CN117198477A - Method for determining abnormal medical consumption item, related device and computer program product - Google Patents

Abstract

The present disclosure provides a method, apparatus, electronic device, computer readable storage medium and computer program product for determining abnormal medical consumption items, relating to the technical field of artificial intelligence such as smart medical treatment, natural language processing, big data technology, etc. One embodiment of the method comprises the following steps: an actual diagnosis item and an actual medical consumption item are extracted from case information, then, a reference medical consumption item corresponding to the actual diagnosis item is determined, and finally, an actual medical consumption item different from the reference medical consumption item is determined as an abnormal medical consumption item. The embodiment provides a method for determining abnormal medical consumption items, which can screen recorded actual medical consumption items with low association degree in cases based on actual diagnosis items so as to assist in medical decision making and improve the use quality of case information.

Description

Method for determining abnormal medical consumption item, related device and computer program product

Technical Field

The present disclosure relates to the field of computer technology, and in particular, to the field of artificial intelligence technologies such as smart medicine, natural language processing, and big data technology, and more particularly, to a method, an apparatus, an electronic device, a computer readable storage medium, and a computer program product for determining abnormal medical consumption items.

Background

In the medical field, the diagnosis items are required to be obtained based on a diagnosis chain formed by a plurality of medical consumption items, wherein the medical consumption items refer to various charge items in the diagnosis and treatment process, such as examination, treatment, anesthesia, operation, medicines, consumable items and the like, and the medical consumption items can be collectively called as "medical consumption items".

In practice, the case information is highly concentrated in the information such as the medical consumption item and the diagnosis item, so that a doctor can re-etch and reproduce the diagnosis item obtaining process of a patient, so that the secondary decision is made after the diagnosis process of the patient is known based on the re-present process, the quality of the obtained diagnosis item can be directly influenced by the correct selection of the medical consumption item, and the correct selection of the medical consumption item information is also closely related to the contents such as the standardization of medical resource utilization and the rationalization of charge.

Disclosure of Invention

Embodiments of the present disclosure provide a method, apparatus, electronic device, computer-readable storage medium, and computer program product for determining abnormal medical consumption items.

In a first aspect, an embodiment of the present disclosure proposes a method of determining an abnormal medical consumption item, including: extracting an actual diagnosis item and an actual medical consumption item from the case information; determining a reference medical consumption item corresponding to the actual diagnostic item; an actual medical consumption item different from the reference medical consumption item is determined as an abnormal medical consumption item.

In a second aspect, embodiments of the present disclosure provide an apparatus for determining abnormal medical consumption items, comprising: a case information extraction unit configured to extract an actual diagnosis item and an actual medical consumption item from the case information; a reference medical consumption item recall unit configured to determine a reference medical consumption item corresponding to the actual diagnostic item; an abnormal medical consumption item determination unit configured to determine an actual medical consumption item different from the reference medical consumption item as an abnormal medical consumption item.

In a third aspect, an embodiment of the present disclosure provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to implement a method of determining abnormal medical consumption items as described in any one of the implementations of the first aspect when executed.

In a fourth aspect, embodiments of the present disclosure provide a non-transitory computer-readable storage medium storing computer instructions for enabling a computer to implement a method of determining abnormal medical consumption items as described in any of the implementations of the first aspect when executed.

In a fifth aspect, embodiments of the present disclosure provide a computer program product comprising a computer program which, when executed by a processor, is capable of implementing a method of determining an abnormal medical consumption item as described in any of the implementations of the first aspect.

The embodiment of the disclosure provides a method, a device, an electronic device, a computer readable storage medium and a computer program product for determining abnormal medical consumption items, wherein an actual diagnosis item and an actual medical consumption item are extracted from case information, then a reference medical consumption item corresponding to the actual diagnosis item is determined, and finally an actual medical consumption item different from the reference medical consumption item is determined as the abnormal medical consumption item.

The method and the device can screen and recheck each actual medical consumption item included in the case information based on the reference medical consumption item corresponding to the actual diagnosis item included in the case information so as to determine the abnormal medical consumption item which is included in the case information and has lower association degree with the actual diagnosis item and weaker reference value, so that interference of the abnormal medical consumption item on related decision and recheck of the actual diagnosis item is avoided, medical decision is assisted, and the use quality of the case information 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

Other features, objects and advantages of the present disclosure will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings:

FIG. 1 is an exemplary system architecture in which the present disclosure may be applied;

FIG. 2 is a flow chart of a method of determining abnormal medical consumption items provided by an embodiment of the present disclosure;

FIG. 3 is a flow chart of another method of determining abnormal medical consumption items provided by an embodiment of the present disclosure;

FIG. 4 is a schematic diagram showing the effect of a knowledge graph in medicine in one implementation manner according to the embodiments of the present disclosure;

FIG. 5 is a flowchart of a method for determining abnormal medical consumption items in an application scenario provided by an embodiment of the present disclosure;

FIG. 6 is a block diagram of a device for determining abnormal medical consumption items according to an embodiment of the present disclosure;

fig. 7 is a schematic structural diagram of an electronic device adapted to perform a method for determining abnormal medical consumption items 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, without conflict, the embodiments of the present disclosure and features of the embodiments may be combined with each other.

