoding;
a generation module configured for generating target medical data having a surgical ranking feature.
According to one aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, implement:
the method for processing medical data according to the above.
The medical data processing method, the medical data processing device and the computer readable storage medium of various embodiments of the present disclosure are achieved by acquiring original medical data containing a plurality of pieces of surgical information each having a code; based on the encoding, first sorting the plurality of surgical information according to a first sorting rule; second sorting the first sorted surgical information according to a second sorting rule based on the encoding; performing a third ordering of the second ordered surgical information according to a third ordering rule based on the encoding; and generating target medical data with operation sequencing characteristics, so that the complete medical data can be processed on the basis of extracting a plurality of operation information in the medical data, and the ordered operation information can be obtained through information integration. According to the processing method, the clinical diagnosis and the operation name written by the clinician are coded on the standard terms, the operation name written by the clinician is not detailed enough, no operation sequence exists in the data, or the operation coding sequence is wrong, the medical record document can be intelligently combined for refining and perfecting, ordered main operations are output according to the method of joint sequencing of operation types, operation levels and expenses, various medical information is expressed completely, accurately and efficiently, and therefore the accuracy and the efficiency of medical research and medical diagnosis and treatment are improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure, as claimed.
Drawings
In the drawings, which are not necessarily drawn to scale, like reference numerals may designate like components in different views. Like reference numerals with letter suffixes or like reference numerals with different letter suffixes may represent different instances of like components. The drawings illustrate various embodiments generally, by way of example and not by way of limitation, and together with the description and claims, serve to explain the disclosed embodiments.
Fig. 1 shows a flow chart of a method of processing medical data to which an embodiment of the present disclosure relates;
fig. 2 shows an architecture diagram of a medical data processing apparatus according to an embodiment of the present disclosure;
fig. 3 illustrates a surgical information ranking table to which various embodiments of the present disclosure relate.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described below clearly and completely with reference to the accompanying drawings of the embodiments of the present disclosure. It is to be understood that the described embodiments are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the disclosure without any inventive step, are within the scope of protection of the disclosure.
Unless otherwise defined, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure belongs. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items.
To maintain the following description of the embodiments of the present disclosure clear and concise, a detailed description of known functions and known components have been omitted from the present disclosure.
The present disclosure relates to processing of medical data for parsing against medical information, and ordering of surgical information. In the processing of medical data, the order of coding of the procedures is required in relation to the interpretation of the procedure information, in particular the determination of the first procedure information, which is directly related to the outcome of the DRG packet. The clinician is typically not aware of the procedure sequence when writing the procedure or the sequence of the procedure code is wrong. The operation names written by the clinician are not detailed enough, and the data has no operation sequence or the operation coding sequence is wrong, so that the operation names are sorted according to some preset sorting rules.
As one solution, as shown in fig. 1 in conjunction with fig. 3, an embodiment of the present disclosure provides a method for processing medical data, including:
s101: acquiring original medical data, wherein the original medical data comprises a plurality of operation information respectively provided with codes;
s102: based on the encoding, first sorting the plurality of surgical information according to a first sorting rule;
s103: second sorting the first sorted surgical information according to a second sorting rule based on the encoding;
s104: performing a third ordering of the second ordered surgical information according to a third ordering rule based on the encoding;
s105: target medical data having a surgical ordering feature is generated.
One of the inventive concepts of the present disclosure is to process complete medical data on the basis of extracting a plurality of operation information in the medical data, and obtain ordered operation information through information integration. According to the processing method, the clinical diagnosis and the operation name written by the clinician are coded on the standard terms, the operation name written by the clinician is not detailed enough, no operation sequence exists in the data, or the operation coding sequence is wrong, the medical record document can be intelligently combined for refining and perfecting, and the ordered main operations are output by a method of combining and sequencing in a grading manner according to the preset rules.
The medical data in the embodiments of the present disclosure, which belongs to the data source, need not be particularly limited, and may be historical data or current real-time data. From the aspect of data format, medical record text data, video data, audio data and the like can be used as long as the operation information which can be contained in the medical record text data, the operation information which can be contained in the. The raw medical data to which the present disclosure relates includes a plurality of or a plurality of surgeries, each having a code, and the specific code can be referred to an ICD9-CM-3 standard information table. In some embodiments, the medical data of the present disclosure may also be medical records and diagnostic books, and the medical data in the embodiments of the present disclosure may be medical texts of the medical data input by a user through an interactive interface and an input device, and may be used for interpretation of relevant medical information by a human, a machine, or the like through a labeling or parsing manner.
