CN113053522B - Method, apparatus, device, medium, and product for processing medical data - Google Patents
Method, apparatus, device, medium, and product for processing medical data Download PDFInfo
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Abstract
The application discloses a method, a device, equipment, a medium and a product for processing medical data, relates to the field of computers, and further relates to the technical field of intelligent medical treatment. The specific implementation scheme is as follows: acquiring medical data; determining medical entity information corresponding to the medical data; determining a label set matched with medical entity information based on each preset operator; medical data is processed based on the set of tags. The implementation mode can improve the medical data processing effect.
Description
Technical Field
The present disclosure relates to the field of computers, and more particularly to the field of intelligent medical technology, and in particular to methods, apparatus, devices, media and products for processing medical data.
Background
At present, the processing of medical data is an important medical link, such as processing medical order data, processing prescription data and the like.
In practice, it has been found that medical safety is affected if there is an order or if the prescription data is not reasonably processed. In this regard, it is now common to rely on medical staff to perform an inspection of the medical data processing, however if the medical staff is overlooked, it is often difficult to cope with the abnormal situation of the medical data processing, resulting in poor medical data processing effect.
Disclosure of Invention
The present disclosure provides a method, apparatus, device, medium, and article for processing medical data.
According to a first aspect, there is provided a method for processing medical data, comprising: acquiring medical data; determining medical entity information corresponding to the medical data; determining a label set matched with medical entity information based on each preset operator; medical data is processed based on the set of tags.
According to a second aspect, there is provided an apparatus for processing medical data, comprising: a data acquisition unit configured to acquire medical data; an information determination unit configured to determine medical entity information corresponding to the medical data; a set determining unit configured to determine a set of labels matching the medical entity information based on preset respective operators; and a data processing unit configured to process the medical data based on the tag set.
According to a third aspect, there is provided an electronic device performing a method for processing medical data, comprising: one or more computing units; a storage unit for storing one or more programs; the one or more programs, when executed by the one or more computing units, cause the one or more computing units to implement a method for processing medical data as any of the above.
According to a fourth aspect, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform a method for processing medical data as any one of the above.
According to a fifth aspect, there is provided a computer program product comprising a computer program which, when executed by a computing unit, implements a method for processing medical data as in any of the above.
According to the technology of the application, a method for processing medical data is provided, the medical data can be acquired, medical entity information corresponding to the medical data is determined, and then a label set matched with the medical entity information is determined based on each preset operator, so that a multi-operator label set is obtained, the medical data is processed based on the label set, the processing requirements corresponding to the labels can be met for processing the medical data, for example, examination items can be determined according to disease labels and patient labels when medical advice data are processed, and therefore the medical data processing effect is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is an exemplary system architecture diagram in which an embodiment of the present application may be applied;
FIG. 2 is a flow chart of one embodiment of a method for processing medical data according to the present application;
FIG. 3 is a schematic illustration of one application scenario of a method for processing medical data according to the present application;
FIG. 4 is a flow chart of another embodiment of a method for processing medical data according to the present application;
FIG. 5 is a schematic structural view of one embodiment of an apparatus for processing medical data according to the present application;
fig. 6 is a block diagram of an electronic device for implementing a method for processing medical data according to an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is an exemplary system architecture diagram illustrating an exemplary system architecture 100 to which embodiments of the method for processing medical data of the present application may be applied, according to a first embodiment of the present disclosure.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. The terminal devices 101, 102, 103 may be mobile phones, computers, tablet computers, and other electronic devices, and various kinds of application software, such as application software for processing medical data, may be installed in the terminal devices 101, 102, 103. The user may perform the intelligent processing on the medical data in the application software for processing the medical data based on the touch operation on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices including, but not limited to, televisions, smartphones, tablets, electronic book readers, car-mounted computers, laptop and desktop computers, and the like. When the terminal devices 101, 102, 103 are software, they can be installed in the above-listed electronic devices. Which may be implemented as multiple software or software modules (e.g., to provide distributed services), or as a single software or software module. The present invention is not particularly limited herein.