In addition, in the technical scheme related to the disclosure, the related processes of acquiring, storing, using, processing, transporting, providing, disclosing and the like of the personal information of the user all conform to the regulations of related laws and regulations and do not violate the popular regulations of the public order.

FIG. 1 illustrates an exemplary system architecture 100 in which embodiments of the present disclosure of methods, apparatus, electronic devices, and computer-readable storage media for determining abnormal medical consumption items may be applied.

As shown in fig. 1, a 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. Various applications for implementing information communication between the terminal devices 101, 102, 103 and the server 105, such as a case resolution type application, a medical evaluation type application, an instant messaging type application, and the like, may be installed on the terminal devices.

The terminal devices 101, 102, 103 and the server 105 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices with display screens, including but not limited to smartphones, tablets, laptop and desktop computers, etc.; when the terminal devices 101, 102, 103 are software, they may be installed in the above-listed electronic devices, which may be implemented as a plurality of software or software modules, or may be implemented as a single software or software module, which is not particularly limited herein. When the server 105 is hardware, it may be implemented as a distributed server cluster formed by a plurality of servers, or may be implemented as a single server; when the server is software, the server may be implemented as a plurality of software or software modules, or may be implemented as a single software or software module, which is not particularly limited herein.

The server 105 can provide various services through various built-in applications, and, for example, a case resolution application that can provide evaluation of abnormal medical consumption items in case information, the server 105 can achieve the following effects when running the case resolution application: firstly, acquiring case information from terminal devices 101, 102, 103 through a network 104, and extracting an actual diagnosis item and an actual medical consumption from the case information; then, the server 105 determines a reference medical consumption item corresponding to the actual diagnosis item; finally, the server 105 determines an actual medical consumption item different from the reference medical consumption item as an abnormal medical consumption item.

It is to be noted that the case information may be stored in advance in the server 105 in various ways, in addition to being acquired from the terminal apparatuses 101, 102, 103 through the network 104. Thus, when the server 105 detects that such data has been stored locally (e.g., a task to determine abnormal medical consumption items that remains prior to beginning processing), such data may optionally be obtained directly from locally, in which case the exemplary system architecture 100 may not include the terminal devices 101, 102, 103 and the network 104.

Since the extraction of the actual diagnosis items and the actual medical consumption items from the case information, and the storage of the reference medical items corresponding to the different diagnosis items require more memory resources and stronger computing power, the method for determining the abnormal medical consumption items provided in the subsequent embodiments of the present disclosure is generally performed by the server 105 having stronger computing power and more computing resources, and accordingly, the device for determining the abnormal medical consumption items is generally also provided in the server 105. However, it should be noted that, when the terminal devices 101, 102, 103 also have the required computing capability and computing resources, the terminal devices 101, 102, 103 may complete each operation performed by the server 105 through the case resolution application installed thereon, and further output the same result as the server 105. Particularly, in the case where there are a plurality of terminal devices having different computing capabilities at the same time, when the case resolution application determines that the terminal device has a higher computing capability and more computing resources remain, the terminal device may be allowed to perform the above-described computation, so that the computing pressure of the server 105 is appropriately reduced, and accordingly, the device for determining the abnormal medical consumption item may be provided in the terminal devices 101, 102, 103. In this case, the exemplary system architecture 100 may also not include the server 105 and the network 104.

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.

Referring to fig. 2, fig. 2 is a flowchart of a method for determining abnormal medical consumption items according to an embodiment of the disclosure, wherein the flowchart 200 includes the following steps:

in step 201, the actual diagnosis items and the actual medical consumption items are extracted from the case information.

In the present embodiment, after the case information is acquired by the execution subject (e.g., the server 105 shown in fig. 1) of the method of determining abnormal medical consumption items, the actual diagnosis items and the actual medical consumption items are extracted from the case information, in practice, the respective actual diagnosis item areas and the actual medical consumption items may be configured by the text areas in the corresponding case information so that the contents included in the corresponding areas in the case information may be directly determined as the actual diagnosis items, the actual medical consumption items.

Further, after each text content is extracted from the case information, classification and extraction of the actual diagnosis item and the actual medical consumption item can be correspondingly realized after determining that the text content belongs to the actual diagnosis item or the actual medical consumption item based on the semantic analysis result of each text content.