In each embodiment, in the implementation process of the present disclosure, the original operation information and the original medical record first page information of the present embodiment may be extracted through a neural network model. In the implementation process, the specific neural network model is not particularly limited, and can be implemented by adopting a neural network model which meets the requirements and is matched with the architecture. According to the more preferable scheme, the extraction accuracy of various information can be further optimized through the adaptive neural network model on the basis of the pre-training model. For extracting medical entity content, entity extraction can be performed based on a text recognition mode, for example, a text recognition mode such as NLP (natural language processing), and clauses and classifications are performed on entities by combining medical concepts. More preferably, the entity can be analyzed by combining with a standard medical information table, such as various information tables of ICD, and extracted on the basis of combining with a proper medical rule analysis result.
In some embodiments, the first, second, and third ordering rules of the present disclosure are configured in a surgical information ordering table, wherein the surgical information ordering table includes:
standard surgical information, standard coding, and attribute information corresponding to different ordering rules.
Specifically, in the present embodiment, the standard surgical information and the standard code may adopt standard words in the ICD9-CM-3 standard information table. The sorting rule can set sorting items according to different clinical requirements, and level information, which can be qualitative level information or quantitative level information, is set under each sorting item. For example, level information is set according to the attribute of the operation, level information is set according to the complexity of the operation, level information is set according to the department involved in the operation, level information is set according to the consultation situation involved in the operation, and the like.
In some embodiments, the attribute information of the present disclosure includes:
surgical type information corresponding to a first ordering rule;
surgical grade information corresponding to the second ordering rule;
surgical expense information corresponding to the third ranking rule.
Specifically, the present embodiment may set three types of sorting items for joint sorting. Since the ICD9-CM-3 standard information table is used in the embodiment, and the ICD9-CM-3 standard information table contains the items of standard operation name, standard operation code and standard operation type, the embodiment can perform corresponding sorting operation by using the operation code as an index number.
In some embodiments, the first ranking of the plurality of surgical information according to the first ranking rule of the present disclosure comprises:
the operation type information is used as interventional therapy, therapeutic operation and diagnostic operation to be ordered. The operation types can be divided into 'operation, interventional therapy and diagnostic operation', and can combine clinical operation experience and medical knowledge to represent the 'interventional therapy, therapeutic operation and diagnostic operation' to construct the sequencing characteristics of the operation types.
The operation level can adopt a uniformly established four-level operation classification standard, and the sequencing items of the operation level are quantified by 1, 2, 3 and 4. The operation fee can be coded in a normalized mode according to each operation related to the charging item, and the corresponding fee amount is used as a sorting characteristic value.
The present disclosure is primarily directed to a sequencing scheme for surgical information with coding, and the extraction and analysis processes of the surgical information coding can be implemented manually or can be processed intelligently through AI.
In some embodiments, the determining of the encoding of the surgical information in the raw medical data of the present disclosure includes:
matching the original operation information with the standard words of the standard operation information table to obtain a matching relation;
screening out to-be-processed operation information respectively comprising original operation information and standard words based on the matching relationship;
and carrying out normalization processing on the operation information to be processed through a classification algorithm to obtain a standard code of the operation information.
In some embodiments, the obtaining a matching relationship of the present disclosure includes: combining the original operation information and each standard word of a standard operation information table in pairs, and respectively calculating matching degrees;
based on the matching relationship, screening out the to-be-processed operation information respectively containing the original operation information and the standard words, and the method comprises the following steps: and under the condition that the matching degree meets a preset condition, taking the original operation information and the standard words of the standard operation information table as the operation information to be processed.
Specifically, the standard surgical information table of the present embodiment can be obtained through the ICD-9-CM-3 standard table, and the surgical information can be calibrated in detail and the corresponding surgical code can be given through the ICD-9-CM-3 standard table.