The server 105 may be a server providing various services, for example, medical data such as orders in the terminal devices 101, 102, 103 may be acquired. Thereafter, data analysis may be performed on the medical data to determine medical entity information corresponding to the medical data. And determining a label set corresponding to the medical entity information based on each preset operator, and processing medical data based on the label set to obtain a data processing result. After that, the server 105 may return corresponding data to the terminal devices 101, 102, 103 based on the data processing result.
The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster formed by a plurality of servers, or as a single server. When server 105 is software, it may be implemented as a plurality of software or software modules (e.g., to provide distributed services), or as a single software or software module. The present invention is not particularly limited herein.
It should be noted that, the method for processing medical data provided in the embodiment of the present application may be performed by the terminal devices 101, 102, 103, or may be performed by the server 105. Accordingly, the means for processing medical data may be provided in the terminal devices 101, 102, 103 or in the server 105.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to fig. 2, a flow 200 of one embodiment of a method for processing medical data according to the present application is shown. The method for processing medical data of the present embodiment includes the steps of:
in step 201, medical data is acquired.
In this embodiment, the execution body (such as the server 105 or the terminal devices 101, 102, 103 in fig. 1) may store the medical data to be processed in advance, or may read the medical data by calling the interfaces of other storage devices, which is not limited in this embodiment. The medical data refers to data generated in a medical process, and may include, but is not limited to, medical record data, medical order data, prescription data, and the like, which is not limited in this embodiment.
Step 202, medical entity information corresponding to the medical data is determined.
In this embodiment, the medical entity information refers to information containing medical entities, that is, information containing medical keywords that need to be focused on. The execution body may segment the initial medical data and identify the entities to segment each medical entity in the medical data. Optionally, the executing entity may also determine the category of the respective medical entity. For example, assuming that the medical data is "i cough, headache, and pain with a cold" the executing body may first word the medical data, and the existing word segmentation technique may be adopted here, which is not described here again. The medical data are segmented to obtain 'I', 'today', 'stomach ache', 'dotted', 'cold'. Then, entity identification is performed, and the following medical entities are determined from the entity identification: "cough", "headache", "cold". Optionally, the executing subject may further determine the category corresponding to each medical entity, such as determining the category corresponding to "cough", "headache" as "symptom", and determining the category corresponding to "cold" as "disease". And obtaining medical entity information by integrating the medical entities. By determining the category to which each medical entity corresponds, semantic analysis can be performed based on the category more quickly, and labels can be determined.
Alternatively, determining medical entity information corresponding to medical data may be implemented by deep learning, preferably by training a deep network model using a combination of a two-way long-short-term memory network, an attention mechanism, and a conditional random field. By adopting the method, the mutual influence between word sequence and semantics can be considered, the problems of gradient explosion and gradient dispersion of the traditional neural network can be solved based on the long and short memory units, and the stability of model training is improved.
Step 203, determining a label set matched with the medical entity information based on each preset operator.
In this embodiment, the operator refers to a mapping from a function space to a function space, that is, a mapping relationship between medical entity information and a label. The execution body can be provided with a plurality of operators in advance, and each operator corresponds to a different label determination mode, so that based on each operator, the label matched with the medical entity information can be determined according to each label determination mode. And then summarizing all the labels to obtain a label set. Wherein each preset operator at least comprises crowd operators, index operators, entity operators, state operators and the like. The crowd operator corresponds to a label determining mode of identifying crowd labels by the deep learning model; the index operator corresponds to a label determining mode of matching the index condition with the identification index label; the entity operator corresponds to a label determining mode of text matching determining entity labels; the state operator corresponds to a label determination mode for identifying the user state label through text analysis. That is, for each operator, determining the label matched with the medical entity information according to the label determining mode corresponding to the operator, and summarizing each label corresponding to each operator to obtain a label set matched with the medical entity information. The labels in the label set may be labels for describing patients, labels for describing medicines, labels for describing examination items, or the like, which is not limited in this embodiment.