It should be noted that the case information may be obtained directly from a local storage device by the execution subject, or may be obtained from a non-local storage device (for example, the terminal devices 101, 102, 103 shown in fig. 1). The local storage device may be a data storage module, such as a server hard disk, provided in the execution body, in which case information may be read quickly locally; the non-local storage device may also be any other electronic device arranged for storing data, such as some user terminals or the like, in which case the executing entity may acquire the required case information by sending an acquisition command to the electronic device.

Step 202, a reference medical consumption item corresponding to the actual diagnostic item is determined.

In this embodiment, after an actual diagnosis item is extracted from case information based on the above step 201, a reference medical consumption item corresponding to the actual diagnosis item is determined, where the reference medical consumption item is a medical consumption item having an association relationship with the diagnosis item, that is, a medical consumption item for determining and treating the actual diagnosis item, and the reference medical consumption item corresponding to a different diagnosis item may be determined based on medical data, historical case data, and the like, where the confidence level satisfies the requirements, for example, when the actual diagnosis item is determined to be "pain caused by cold", and the reference medical consumption item corresponding to the actual diagnosis item is determined to be "blood routine test", "acetaminophen tablet", and the like.

Step 203, determining an actual medical consumption item different from the reference medical consumption item as an abnormal medical consumption item.

In this embodiment, after the reference medical consumption item corresponding to the actual diagnostic item is acquired based on the above step 202, the difference between the reference medical consumption item and the actual medical consumption item determined based on step 201 is compared, and the actual medical consumption item different from the reference medical consumption item is determined as an abnormal medical consumption item for characterizing and indicating the actual medical consumption item having a degree of association with the actual diagnostic item lower than the preset threshold.

In practice, when it is determined that the number of reference medical consumption items corresponding to the actual diagnostic item is plural, a reference medical consumption item set may be constructed accordingly so that no actual medical consumption item in the medical consumption item set is determined as an abnormal medical consumption item.

Furthermore, the reference medical consumption item which is not embodied as the actual medical consumption item can be extracted and fed back so as to prompt whether the actual medical consumption item is missing or missing in the process of determining the actual diagnosis item, so that medical decision is assisted, and the use quality of case information is improved.

According to the method for determining the abnormal medical consumption item, which is provided by the embodiment of the disclosure, each actual medical consumption item included in the case information can be screened and rechecked based on the reference medical consumption item corresponding to the actual diagnosis item included in the case information, so that the abnormal medical consumption item which is included in the case information and has low relevance to the actual diagnosis item and weak reference value can be determined, interference of the abnormal medical consumption item on relevant decision and rechecking of the actual diagnosis item can be avoided, medical decision is assisted, and the use quality of the case information is improved.

In some optional implementations of the present embodiment, the method of determining an abnormal medical consumption item further includes: in response to the presence of a plurality of the actual diagnostic items, a priority sequence of each of the actual diagnostic items is determined based on the number of actual medical consumption items included in the reference medical consumption item to which each of the actual diagnostic items corresponds.

Specifically, when a plurality of actual diagnosis items exist in the case information, after the reference medical consumption items corresponding to the actual diagnosis items are respectively acquired, the actual diagnosis items are prioritized based on the number of the actual medical consumption items included in the reference medical consumption items, that is, the actual diagnosis item with the highest actual diagnosis item number including the actual diagnosis items in the corresponding reference medical consumption items is determined to be the actual diagnosis item or the main diagnosis item with the highest priority, the priorities of the actual diagnosis items are sequentially determined, and a priority sequence of the actual diagnosis items included in the case information is generated, so that a user of the case information can know the priorities of the actual diagnosis items included in the case information according to the priority sequence, and assist the user in making a diagnosis policy.

In some optional implementations of the present embodiment, the method of determining an abnormal medical consumption item further includes: calculating the ratio of the number of the abnormal medical consumption items to the total number of the actual medical consumption items; generating evaluation information according to the ratio; wherein the evaluation information is used for indicating the association degree between the actual diagnosis item and the actual medical consumption item.

Specifically, the evaluation information may also be generated based on a ratio of the number of abnormal medical consumption items to the total number of actual medical consumption items, so as to feed back the degree of association between the actual diagnosis items and the actual medical consumption items by using the evaluation information, so as to evaluate the quality of the case information.

Further, a plurality of ratio value intervals corresponding to the evaluation grades may be configured in advance, for example, when the ratio falls into the interval [0,0.2 ], the quality of the case information is determined to be excellent, when the ratio falls into the interval [0.2,0.3 ], the quality of the case information is determined to be excellent, when the ratio falls into the interval [0.3, 0.4), the quality of the case information is determined to be qualified, and when the ratio falls into the interval [0.4,1], the quality of the case information is determined to be unqualified.