The original operation information and the standard medical information in the ICD-9-CM-3 standard table, or called standard words, are combined in pairs. For example, the input in this embodiment is surgical medical data, wherein the surgical information is recorded in the text as
"name of operation: coronary angiography + PCI
The operation process comprises the following steps: the operation area is disinfected conventionally, a sterile hole towel is laid, the right radial artery puncture is successful, and the radiography shows that: the left trunk is normal, the front descending branch is 7-8 sections and is 80% narrow, the circumgyrating branch is 11 sections and is 80% narrow, the right crown is 2-3 sections and is 40% narrow, and the PDA opening is 70% narrow. PCI (peripheral component interconnect) operation: replacing AL1guiding6F to the left coronary orifice, feeding a BMW guide wire to the distal end of the anterior descending branch, feeding another BMW to the distal end of D2, feeding a Tazuna2.0 × 15mm saccule to expand the 7-8 segment lesion at 12-13atm for 7-8 seconds, then implanting a Firenbird3.0 × 20mm scaffold to expand the stent at 14atm for 8 seconds, releasing the 3.5 × 18mm scaffold at 7 segment 12atm8 seconds, expanding the saccule expandable stent at 3.0 × 10mm for 18atm7-8 seconds, expanding the circumflex-branch lesion at 2.0 × 15mm saccule from the BMW guide wire to the distal end of the circumflex-branch for 16atm8 seconds, releasing the 3.5 × 15mm scaffold at 12atm8 seconds, repeatedly showing good stent expansion by radiography, having no residual stenosis, achieving the TIMI3 grade of blood flow, removing the catheter and the sheath, locally compressing hemostasis, finishing the surgery,
the name of the operation is only written as 'PCT operation', but the recorded content of the operation is combined with medical knowledge to determine what type of stent the patient is, actually implant several stents, and perform percutaneous coronary artery sacculus dilatation and plasty on the patient. Then, based on the data processing method of the present disclosure, at least error correction of the procedure name according to the procedure pass can be realized for the input.
The standard surgical information table of this embodiment includes a plurality of rows of data entries containing information such as primary code, additional code, surgical name, category, etc. And combining the input text original text and each standard word in the standard operation information table in pairs to form a data item in the format of 'original text and standard word'. E.g. forming
"1. (original, non-drug eluting coronary stents placed)
2. (original, coronary artery drug coating stent implantation)
3. (original, drug eluting coronary stent)
… …' (pair of information).
The operation passing information is described through text, so that the operation passing information is the detailed content of the operation name, and the operation passing information and the operation name have a corresponding relation. Based on the correspondence, each pair of combined pair can be used as a piece of training data in this embodiment.
The matching degree is calculated for each pair combination of the above forms, and the combination with the matching degree lower than a preset threshold is discarded and is not used as a data normalization target. And taking the combination with the matching degree meeting the preset threshold as a to-be-selected normalization object.
Specifically, the determination method of the matching degree in this embodiment may be calculated based on the number of words included in each of the original text and the standard word, for example, the matching degree is calculated by the number of words in intersection between the original text and the standard word and the number of words in union of the original text and the standard word, or is referred to as the similarity between the original text and the standard word. By way of example with simple text numbers, the original text contains 'bilateral thyroidectomy', a standard word after two-two combination is 'thyroidectomy', the intersection of the two is 'thyroidectomy', the number of words is 4, and the union of the two is the sum of the lengths of two character strings minus the number of intersection words: 9+8-4 is 13, so the similarity of this pairwise combination is "4/13". The embodiment can perform similarity judgment on long text operation passes, such as operation description characters in thousands of characters.
In some embodiments, the normalizing the surgical information to be processed by the classification algorithm of the present disclosure includes:
inputting the operation information to be processed into a neural network classification model;
analyzing whether the original operation information in the operation information to be processed corresponds to the standard words or not;
according to the analysis result, normalization processing is carried out on the corresponding original operation information and the standard words;
outputting standard surgical information corresponding to the original surgical information for generating the target medical data.
Specifically, continuing with the above example, assuming that the pair of pair is used as the surgical information to be processed, the present embodiment may use the pair of pair as the surgical information to be processed
"1. (original, non-drug eluting coronary stents placed)
2. (original, coronary artery drug coating stent implantation)
3. (original, drug eluting coronary stent)
”
……
And inputting the neural network classification model. The method can construct an adaptive neural network classification model, and analyze and normalize the operation information to be processed in the modes of adaptively configuring corresponding frameworks, the number of neurons and the like.
For example, each group of information is classified, and whether the operation information from the medical data and the standard word screened from the standard information table have related or similar medical concepts, for example, whether the two have a containing relationship is judged. If the neural network classification model considers that the two belong to the situation with the inclusion relationship, the neural network classification model outputs a corresponding result, for example, outputs "1" to characterize a positive conclusion. If the neural network classification model considers that the two do not belong to the situation with the inclusion relationship, the neural network classification model outputs a corresponding result, for example, outputs "0" to represent a negative conclusion.