Step 204, processing medical data based on the set of tags.
In this embodiment, after determining the tag set, the executing body may perform corresponding processing on the medical data based on each tag in the tag set, for example, perform data anomaly detection on the medical data, perform integration processing on the medical data, output integrated teaching information, recommend medication data based on the medical data, and the like, which is not limited in this embodiment. Wherein, in the case of performing the integration processing on the medical data and outputting the integrated teaching information, processing the medical data based on the tag set may include: based on each label in the label set, analyzing the semantics of the label and constructing a label association relation map; determining association information among the medical entities based on the medical entities corresponding to the labels in the label association relationship map; determining medical entities to be consolidated based on the association information between the medical entities; merging the medical entities to be merged to obtain merged medical entities; outputting an integration result based on each merged medical entity; and outputting the label association relation map, the association relation among the medical entities and the medical entities to be combined as integrated teaching information. In the case of recommending medication data based on medical data, processing medical data based on the set of tags may include: and determining a drug label corresponding to the medical data from the label set, determining drug data matched with the drug label, and outputting the drug data.
With continued reference to fig. 3, a schematic diagram of one application scenario of a method for processing medical data according to the present application is shown. In the application scenario of fig. 3, the execution subject may first acquire the medical data 301, and the medical data 301 includes index information 3011, crowd information 3012, and examination item information 3013. The index information 3011 refers to a value of an index, such as index a: numerical values. Crowd information 3012 refers to patient information and may include, but is not limited to, age, gender. The examination item information 3013 refers to an item to be examined by the patient. Thereafter, medical entity information corresponding to the medical data 301 may be determined, wherein the medical entity information may include index information 3011 and crowd information 3012. The execution subject may determine crowd labels of the crowd information 3012 based on a preset crowd operator, and determine index labels of the index information 3011 based on a preset index operator, and summarize the crowd labels and the index labels to obtain a label set 302. Thereafter, the inspection item information 3013 may be subjected to data abnormality detection processing based on the index tag and the crowd tag, and it is detected whether or not an item that is in contradiction with the tag set 302 is included in the inspection item information 3013, and if so, a data abnormality prompt may be output.
According to the method for processing medical data, which is provided by the embodiment of the application, the medical data can be acquired, the medical entity information corresponding to the medical data is determined, and then the label set matched with the medical entity information is determined based on each preset operator, so that the multi-operator label set is obtained, the medical data is processed based on the label set, the processing requirements corresponding to the labels can be met for the processing of the medical data, for example, the examination item can be determined according to the disease label and the patient label during the processing of the medical advice data, and the medical data processing effect is improved.
With continued reference to fig. 4, a flow 400 of another embodiment of a method for processing medical data according to the present application is shown. As shown in fig. 4, the method for processing medical data of the present embodiment may include the steps of:
in step 401, medical data is acquired.
In this embodiment, the detailed description of step 401 is referred to the detailed description of step 201, and will not be repeated here.
Step 402, medical entity information corresponding to medical data is determined.
In this embodiment, the detailed description of step 402 is referred to the detailed description of step 202, and will not be repeated here.
Step 403, determining a label set matched with the medical entity information based on each preset operator.
In this embodiment, each preset operator corresponds to different label determining modes, and a label matched with medical entity information can be determined for each operator according to the label determining mode corresponding to the operator. For a detailed description of step 403, please refer to a detailed description of step 203, which is not described herein.
In some optional implementations of this embodiment, each preset operator includes at least a crowd operator; and determining a label set matched with the medical entity information based on each preset operator, wherein the label set comprises the following components: for each operator, determining a crowd label matched with the operator based on medical entity information and a pre-trained crowd classification model in response to determining that the operator is a crowd operator; based on the crowd labels, a set of labels that matches the medical entity information is determined.