Referring to fig. 3, fig. 3 is a flowchart of another method for determining abnormal medical consumption items according to an embodiment of the present disclosure, wherein the flowchart 300 includes the following steps:

Step 301, a diagnosis item entity and a medical consumption item entity described in case information are extracted.

In this example, similar to the example shown in fig. 2, the diagnostic term entity and the medical term entity described in the case information are directly extracted, and the medical term entity is, for example, "community-acquired pneumonia, non-severe", "chest X-ray computerized tomography (Computed Tomography, abbreviated as CT) plain scan", "antinuclear antibody measurement", "laparoscopic holohecthymectomy" and "cefuroxime sodium for injection".

Step 302, using a pre-configured entity core word recognition model to process the diagnosis item entity and the medical consumption item entity respectively, so as to obtain a diagnosis item core word and a medical consumption item core word.

In this embodiment, the diagnosis item entity and the medical consumption item entity are processed by using a pre-configured entity core word recognition model, respectively, to obtain a diagnosis item core word and a medical consumption item core word, where the core word has different extraction criteria for different entity types, for example, when the entity type is diagnosis, the core word is composed of a lesion site and a disease description, when the entity type is examination, the core word is composed of a lesion site and an examination category, when the entity type is examination, the core word is composed of a core examination item, when the entity type is operation, the core word is composed of a lesion site and an operation mode, when the entity type is medication, the core word is composed of core components of a drug, it should be understood that, the entity type can be determined based on a pre-configured entity type recognition model or an entity type recognition layer of the entity core word recognition model, or the entity core word recognition model can be trained by using a training sample with an entity type mark, so that the entity core word recognition model has the capability of recognizing the entity type, and the method can correspondingly treat the acquired pneumonia of the medical consumption item entity community, obtain the medical consumption item core word pneumonia without severe symptoms, and obtain the medical consumption item core word chest CT by chest X-ray computer body layer (CT) flat scanning, treat the medical consumption item entity anti-nuclear antibody assay (antinsensibody, simply called ANA) to obtain the medical consumption item core word anti-nuclear antibody, treat the medical consumption item entity cefuroxime sodium for injection to obtain the medical consumption item core word cefuroxime sodium, processing the medical consumption item entity through the laparoscope female total hysterectomy to obtain a medical consumption item core word hysterectomy, and obtaining an entity-core word corresponding relation table shown in table 1.

TABLE 1

The entity core word recognition model can be obtained by training based on basic models such as a language characterization model (Bidirectional Encoder Representation From Transformers, BERT for short) or a named entity recognition model (Named Entity Recognition, NER for short).

Step 303, determining the diagnosis item core word as an actual diagnosis item and determining the medical consumption item core word as an actual medical consumption item.

In this embodiment, after the diagnosis item entity and the medical consumption item entity are completed based on the above step 302, the diagnosis item core word and the medical consumption item core word are obtained, and each diagnosis item core word is determined as an actual diagnosis item, and each medical consumption item core word is determined as an actual medical consumption item.

Step 304, a reference medical consumption item corresponding to the actual diagnostic item is determined.

In step 305, an actual medical consumption item different from the reference medical consumption item is determined as an abnormal medical consumption item.

The above steps 304-305 are consistent with the steps 202-203 shown in fig. 2, and the same parts are not repeated herein, and the implementation is further based on the embodiment shown in fig. 2, in which the entity core word recognition model is used to process the diagnosis item entity and the medical consumption item entity recorded in the case information, so as to obtain the diagnosis item core word and the medical consumption item core word with more condensed content and higher value, and then determine the diagnosis item core word as the actual diagnosis item and the medical consumption item core word as the actual medical consumption item, so that the quality comparison of the method for determining the abnormal medical consumption item provided in the embodiment compared with the method for determining the abnormal medical consumption item provided in fig. 2 can be shown in table 2.

Policy method	Accuracy rate of	Recall rate of recall	Evaluation score
				FIG. 2 shows an embodiment	92.0％	33.0％	0.485
FIG. 3 shows an embodiment	92.6％	67.5％	0.781

TABLE 2

In some optional implementations of this embodiment, the processing the diagnostic item entity and the medical consumption item entity to obtain the diagnostic item core word and the medical consumption item core word using a pre-configured entity core word recognition model includes: the diagnosis item entity and the medical consumption item entity are respectively subjected to word segmentation processing by utilizing a word segmentation layer of the entity core word recognition model, so that word segmentation processing results are obtained; carrying out semantic analysis on the word segmentation processing result by utilizing a semantic understanding layer of the entity core word recognition model to obtain a semantic analysis result; and processing the semantic analysis result by utilizing a conditional random field layer of the entity core word recognition model to obtain the diagnosis item core word and the medical consumption item core word.