In order to optimize the performance of the neural network classification model, in various embodiments of the present disclosure, the performance of the neural network classification model may be increased by optimizing a pre-training model. In particular, large-scale medical anticipation data may be collected, with the data sources targeting surgical information, including but not limited to: medical data of medical structures, medical data of medical research institutions, medical data in medical information systems, medical textbooks of various languages, medical dictionaries, medical treatises, medical treatment data in online databases, department libraries, website webpage data, forum data and the like. In the embodiment of the disclosure, a pre-training model can be constructed to pre-train the acquired large-scale medical prediction data, so that the model learns various medical knowledge from the large-scale data, and can be applied to specific tasks on the basis to improve the performance of the neural network model of the disclosure. Pre-training models include, but are not limited to: BERT model, XLNET model, roBERTa model, etc.
"1" for the aforementioned input neural network classification model based on the neural network classification model of the present embodiment (plain, non-drug eluting coronary stent placement)
2. (original, coronary artery drug coating stent implantation)
3. (original, drug eluting coronary stent)
… … ", standard surgical information corresponding to the original text may be output. Since the present disclosure can be aided with the ICD-9-CM-3 table, such standard surgical information may include standard surgical names, standard surgical name codes, etc., such as output:
"1.36.0700 | drug eluting coronary stent placement
2.00.6600X 004| percutaneous coronary artery saccule dilatation forming operation
3.00.4700 stent for three blood vessels
4.88.5700| other and unspecified coronary angiography "
Intuitively, it can be understood that the above results outputted by the neural network classification model of the present embodiment can obtain accurate information description of the operation, including details about what type of stent is, how many stents are actually implanted, and how the patient has performed percutaneous coronary angioplasty.
In some embodiments, the neural network classification model of the present disclosure is obtained based on pre-training, and the pre-training mode includes:
acquiring medical data comprising original surgical information;
extracting operation name information and operation passing information in the original operation information;
combining the operation passing information with standard words in an operation name library, wherein the operation name library is constructed based on the operation name information and comprises an original operation name and a standard operation name of original operation information;
training a pre-training model by using the combined surgical procedure information and standard words in a surgical name library as training data;
and obtaining the neural network classification model based on the pre-training model.
In particular, embodiments of the present disclosure may construct and iteratively train a training model to which the present disclosure relates based on a sufficient amount of data to satisfy the training, such as one or more batches, or massive acquisitions of medical data containing the original surgical information.
In some embodiments, the method of constructing the surgical name library of the present disclosure comprises:
inputting the operation name information into a neural network model for coding an operation name so as to extract a standard operation name code from the operation name information;
and constructing an operation name library containing the original operation names and the coded standard operation names based on the operation name information and the standard operation name codes.
Specifically, with the operation name "coronary angiography + PCI surgery", the operation name coding of the ICD standard can be extracted from the operation name text in the medical data, preferably by a neural network model coding the operation name, in combination with the operation name "coronary angiography + PCI surgery", according to the result of the coding of the original operation name text
"1. other and unspecified coronary angiography
2. Non-drug eluting coronary stent placement "
The operation name library of the present embodiment is constructed, which includes the original operation name and the encoded standard operation name.
The neural network classification model of the present embodiment may use any other classification model, including but not limited to: CNN, LSTM, transformer, etc. Based on the pre-trained model finetune in some embodiments, the model converges faster with a 2% improvement in performance.
In some embodiments, the standard surgical information table of the present disclosure is constructed in a manner including: and constructing based on the label of the ICD standard table, wherein the labeled result comprises the result according to the operation name and the coded result. In combination with the foregoing, the present embodiment may construct a standard information table based on the labeling result, including information such as main codes, additional codes, operation names, and categories.
In some aspects, the embodiments of the present disclosure are directed to surgical information described in medical record first page information for surgical contents, and may be able to parse N pieces of first surgical information from M pieces of original surgical information, using standard medical information as a guide, in a case of a clause model process based on deep learning training. The present disclosure focuses more on the case where the original medical data includes a plurality of original medical information, that is, at least for the scenario where M is greater than or equal to 2, N pieces of first surgical information are analyzed from M pieces of original medical information, and the number N of the analyzed pieces of first surgical information may be equal to M in theory, may be smaller than M, and certainly may be greater than M in some aspects as long as the medical concept and the clinical diagnosis significance are met.
In some embodiments, the construction method of the deep learning-trained clause model of the present disclosure includes:
extracting medical data;
sentence division is carried out on the medical data respectively to obtain a binary group containing an original text and a text after sentence division;
and (5) iteratively training the model.