In this implementation, the pre-trained crowd classification model is configured to output corresponding patient information based on the input medical entity, where the patient information includes at least gender and age. Thereafter, the executive may determine crowd labels, such as male, female, neonate, adult, etc., based on the patient information. Optionally, the crowd classification model may be a deep semantic model, and training steps of the crowd classification model are as follows: acquiring a sample set, wherein the sample set comprises medical entities with corresponding relations and crowd labels; inputting the medical entity into a model to be trained to obtain output data of the model to be trained; training a model to be trained based on the difference between the crowd labels corresponding to the medical entities and the output data to obtain a trained crowd classification model. In the actual use process of the crowd classification model, the crowd classification model can determine the probability that the information of the medical entity belongs to each crowd label, and the crowd labels matched with crowd operators are determined based on the crowd labels with high probability.
In some optional implementations of this embodiment, each preset operator includes at least an index operator; and determining a label set matched with the medical entity information based on each preset operator, wherein the label set comprises the following components: for each operator, responding to determining the operator as an index operator, and acquiring a preset index condition; determining an index label corresponding to a preset index condition in response to determining that the medical entity information is matched with the preset index condition; a set of labels that matches the medical entity information is determined based on the index labels.
In this implementation manner, the preset index condition and the index label are stored in the execution body correspondingly, which means that the preset index condition is satisfied and then the index label is satisfied. If the preset index condition is that the target parameter is greater than the threshold, the corresponding index label is the disease A, that is, if the preset index condition is met, the patient is indicated to have the disease A. Index information may be included in the medical entity information, the index information including at least an index category and an index value. If the index information is matched with the preset index condition, the index category is consistent with the category of the preset index condition, and the index numerical value meets the preset index condition, and at the moment, the medical entity information can be determined to have the index label.
It should be noted that if each preset operator includes a crowd operator and an index operator, crowd labels and index operators may be combined into a label set.
In some optional implementations of this embodiment, the preset index condition includes a central word sub-portion, a symbol sub-portion, a numeric sub-portion, and a unit sub-portion; the method further comprises the following steps: determining index center words and index units of medical entity information; responsive to determining that the index center word matches the center word subsection and the index unit matches the unit subsection, determining an index value at a location between the index center word and the index unit; sequentially splicing the index value, the symbol sub-part and the value sub-part to obtain a target expression; and in response to determining that the target expression is established, determining that the medical entity information matches a preset index condition.
In this implementation, the preset index condition may include a central word sub-portion, a symbol sub-portion, a numerical sub-portion, and a unit sub-portion. Wherein, the central word sub-part refers to a word part for describing the index category; the symbol sub-parts are symbols which are larger than, smaller than and the like and are used for representing the size relation; the numerical subsection refers to a threshold value corresponding to the index category; the unit subsection refers to a unit of numerical value. For example, the preset index condition may be "the target index is greater than the target threshold (unit)", where the target index is the central word subsection, and the target index is greater than the symbol subsection, and the target threshold is the numerical subsection, and the unit is the unit subsection. For medical entity information, it may comprise index information. The execution main body can firstly acquire the index center word, match the index center word with the center word sub-part in a regular matching mode, and acquire an index unit if the matching is successful. And carrying out forward search and/or backward search at the adjacent position of the central word subsection to determine the unit subsection. Matching the index unit with the unit sub-portion, if the matching is successful, determining an index value at a location between the index center word and the index unit, and determining a value sub-portion at a location between the center word sub-portion and the unit sub-portion. And then, splicing the index value, the symbol sub-part and the value sub-part to obtain a target expression, wherein the A is larger than the B. If the target expression is established, determining that the medical entity information is matched with a preset index condition, and determining an index label corresponding to the preset index condition.