Specifically, the preconfigured entity core word recognition model at least comprises a word division layer, a semantic understanding layer (Ernie) and a Conditional Random Field (CRF), when a diagnosis item entity and a medical consumption item entity are obtained, the word division layer is sequentially utilized to carry out word division processing on the diagnosis item entity and the medical consumption item entity number, the semantic understanding layer is utilized to carry out semantic analysis on a processing result of the word division layer, the conditional random field layer is utilized to process a semantic analysis result, and finally a diagnosis item core word and a medical consumption item core word are obtained, so that the purpose of extracting the core word of the diagnosis item entity and the core word of the medical consumption item entity and obtaining the diagnosis item core word and the medical consumption item core word is achieved through the entity core word recognition model based on the word division layer, the semantic understanding layer (Ernie) and the Conditional Random Field (CRF).

On the basis of any one of the above embodiments, the method for determining an abnormal medical consumption item, determining a reference medical consumption item corresponding to the actual diagnostic item, includes: determining a reference medical consumption item corresponding to the actual diagnosis item based on a pre-configured correspondence table; wherein, the corresponding table records the corresponding relation between different diagnosis items and different reference medical consumption items.

Specifically, the reference medical consumption item corresponding to the actual diagnosis item is obtained by using a pre-configured correspondence table in which correspondence between each diagnosis item and the corresponding reference medical consumption item is recorded, so that the reference medical consumption item corresponding to each diagnosis item (actual diagnosis item) is determined by repeatedly using the correspondence table, and the efficiency of determining the reference medical consumption item corresponding to the actual diagnosis item is improved.

Further, in some embodiments, the entity core word recognition model may also be used to configure the correspondence table to improve the quality of the correspondence table, where the method for determining abnormal medical consumption items further includes: extracting a diagnosis item entity and a corresponding reference medical consumption item entity from the medical knowledge graph; respectively processing the diagnosis item entity and the reference medical consumption item entity by using a preset entity core word recognition model to obtain diagnosis item core words and reference medical consumption item core words; determining the diagnostic item core word as the diagnostic item, and determining the reference medical consumption item core word as the reference medical consumption item; the correspondence table is constructed based on the correspondence between the diagnostic item and the reference medical consumption item.

Specifically, a medical book based on confidence meeting requirements, an existing medical knowledge graph of a corresponding relationship between a diagnostic item and a medical consumption item may be used to extract a diagnostic item entity and a corresponding reference medical consumption item entity, such as a medical knowledge graph of a lung cancer illustrated in fig. 4, and each diagnostic item entity and the reference medical consumption item entity are processed by using a pre-configured entity core word recognition model, so as to obtain a diagnostic item core word corresponding to the diagnostic item entity and a reference medical consumption item core word corresponding to the reference medical consumption item entity, the diagnostic item core word and the medical consumption item core word are respectively determined as a diagnostic item and a reference medical consumption item, and the corresponding table is constructed based on the corresponding relationship between the diagnostic item and the reference medical consumption item, where the entity core word recognition model is the same as the entity core word recognition model described in the embodiment and the related implementation manner illustrated in fig. 3, and is not repeated here, so that the diagnostic item and the reference medical consumption item constructing the corresponding table are processed by the entity core word recognition model, thereby improving the quality of the corresponding relationship table, and increasing the use value of the corresponding table.

In some optional embodiments, in the process of constructing the correspondence table based on the medical knowledge graph, a plurality of historical standard case information with confidence meeting requirements can be used for constructing a plurality of different medical consumption item combinations corresponding to the same diagnosis item, wherein the medical consumption item combination comprises at least one medical consumption item, the same diagnosis item can be obtained based on any medical consumption item combination, the medical consumption items included in each medical consumption item combination have differences in at least one medical consumption item from the medical consumption items included in other medical consumption item combinations, so that in the step of determining the reference medical consumption item corresponding to the actual diagnosis item by using the correspondence table, the number of the actual medical consumption items hit by each medical consumption item combination is used for determining the medical consumption item combination with the highest diagnosis logic similarity with the actual medical consumption item recorded in the case information, and the medical consumption item included in the medical consumption item combination is used as the reference medical consumption item, so as to avoid the false identification of abnormal medical consumption items due to different diagnosis logic.

To further understand, the disclosure further provides a specific implementation scheme in combination with a specific application scenario, which includes constructing a correspondence table for determining a reference medical consumption item, and determining two parts of abnormal medical consumption items existing in case information by using the correspondence table, please refer to a flow 500 shown in fig. 5, specifically as follows:

For convenience of explanation, the diagnosis item is taken as lung cancer as an example.