Specifically, the medical data of the embodiment takes the operation data for the operation, such as the operation content and the operation name, as an example, the operation data cannot be divided into sentences in a rule manner, only the rules applicable in a specific scene exist, and the scene division requires medical judgment. Therefore, in the present embodiment, a clause model may be used to perform clause operations. The sentence splitting model can be constructed based on deep learning training, specifically, a batch of data can be labeled by professional manual work based on the standard of the operation fund, original data containing the operation name is split to obtain a data binary group example, the binary group can comprise an original text and a text after the sentence splitting, for example, a binary group in a data format of 'the original text, the text after one or more sentences' is formed. The sentence splitting model of the present embodiment can be trained based on a sufficient amount of data that satisfies the training, such as one or more batches, or mass labeled bigrams.
In some embodiments, the clauseing of the medical data of the present disclosure includes:
and performing clauses according to the specific identification contained in the medical data.
For the input "surgery name: bilateral thyroid gland partial resection + upper left parathyroid adenoma resection + right mammary gland segmental resection ", in the process of marking clauses, regarding the section of operation data, a" + "sign is taken as a specific mark in the section of operation data to separate operation information, wherein the embodied operation information comprises an operation name: bilateral thyroidectomy, upper left parathyroid adenomatomy, right mammary gland segmental resection ". In the input operation data, no information needing to be continued across plus signs and plus signs exists, and the operation names before and after each plus sign are independent and complete from the analysis of medical diagnosis, so that the length of sentences can be shortened by sentence division. Thus, the results of the clauses can be considered as three subjects "bilateral thyroidectomy", "superior left parathyroid adenomatoctomy", "right mammary gland segmental resection".
In some embodiments, the clauseing of the medical data of the present disclosure includes:
analyzing medical information contained in the medical data;
determining medical information with an association relation according to the semantics of the medical information;
and determining a clause object based on the medical information with the association relation.
Specifically, habitual writing or shorthand occurs in the process of surgical data entry or recording, so that the surgical information contained in the data is related before and after, but appears to be separated from the surface of the data, and therefore, the accurate surgical information contained in the data is difficult to judge. In the embodiment, by analyzing the semantics of the medical information of each part in the medical information, the representation contents of the medical information on the medical concept are extracted, and whether a correlation exists in all the representation contents, for example, whether a mutual inclusion relationship exists between the operation information 1 and the operation information 2, such as whether common information exists between the operation information 1 and the operation information n, and further such as whether contradictory information exists between the operation information 1 and the operation information x, is judged according to all the representation contents.
For example, the input medical data includes "full uterus under laparoscope + bilateral appendectomy", and if a regular clause is adopted, the mark of "+" is included, and the clause result analyzed by the clause is "full uterus under laparoscope" and "bilateral appendectomy". In combination with the medical concept, it can be determined that the surgical information of "whole uterus under laparoscope" can only express "laparoscope and site", and the specific surgical style cannot be clarified. In addition, the surgical information of the bilateral appendectomy lacks surgical operation information, such as the information of laparoscope. In this case, if the sentence is divided in a partitioned form according to the feature identifier, although the inputted medical data "the total uterus under laparoscope + bilateral appendectomy" completely contains information that can be related to various operations, the sentence dividing result according to the rule is wrong, so that the final operation standard name is converted wrongly, and the accurate information of the operation process cannot be accurately, exhaustively and intelligently expressed, and thus the actual semantics cannot be met. If the sentence dividing model of the embodiment is adopted, the sentence dividing is carried out according to the actual semantics aiming at the full uterus + bilateral adnexectomy under the laparoscope, the sentence dividing model learns the relevant knowledge in the training stage, and here the + is not the sentence dividing mark, so that the + is not divided.
For another example, the input medical data includes 'VATS upper right lobe resection + wedge resection of middle right lobe and lower right lobe + pleural adhesion cauterization + ductus thoracis ligation', the sentence division model learns related knowledge in the training phase, the 'VATS' thoracico-scope information at the beginning of the sentence can be judged to need to be continued backwards, and here, '+' can be judged not to be a sentence division mark, so that the sentence division cannot be carried out according to the '+'.
Preferably, the obtaining second medical data according to the matching result of the first surgical information and the standard medical information of the present disclosure includes: combining each piece of first surgical information and each piece of standard medical information in pairs; the matching degree is calculated separately, and the determination manner of the matching degree may be calculated based on the number of words included in each of the clauses and the standard words, for example, the matching degree is calculated by the number of words in intersection between the clauses and the standard words and the number of words in union of the clauses and the standard words, or referred to as the similarity between the clauses and the standard words.