Step 404, obtaining a preset medical relation chart.
In this embodiment, the preset medical relationship chart may include a relationship chart formed by any combination of a disease label, a drug label, and an inspection item label. Taking disease labels, medicines and medicine labels as examples, the preset medical relation graph can comprise nodes corresponding to the disease labels, the medicines and the medicine labels and communication paths among the nodes. The communication path in the medical relation graph is used for describing the association relation among the disease label, the medicine and the medicine label.
Step 405, determining path information between the medical data and the set of tags based on the medical relationship graph.
In the present embodiment, the medical data may include drug data, examination item data, and the like. The tag sets may include disease tags, drug tags, and the like. Based on the medical relation graph, path information between the medicine and the examination item in the medical data and the disease label and the medicine label can be determined, wherein the path information is used for describing the association relation between the medicine and the examination item in the medical data and the disease label and the medicine label. Further, the index tag in the tag set corresponds to a disease tag.
In step 406, in response to determining that the distance of the path information exceeds a preset distance threshold, it is determined that there is abnormal data in the medical data that matches the set of labels.
In this embodiment, the distance of the path information may be calculated based on the path information and a preset formula, where the preset formula is as follows:
P reminder ＝(P drug-tag +P disease-tag )/N α
wherein P is reminder Distance, P representing path information drug-tag Representing the edge weight between a drug node and a drug label node, i.e., the probability that the drug belongs to the drug label, P disease-tag Represents the edge weight between the disease label and the drug label, i.e. the probability that the disease belongs to the drug label, α represents the attenuation factor of the path length, and N represents the number of communication path nodes.
In this embodiment, the execution body may analyze the path information to obtain the number of nodes of the communication path, the edge weights between the nodes in the communication path, and the attenuation factors corresponding to the communication path, and then substitutes these parameters into the above preset formula to determine the distance of the path information. The execution subject may store a distance threshold in advance. If the distance of the path information exceeds the distance threshold value, the medical data and the label set are weakly associated, and abnormal data matched with the label set is determined to exist in the medical data, for example, a patient suffering from a certain disease is prescribed a drug contraindicated to the disease.
Step 407, in response to determining that there is no correspondence between the medical data and the tag set in the preset correspondence table, determining that there is abnormal data matching the tag set in the medical data.
In this embodiment, the preset correspondence table may store information such as reasonable drugs and reasonable inspection items corresponding to each label. If the corresponding relation between the medical data and the tag set does not exist in the corresponding relation table, the fact that the medicine and/or the check item in the medical data belong to the unreasonable medicine and/or the check item corresponding to the tag set is indicated, and abnormal data matched with the tag set exists in the medical data.
Step 408, outputting a data anomaly alert in response to determining that there is anomalous data in the medical data that matches the set of labels.
In this embodiment, the execution body may output a data abnormality alert when it is determined that abnormal data exists in the medical data, so as to alert in time when abnormality occurs in the medical advice data, abnormality occurs in the drug data, and the like, thereby ensuring medical safety.
The method for processing medical data provided in the foregoing embodiment of the present application may further determine, according to a corresponding tag determination manner, a crowd tag and an index tag for the crowd operator and the index operator, and generate a tag set. And determining the relevance between the label set and the medical data based on the medical relation graph and the corresponding relation table, and outputting data abnormality reminding in time under a weakly-relevant scene to ensure medical safety.
With further reference to fig. 5, as an implementation of the method shown in the above figures, the present application provides an embodiment of an apparatus for processing medical data, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable to various servers.
As shown in fig. 5, the apparatus 500 for processing medical data of the present embodiment includes: a data acquisition unit 501, an information determination unit 502, a set determination unit 503, a data processing unit 504.
The data acquisition unit 501 is configured to acquire medical data.
The information determination unit 502 is configured to determine medical entity information corresponding to the medical data.
The set determining unit 503 is configured to determine a set of labels matching the medical entity information based on preset respective operators.