For constructing a correspondence table portion for determining a reference medical consumption item:

first, a diagnosis item entity of lobar pneumonia and a corresponding reference medical consumption item entity of chest X-ray computer body layer (CT) plain scan are extracted from a medical knowledge graph.

Then, the entity core word recognition model is utilized to process diagnosis item entity 'lobar pneumonia', reference medical consumption item entity 'chest X-ray computer body layer (CT) flat scan', and diagnosis item core word 'pneumonia' and reference medical consumption item core word 'chest CT' are obtained.

Finally, the diagnosis item core word "pneumonia" and the reference medical consumption item core word "chest CT" are determined as the diagnosis item and the reference medical consumption item, and a correspondence table is constructed based on the correspondence between the "pneumonia" and the "chest CT".

For determining an abnormal medical consumption item portion existing in the case information using the correspondence table:

first, a diagnostic entity "community acquired pneumonia" and a medical wasting entity "chest X-ray Computer Tomography (CT) pan", "antinuclear antibody assay" are extracted from case information.

Then, the entity core word recognition model is utilized to process diagnosis entity 'community acquired pneumonia' and medical consumption entity 'chest X-ray computer body layer (CT) plain scan' and 'anti-nuclear antibody measurement', so as to obtain diagnosis core words 'pneumonia' and medical consumption item core words 'chest CT' and 'anti-nuclear antibody', the diagnosis core words 'pneumonia' are determined to be actual diagnosis items, and the medical consumption item core words 'chest CT' and 'anti-nuclear antibody' are determined to be actual medical consumption items.

Finally, a reference medical consumption item "chest CT" corresponding to the actual diagnosis item is determined using the correspondence table constructed in the above-described part, and an actual medical consumption item "antinuclear antibody" different from the reference medical consumption item is determined as an abnormal medical consumption item.

With further reference to fig. 6, as an implementation of the method shown in the above figures, the present disclosure provides an embodiment of an apparatus for determining abnormal medical consumption items, which corresponds to the method embodiment shown in fig. 2, and which is particularly applicable to various electronic devices.

As shown in fig. 6, the determination of the abnormal medical consumption item apparatus 600 of the present embodiment may include: a case information extraction unit 601, a reference medical consumption item recall unit 602, and an abnormal medical consumption item determination unit 603. Wherein the case information extraction unit 601 is configured to extract an actual diagnosis item and an actual medical consumption item from the case information; a reference medical consumption item recall unit 602 configured to determine a reference medical consumption item corresponding to the actual diagnostic item; an abnormal medical consumption item determination unit 603 configured to determine an actual medical consumption item different from the reference medical consumption item as an abnormal medical consumption item.

In the present embodiment, in the determination of the abnormal medical consumption item apparatus 600: the specific processing of the case information extraction unit 601, the reference medical consumption item recall unit 602, and the abnormal medical consumption item determination unit 603 and the technical effects thereof may be respectively described with reference to steps 201 to 203 in the corresponding embodiment of fig. 2, and will not be described herein.

In some optional implementations of the present embodiment, the case information extraction unit 601 includes an entity information extraction subunit configured to extract a diagnostic item entity and a medical consumption item entity described in the case information; the core word extraction subunit is configured to respectively process the diagnosis item entity and the medical consumption item entity by utilizing a pre-configured entity core word recognition model to obtain a diagnosis item core word and a medical consumption item core word; an actual information determination subunit configured to determine the diagnostic item core word as the actual diagnostic item and the medical consumption item core word as the actual medical consumption item.

In some optional implementations of this embodiment, the core word extraction subunit includes: the first model calling module is configured to utilize the word segmentation layer of the entity core word recognition model to respectively perform word segmentation processing on the diagnosis item entity and the medical consumption item entity to obtain word segmentation processing results; the second model calling module is configured to utilize a semantic understanding layer of the entity core word recognition model to carry out semantic analysis on the word segmentation processing result so as to obtain a semantic analysis result; and the third model calling module is configured to process the semantic analysis result by utilizing the conditional random field layer of the entity core word recognition model to obtain the diagnosis item core word and the medical consumption item core word.

In some optional implementations of the present embodiment, the reference healthcare consumption item recall unit 602 is further configured to determine a reference healthcare consumption item corresponding to the actual diagnostic item based on a pre-configured correspondence table; wherein, the corresponding table records the corresponding relation between different diagnosis items and different reference medical consumption items.