On the basis, data such as "1. (bilateral thyroidectomy, thyroidectomy)/2. (bilateral thyroidectomy, unilateral thyroidectomy/3. (bilateral thyroidectomy, bilateral thyroidectomy)/4. (bilateral thyroidectomy, greater thyroidectomy)/5. … …" are constructed, and the data are analyzed to obtain standardized information about surgical information through normalization processing, specifically through a neural network classification model, and the method comprises the following steps:
analyzing each set of first surgical information and standard medical information contained in the second medical data;
analyzing whether each set of first surgical information corresponds to standard medical information based on a neural network;
and outputting an analysis result, and taking the corresponding first surgical information and the standard medical information as a normalization result.
Specifically, with the above example being combined, the present disclosure may construct a neural network classification model, configure corresponding architectures and numbers of neurons, and perform analysis and normalization processing on the second medical data. For the second medical data, for example, in the form of "1. (bilateral thyroidectomy, thyroidectomy)/2. (bilateral thyroidectomy, unilateral thyroidectomy/3. (bilateral thyroidectomy, bilateral thyroidectomy)/4. (bilateral thyroidectomy, greater thyroidectomy)/5. … …", each set of information is classified, it is determined whether the standard words from the original first medical data and the standard information table have related or similar medical concepts, for example, whether both have inclusion relations, if the neural network classification model considers that both belong to the case where there is an inclusion relation, the neural network classification model outputs a corresponding result, for example, outputs "1" to represent a positive conclusion, if the neural network classification model considers that both do not belong to the case where there is an inclusion relation, the neural network classification model outputs a corresponding result, for example "0" to characterize a negative conclusion.
In various embodiments of the present disclosure, the performance of the neural network classification model may be increased by preferring a pre-trained model. In particular, large-scale medical anticipation data may be collected, and data sources include, but are not limited to: medical record data of each medical structure, medical data of each medical research institution, medical data in each medical information system, medical textbooks of various languages, medical dictionaries, medical treatises, medical treatment data in online databases, department libraries, website webpage data, forum data and the like. In the embodiment of the disclosure, a pre-training model can be constructed to pre-train the acquired large-scale medical prediction data, so that the model learns various medical knowledge from the large-scale data, and can be applied to specific tasks on the basis to improve the performance of the neural network model of the disclosure. Pre-training models include, but are not limited to: BERT model, XLNET model, roBERTa model, etc.
In more detail, the neural network classification model according to each embodiment of the present disclosure is combined with the foregoing to complete final classification, and it is determined whether the original medical data and the candidate standard word represent the same medical information concept, for example, whether there is overlapping description, conflicting description, missing description, and the like between the surgical information. If the pre-trained model of the embodiment of the present disclosure is used, the neural network classification model in the embodiment may be substantially consistent with the pre-trained model, and the neural network classification model of the embodiment needs to be initialized by the pre-trained model. The neural network classification model of the present embodiment may use any other classification model if the pre-trained model is not employed, including but not limited to: CNN, LSTM, transformer, etc.
Further, the outputting the analysis result of the present disclosure, using the corresponding first surgical information and standard medical information as a normalization result, includes: characterizing the first surgical information in a format of standard medical information; the surgical code of the embodiments of the present disclosure is output.
Specifically, with the above contents being combined, on the basis that the neural network classification model of the present embodiment outputs a corresponding result, and for a combination in which the output result is a positive conclusion, the present embodiment may extract the operation name and the operation code in the ICD-9-CM-3 standard table, and perform a unified expression process on the originally input operation data. For the above originally inputted "bilateral thyroid gland resection", "upper left parathyroid adenoma resection" and "right mammary gland segmental resection", in this embodiment, the "operation coding operation name" is extracted from the ICD-9-CM-3 standard table, and these information are respectively updated, which is expressed as outputting the target medical data:
"1.06.3900X 012| bilateral thyroidectomy
2.06.8903 Parathyroid lesion excision
3.85.2100X 019 mastectomy "
Compared with the originally input operation data, the target medical data output by the embodiment can be completely and specifically expressed in a unified manner, and the operation information codes in the first page information of the medical record are obtained.
As one of the aspects of the present disclosure, as shown in fig. 2 in combination with fig. 3, the present disclosure also provides a medical data processing apparatus, including:
an acquisition unit configured to acquire raw medical data containing a plurality of pieces of procedure information each having a code;
a ranking module configured to first rank the plurality of surgical information according to a first ranking rule based on the encoding; second sorting the first sorted surgical information according to a second sorting rule based on the encoding; performing a third ordering of the second ordered surgical information according to a third ordering rule based on the encoding;
a generation module configured for generating target medical data having a surgical ranking feature.