A data processing unit 504 configured to process medical data based on the set of tags.
In some optional implementations of this embodiment, each preset operator includes at least a crowd operator; and the set determination unit 503 is further configured to: for each operator, determining a crowd label matched with the operator based on medical entity information and a pre-trained crowd classification model in response to determining that the operator is a crowd operator; based on the crowd labels, a set of labels that matches the medical entity information is determined.
In some optional implementations of this embodiment, the preset operators include at least index operators; and the set determination unit 503 is further configured to: for each operator, responding to determining the operator as an index operator, and acquiring a preset index condition; determining an index label corresponding to a preset index condition in response to determining that the medical entity information is matched with the preset index condition; a set of labels that matches the medical entity information is determined based on the index labels.
In some optional implementations of this embodiment, the preset index condition includes a central word sub-portion, a symbol sub-portion, a numeric sub-portion, and a unit sub-portion; the apparatus further comprises: an index determination unit configured to determine an index center word and an index unit of medical entity information; a numerical value determining unit configured to determine an index numerical value at a position between the index center word and the index unit in response to determining that the index center word matches the center word subsection and that the index unit matches the unit subsection; the splicing unit is configured to splice the index value, the symbol sub-part and the value sub-part in sequence to obtain a target expression; and a matching determination unit configured to determine that the medical entity information matches a preset index condition in response to determining that the target expression is established.
In some alternative implementations of the present embodiment, the data processing unit 504 is further configured to: and outputting a data abnormality alert in response to determining that abnormal data matched with the tag set exists in the medical data.
In some optional implementations of this embodiment, the apparatus further includes: and a first condition judgment unit configured to determine that abnormal data matched with the tag set exists in the medical data in response to determining that the correspondence between the medical data and the tag set does not exist in the preset correspondence table.
In some optional implementations of this embodiment, the apparatus further includes: a relationship graph acquisition unit configured to acquire a preset medical relationship graph; a path determination unit configured to determine path information between the medical data and the tag set based on the medical relation graph; and a second condition judgment unit configured to determine that abnormal data matching the tag set exists in the medical data in response to determining that the distance of the path information exceeds a preset distance threshold.
It should be understood that the units 501 to 504 recited in the apparatus 500 for processing medical data correspond to the respective steps in the method described with reference to fig. 2. Thus, the operations and features described above with respect to the method of processing medical data are equally applicable to the apparatus 500 and the units contained therein and are not described in detail herein.
According to embodiments of the present application, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
Fig. 6 illustrates a block diagram of an electronic device 600 for implementing a method for processing medical data according to an embodiment of the disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the apparatus 600 includes a computing unit 601 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 may also be stored. The computing unit 601, ROM 602, and RAM 603 are connected to each other by a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Various components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, mouse, etc.; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as methods for processing medical data. For example, in some embodiments, the method for processing medical data may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the method for processing medical data described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the method for processing medical data by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
It should be noted that, the acquisition of various medical data in the present disclosure accords with the rules of relevant laws and regulations, and does not violate the well-known order.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (12)
1. A method for processing medical data, comprising:
acquiring medical data;
determining medical entity information corresponding to the medical data;
determining a label set matched with the medical entity information based on preset operators, wherein the label set comprises the following steps: for each operator, responding to determining the operator as an index operator, and acquiring a preset index condition; responsive to determining that the medical entity information matches the preset index condition, determining an index tag corresponding to the preset index condition; determining a label set matched with the medical entity information based on the index labels, wherein each preset operator at least comprises an index operator;
processing the medical data based on the set of tags;
the preset index condition comprises a central word sub-part, a symbol sub-part, a numerical value sub-part and a unit sub-part; the method further comprises the steps of: determining index center words and index units of the medical entity information; responsive to determining that the index center word matches the center word subsection and the index unit matches the unit subsection, determining an index value between the index center word and the index unit; sequentially splicing the index value, the symbol sub-part and the value sub-part to obtain a target expression; in response to determining that the target expression is true, determining that the medical entity information matches the preset index condition.