In some optional implementations of the present embodiment, the determining abnormal medical consumption item apparatus 600 further includes: a knowledge graph invoking unit configured to extract a diagnosis item entity and a corresponding reference medical consumption item entity from the medical knowledge graph; the reference core word extraction unit is configured to respectively process the diagnosis item entity and the reference medical consumption item entity by utilizing a preset entity core word recognition model to obtain a diagnosis item core word and a reference medical consumption item core word; a reference information determining unit configured to determine the diagnosis item core word as the diagnosis item, and determine the reference medical consumption item core word as the reference medical consumption item; and a correspondence table construction unit configured to construct the correspondence table based on a correspondence relationship between the diagnostic item and the reference medical consumption item.

In some optional implementations of the present embodiment, the determining abnormal medical consumption item apparatus 600 further includes: a diagnosis item priority determining unit configured to determine, in response to the presence of a plurality of the actual diagnosis items, a priority sequence of each of the actual diagnosis items based on the number of the actual medical consumption items included in the reference medical consumption item to which each of the actual diagnosis items corresponds.

In some optional implementations of the present embodiment, the determining abnormal medical consumption item apparatus 600 further includes: an abnormal duty ratio calculation unit configured to calculate a ratio of the number of the abnormal medical consumption items to the total number of the actual medical consumption items; an evaluation information generation unit configured to generate evaluation information according to the ratio; wherein the evaluation information is used for indicating the association degree between the actual diagnosis item and the actual medical consumption item.

The device for determining abnormal medical consumption items provided in this embodiment may screen and review each actual medical consumption item included in the case information based on the reference medical consumption item corresponding to the actual diagnosis item included in the case information, so as to determine the abnormal medical consumption item included in the case information, which has a low association degree with the actual diagnosis item and a weak reference value, so as to avoid interference of the abnormal medical consumption item on the decision and review related to the actual diagnosis item, and assist in making medical decisions, and improve the use quality of the case information.

According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

Fig. 7 illustrates a schematic block diagram of an example electronic device 700 that may be used to implement embodiments of the present 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. 7, the apparatus 700 includes a computing unit 701 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 702 or a computer program loaded from a storage unit 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 may also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

Various components in device 700 are connected to I/O interface 705, including: an input unit 706 such as a keyboard, a mouse, etc.; an output unit 707 such as various types of displays, speakers, and the like; a storage unit 708 such as a magnetic disk, an optical disk, or the like; and a communication unit 709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The computing unit 701 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 701 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 calculation unit 701 performs the respective methods and processes described above, for example, a method of determining an abnormal medical consumption item. For example, in some embodiments, the method of determining abnormal medical consumption items may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 700 via ROM 702 and/or communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the above-described method of determining abnormal medical consumption items may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the method of determining abnormal medical consumption items 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. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of large management difficulty and weak service expansibility in the traditional physical host and virtual private server (VPS, virtual Private Server) service. Servers may also be divided into servers of a distributed system or servers that incorporate blockchains.

According to the technical scheme of the embodiment of the disclosure, each actual medical consumption item included in the case information can be screened and rechecked based on the reference medical consumption item corresponding to the actual diagnosis item included in the case information, so that the abnormal medical consumption item which is included in the case information and has lower association degree with the actual diagnosis item and weaker reference value can be determined, interference of the abnormal medical consumption item on related decision and rechecking of the actual diagnosis item can be avoided, medical decision can be assisted, and the use quality of the case information can be improved.

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 technical solutions provided by the present disclosure 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 (17)

1. A method of determining an abnormal medical consumption item, comprising:

extracting an actual diagnosis item and an actual medical consumption item from the case information;

determining a reference medical consumption item corresponding to the actual diagnostic item;

and determining an actual medical consumption item different from the reference medical consumption item as an abnormal medical consumption item.

2. The method of claim 1, wherein the extracting the actual diagnostic item and the actual medical consumption item from the case information comprises:

extracting a diagnosis item entity and a medical consumption item entity recorded in the case information;

respectively processing the diagnosis item entity and the medical consumption item entity by utilizing a pre-configured entity core word recognition model to obtain diagnosis item core words and medical consumption item core words;

determining the diagnosis item core word as the actual diagnosis item, and determining the medical consumption item core word as the actual medical consumption item.

3. The method of claim 2, wherein the processing the diagnostic item entity and the medical consumable item entity, respectively, using a pre-configured entity core word recognition model, to obtain a diagnostic item core word and a medical consumable item core word, comprises:

The diagnosis item entity and the medical consumption item entity are subjected to word division processing respectively by utilizing a word division layer of the entity core word recognition model, so that a word division processing result is obtained;

carrying out semantic analysis on the word segmentation processing result by utilizing a semantic understanding layer of the entity core word recognition model to obtain a semantic analysis result;

and processing the semantic analysis result by utilizing a conditional random field layer of the entity core word recognition model to obtain the diagnosis item core word and the medical consumption item core word.