In connection with the examples described above:
in some embodiments, the obtaining unit of the present disclosure may be an input device, a screen capturing device, a text recognition device, and the like, and is intended to obtain medical data including a plurality of pieces of encoded surgical information, and may include medical record data, where the medical record data includes: medical record text information; and/or medical record first page information.
In some embodiments, the ranking module of the present disclosure may be further configured to:
and sorting based on the operation information sorting table configured with the first sorting rule, the second sorting rule and the third sorting rule.
Further, the operation information sorting table includes:
standard surgical information, standard codes, and attribute information corresponding to different ordering rules;
the attribute information includes:
surgical type information corresponding to a first ordering rule;
surgical grade information corresponding to the second ordering rule;
surgical expense information corresponding to the third ranking rule.
In particular, one of the inventive concepts of the present disclosure is directed to a medical diagnostic system by obtaining raw medical data containing a plurality of surgical information each having a code; based on the encoding, first sorting the plurality of surgical information according to a first sorting rule; second sorting the first sorted surgical information according to a second sorting rule based on the encoding; performing a third ordering of the second ordered surgical information according to a third ordering rule based on the encoding; and generating target medical data with operation sequencing characteristics, so that the complete medical data can be processed on the basis of extracting a plurality of operation information in the medical data, and the ordered operation information can be obtained through information integration. According to the processing method, the clinical diagnosis and the operation name written by the clinician are coded on the standard terms, the operation name written by the clinician is not detailed enough, no operation sequence exists in the data, or the operation coding sequence is wrong, the medical record document can be intelligently combined for refining and perfecting, ordered main operations are output according to the method of joint sequencing of operation types, operation levels and expenses, various medical information is expressed completely, accurately and efficiently, and therefore the accuracy and the efficiency of medical research and medical diagnosis and treatment are improved.
As one of the aspects of the present disclosure, the present disclosure also provides a computer-readable storage medium having stored thereon computer-executable instructions, which when executed by a processor, mainly implement a processing method according to the medical data described above, including at least:
acquiring original medical data, wherein the original medical data comprises a plurality of operation information respectively provided with codes;
based on the encoding, first sorting the plurality of surgical information according to a first sorting rule;
second sorting the first sorted surgical information according to a second sorting rule based on the encoding;
performing a third ordering of the second ordered surgical information according to a third ordering rule based on the encoding;
target medical data having a surgical ordering feature is generated.
In some embodiments, a processor executing computer-executable instructions may be a processing device including more than one general-purpose processing device, such as a microprocessor, Central Processing Unit (CPU), Graphics Processing Unit (GPU), or the like. More specifically, the processor may be a Complex Instruction Set Computing (CISC) microprocessor, Reduced Instruction Set Computing (RISC) microprocessor, Very Long Instruction Word (VLIW) microprocessor, processor running other instruction sets, or processors running a combination of instruction sets. The processor may also be one or more special-purpose processing devices such as an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), a system on a chip (SoC), or the like.
In some embodiments, the computer-readable storage medium may be a memory, such as a read-only memory (ROM), a random-access memory (RAM), a phase-change random-access memory (PRAM), a static random-access memory (SRAM), a dynamic random-access memory (DRAM), an electrically erasable programmable read-only memory (EEPROM), other types of random-access memory (RAM), a flash disk or other form of flash memory, a cache, a register, a static memory, a compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD) or other optical storage, a tape cartridge or other magnetic storage device, or any other potentially non-transitory medium that may be used to store information or instructions that may be accessed by a computer device, and so forth.
In some embodiments, the computer-executable instructions may be implemented as a plurality of program modules that collectively implement the method for displaying medical images according to any one of the present disclosure.
The present disclosure describes various operations or functions that may be implemented as or defined as software code or instructions. The display unit may be implemented as software code or modules of instructions stored on a memory, which when executed by a processor may implement the respective steps and methods.
Such content may be source code or differential code ("delta" or "patch" code) that may be executed directly ("object" or "executable" form). A software implementation of the embodiments described herein may be provided through an article of manufacture having code or instructions stored thereon, or through a method of operating a communication interface to transmit data through the communication interface. A machine or computer-readable storage medium may cause a machine to perform the functions or operations described, and includes any mechanism for storing information in a form accessible by a machine (e.g., a computing display device, an electronic system, etc.), such as recordable/non-recordable media (e.g., Read Only Memory (ROM), Random Access Memory (RAM), magnetic disk storage media, optical storage media, flash memory display devices, etc.). The communication interface includes any mechanism for interfacing with any of a hardwired, wireless, optical, etc. medium to communicate with other display devices, such as a memory bus interface, a processor bus interface, an internet connection, a disk controller, etc. The communication interface may be configured by providing configuration parameters and/or transmitting signals to prepare the communication interface to provide data signals describing the software content. The communication interface may be accessed by sending one or more commands or signals to the communication interface.