2. The method of claim 1, wherein the preset individual operators comprise at least crowd operators; and
the determining a label set matched with the medical entity information based on each preset operator comprises the following steps:
for each operator, determining a crowd label matched with the operator based on the medical entity information and a pre-trained crowd classification model in response to determining that the operator is the crowd operator;
based on the crowd labels, a set of labels that matches the medical entity information is determined.
3. The method of claim 1, wherein the processing the medical data based on the set of tags comprises:
and outputting a data abnormality reminder in response to determining that abnormal data matched with the tag set exists in the medical data.
4. A method according to claim 3, wherein the method further comprises:
and determining that abnormal data matched with the tag set exists in the medical data in response to determining that the corresponding relation between the medical data and the tag set does not exist in a preset corresponding relation table.
5. A method according to claim 3, wherein the method further comprises:
acquiring a preset medical relation diagram;
determining path information between the medical data and the set of labels based on the medical relationship graph;
and in response to determining that the distance of the path information exceeds a preset distance threshold, determining that abnormal data matched with the tag set exists in the medical data.
6. An apparatus for processing medical data, comprising:
a data acquisition unit configured to acquire medical data;
an information determination unit configured to determine medical entity information corresponding to the medical data;
a set determining unit configured to determine a set of labels matching the medical entity information based on preset respective operators;
a data processing unit configured to process the medical data based on the set of tags;
wherein each preset operator at least comprises an index operator; and the set determination unit is further configured to: for each operator, responding to the determination that the operator is the index operator, and acquiring a preset index condition; responsive to determining that the medical entity information matches the preset index condition, determining an index tag corresponding to the preset index condition; determining a set of labels matching the medical entity information based on the index labels;
the preset index condition comprises a central word sub-part, a symbol sub-part, a numerical value sub-part and a unit sub-part; the apparatus further comprises: an index determination unit configured to determine an index center word and an index unit of the medical entity information; a numerical value determining unit configured to determine an index numerical value in response to determining that the index center word matches the center word subsection and the index unit matches the unit subsection, at a position between the index center word and the index unit; the splicing unit is configured to splice the index value, the symbol sub-part and the value sub-part in sequence to obtain a target expression; and a matching determination unit configured to determine that the medical entity information matches the preset index condition in response to determining that the target expression is established.
7. The apparatus of claim 6, wherein the preset individual operators comprise at least crowd operators; and
the set determination unit is further configured to:
for each operator, determining a crowd label matched with the operator based on the medical entity information and a pre-trained crowd classification model in response to determining that the operator is the crowd operator;
based on the crowd labels, a set of labels that matches the medical entity information is determined.
8. The apparatus of claim 6, wherein the data processing unit is further configured to:
and outputting a data abnormality reminder in response to determining that abnormal data matched with the tag set exists in the medical data.
9. The apparatus of claim 8, wherein the apparatus further comprises:
a first condition judgment unit configured to determine that abnormal data matching the tag set exists in the medical data in response to determining that a correspondence between the medical data and the tag set does not exist in a preset correspondence table.
10. The apparatus of claim 8, wherein the apparatus further comprises:
a relationship graph acquisition unit configured to acquire a preset medical relationship graph;
a path determination unit configured to determine path information between the medical data and the set of labels based on the medical relation graph;
and a second condition judgment unit configured to determine that abnormal data matching the tag set exists in the medical data in response to determining that the distance of the path information exceeds a preset distance threshold.
11. An electronic device that performs a method for processing medical data, comprising:
at least one computing unit; and
a storage unit in communication with the at least one computing unit; wherein,,
the storage unit stores instructions executable by the at least one computing unit to enable the at least one computing unit to perform the method of any one of claims 1-5.
12. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-5.
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