4. A method according to any one of claims 1-3, wherein said determining a reference medical consumption item corresponding to said actual diagnostic item comprises:

determining a reference medical consumption item corresponding to the actual diagnosis item based on a pre-configured correspondence table; wherein, the correspondence table records the correspondence between different diagnosis items and different reference medical consumption items.

5. The method of claim 4, further comprising:

extracting a diagnosis item entity and a corresponding reference medical consumption item entity from the medical knowledge graph;

respectively processing the diagnosis item entity and the reference medical consumption item entity by using a preset entity core word recognition model to obtain diagnosis item core words and reference medical consumption item core words;

Determining the diagnostic item core word as the diagnostic item, and determining the reference medical consumption item core word as the reference medical consumption item;

and constructing the corresponding table based on the corresponding relation between the diagnosis item and the reference medical consumption item.

6. The method of claim 1, further comprising:

in response to the presence of a plurality of the actual diagnostic items, a priority sequence of each of the actual diagnostic items is determined based on the number of actual medical consumption items included in the reference medical consumption item to which each of the actual diagnostic items corresponds.

7. The method of claim 1, further comprising:

calculating the ratio of the number of the abnormal medical consumption items to the total number of the actual medical consumption items;

generating evaluation information according to the ratio; wherein the evaluation information is used to indicate a degree of association between the actual diagnostic item and the actual medical consumption item.

8. An apparatus for determining an abnormal medical consumption item, comprising:

a case information extraction unit configured to extract an actual diagnosis item and an actual medical consumption item from the case information;

a reference medical consumption item recall unit configured to determine a reference medical consumption item corresponding to the actual diagnostic item;

An abnormal medical consumption item determination unit configured to determine an actual medical consumption item different from the reference medical consumption item as an abnormal medical consumption item.

9. The apparatus of claim 8, wherein the case information extraction unit comprises:

an entity information extraction subunit configured to extract a diagnosis item entity and a medical consumption item entity described in the case information;

the core word extraction subunit is configured to respectively process the diagnosis item entity and the medical consumption item entity by utilizing a pre-configured entity core word recognition model to obtain a diagnosis item core word and a medical consumption item core word;

an actual information determination subunit configured to determine the diagnosis item core word as the actual diagnosis item, and the medical consumption item core word as the actual medical consumption item.

10. The apparatus of claim 9, wherein the core word extraction subunit comprises:

the first model calling module is configured to utilize the word segmentation layer of the entity core word recognition model to respectively perform word segmentation processing on the diagnosis item entity and the medical consumption item entity to obtain word segmentation processing results;

The second model calling module is configured to perform semantic analysis on the word segmentation processing result by utilizing a semantic understanding layer of the entity core word recognition model to obtain a semantic analysis result;

and the third model calling module is configured to process the semantic analysis result by utilizing a conditional random field layer of the entity core word recognition model to obtain the diagnosis term core word and the medical consumption term core word.

11. The apparatus of any of claims 8-10, wherein the reference medical consumption item recall unit is further configured to determine a reference medical consumption item corresponding to the actual diagnostic item based on a pre-configured correspondence table; wherein, the correspondence table records the correspondence between different diagnosis items and different reference medical consumption items.

12. The apparatus of claim 11, further comprising:

a knowledge graph invoking unit configured to extract a diagnosis item entity and a corresponding reference medical consumption item entity from the medical knowledge graph;

the reference core word extraction unit is configured to respectively process the diagnosis item entity and the reference medical consumption item entity by utilizing a preset entity core word recognition model to obtain a diagnosis item core word and a reference medical consumption item core word;

A reference information determination unit configured to determine the diagnosis item core word as the diagnosis item, and the reference medical consumption item core word as the reference medical consumption item;

a correspondence table construction unit configured to construct the correspondence table based on a correspondence relationship between the diagnostic item and the reference medical consumption item.

13. The apparatus of claim 8, further comprising:

a diagnosis item priority determining unit configured to determine, in response to the presence of a plurality of the actual diagnosis items, a priority sequence of each of the actual diagnosis items based on the number of actual medical consumption items included in a reference medical consumption item to which each of the actual diagnosis items corresponds.

14. The apparatus of claim 8, further comprising:

an abnormal duty ratio calculation unit configured to calculate a ratio of the number of abnormal medical consumption items to the total number of actual medical consumption items;

an evaluation information generation unit configured to generate evaluation information according to the ratio; wherein the evaluation information is used to indicate a degree of association between the actual diagnostic item and the actual medical consumption item.

15. An electronic device, comprising:

At least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of determining abnormal medical consumption items of any one of claims 1-7.

16. A non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method of determining an abnormal medical consumption item of any one of claims 1-7.

17. A computer program product comprising a computer program which, when executed by a processor, implements the method of determining abnormal medical consumption items according to any one of claims 1-7.