The computer-executable instructions of embodiments of the present disclosure may be organized into one or more computer-executable components or modules. Aspects of the disclosure may be implemented with any number and combination of such components or modules. For example, aspects of the disclosure are not limited to the specific computer-executable instructions or the specific components or modules illustrated in the figures and described herein. Other embodiments may include different computer-executable instructions or components having more or less functionality than illustrated and described herein.
The above description is intended to be illustrative and not restrictive. For example, the above-described examples (or one or more versions thereof) may be used in combination with each other. For example, other embodiments may be used by those of ordinary skill in the art upon reading the above description. In addition, in the foregoing detailed description, various features may be grouped together to streamline the disclosure. This should not be interpreted as an intention that a disclosed feature not claimed is essential to any claim. Rather, the subject matter of the present disclosure may lie in less than all features of a particular disclosed embodiment. Thus, the following claims are hereby incorporated into the detailed description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that these embodiments may be combined with each other in various combinations or permutations. The scope of the disclosure should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
The above embodiments are merely exemplary embodiments of the present disclosure, which is not intended to limit the present disclosure, and the scope of the present disclosure is defined by the claims. Various modifications and equivalents of the disclosure may occur to those skilled in the art within the spirit and scope of the disclosure, and such modifications and equivalents are considered to be within the scope of the disclosure.
Claims (10)
1. A method of processing medical data, comprising:
acquiring original medical data, wherein the original medical data comprises a plurality of operation information respectively provided with codes;
based on the encoding, first sorting the plurality of surgical information according to a first sorting rule;
second sorting the first sorted surgical information according to a second sorting rule based on the encoding;
performing a third ordering of the second ordered surgical information according to a third ordering rule based on the encoding;
target medical data having a surgical ordering feature is generated.
2. The method of claim 1, wherein the first, second, and third ordering rules are configured in a surgical information ordering table, wherein the surgical information ordering table comprises:
standard surgical information, standard coding, and attribute information corresponding to different ordering rules.
3. The method of claim 2, wherein the attribute information comprises:
surgical type information corresponding to a first ordering rule;
surgical grade information corresponding to the second ordering rule;
surgical expense information corresponding to the third ranking rule.
4. The method of claim 3, wherein,
the generation mode of the operation grade information comprises the following steps: classifying according to standard operation grades to determine grade parameters of each piece of operation information in an operation information sequencing list;
the generation mode of the operation expense information comprises the following steps: and (5) carrying out normalization coding on the operations related to the charging items to determine the corresponding cost.
5. The method of claim 3, wherein the first ranking the plurality of procedure information according to a first ranking rule comprises:
the operation type information is used as interventional therapy, therapeutic operation and diagnostic operation to be ordered.
6. The method of claim 1, wherein the encoding of the surgical information in the raw medical data is determined in a manner comprising:
matching the original operation information with the standard words of the standard operation information table to obtain a matching relation;
screening out to-be-processed operation information respectively comprising original operation information and standard words based on the matching relationship;
and carrying out normalization processing on the operation information to be processed through a classification algorithm to obtain a standard code of the operation information.
7. The method of claim 6, wherein the normalizing the surgical information to be processed by the classification algorithm comprises:
inputting the operation information to be processed into a neural network classification model;
analyzing whether the original operation information in the operation information to be processed corresponds to the standard words or not;
according to the analysis result, normalization processing is carried out on the corresponding original operation information and the standard words;
and outputting standard operation information which corresponds to the original operation information and contains standard codes.
8. The method of any of claims 1 to 7, wherein the raw medical data comprises medical record data comprising:
medical record text information; and/or
And (5) first page information of medical records.
9. Apparatus for processing medical data, comprising:
an acquisition unit configured to acquire raw medical data containing a plurality of pieces of procedure information each having a code;
a ranking module configured to first rank the plurality of surgical information according to a first ranking rule based on the encoding; second sorting the first sorted surgical information according to a second sorting rule based on the encoding; performing a third ordering of the second ordered surgical information according to a third ordering rule based on the encoding;
a generation module configured for generating target medical data having a surgical ranking feature.
10. A computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, implement:
the method of processing medical data according to any one of claims 1 to 8.
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