Information query method applied to venous catheter information system and computer equipment

Abstract

The embodiment of the invention relates to the field of disease identification, in particular to an information query method applied to a venous catheter information system and computer equipment. A specific embodiment of the method comprises the following steps: a knowledge graph construction subsystem constructs a complication knowledge graph; the disease recognition subsystem trains a disease recognition model; the information transmission subsystem sends venous catheter information of a patient to the disease identification subsystem and the disease retrieval subsystem; the disease identification subsystem inputs the venous catheter information of the patient into a disease identification model to obtain disease type information of the patient; the disease retrieval subsystem performs retrieval processing on a complication type database in response to the received venous catheter information of the patient so as to generate complication retrieval information; and the information transmission subsystem responds to the received disease type information and complication retrieval information of the patient and sends the disease type information and complication retrieval information of the patient to the management terminal. According to the embodiment, waste of medical resources can be reduced.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of disease identification, and more particularly to an information query method and computer equipment applied to an intravenous catheter information system. Background Art

[0002] Intravenous therapy is one of the most commonly used and most direct treatment methods in clinical practice. It plays an irreplaceable role in disease prevention, treatment and saving patients' lives. However, intravenous therapy often causes a series of complications such as phlebitis, exudation / extravasation, catheter blockage, and catheter-related bloodstream infection, which not only affect the function and treatment effect of the intravenous catheter, but also endanger the patient's life safety. At present, in clinical care, medical staff often need to make judgments based on experience to decide whether to continue to indwell or remove the catheter after adjustment. Common treatment methods in intravenous therapy include peripheral intravenous catheters and central venous catheters. Among them, most complications of peripheral intravenous catheters (such as indwelling needles) can be promptly handled through visual observation, but central venous catheters are more commonly used in clinical practice due to their applicability (can be used for all types of infusion therapy and drug administration). At the same time, due to its special location (the catheter tip is located in the lower 1 / 3 of the superior vena cava, at the entrance of the heart), high treatment cost, wide application range for different patients' treatment needs, and difficult judgment and treatment of complications, it will bring greater difficulties to medical care in clinical treatment. If the judgment is wrong, it will cause a waste of medical resources and affect the patient's treatment process. Summary of the invention

[0003] The content of this application is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this application is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0004] Some embodiments of the present application propose an information query method, a computer device, and a computer-readable storage medium applied to an intravenous catheter information system to solve one or more of the technical problems mentioned in the above background technology section.

[0005] In a first aspect, some embodiments of the present application provide an information query method applied to an intravenous catheter information system, the intravenous catheter information system comprising: a data acquisition subsystem, a knowledge graph construction subsystem, a symptom identification subsystem, a symptom retrieval subsystem, and an information transmission subsystem, the method comprising: the knowledge graph construction subsystem constructing a complication knowledge graph based on an acquired complication type information set; the symptom identification subsystem training a symptom identification model based on the acquired complication type information set and the acquired complication knowledge graph; the information transmission subsystem sending the patient's intravenous catheter information to the symptom identification subsystem and the symptom detection subsystem in response to receiving the patient's intravenous catheter information sent by the management terminal; the above-mentioned disease recognition subsystem, in response to receiving the patient's venous catheter information, inputs the above-mentioned patient's venous catheter information into the above-mentioned disease recognition model to obtain the patient's disease type information, and sends the above-mentioned patient's disease type information to the above-mentioned information transmission subsystem; the above-mentioned disease retrieval subsystem, in response to receiving the patient's venous catheter information, searches the complication type database based on the above-mentioned patient's venous catheter information to generate complication retrieval information, and sends the above-mentioned complication retrieval information to the above-mentioned information transmission subsystem; the above-mentioned information transmission subsystem, in response to receiving the patient's disease type information and the complication retrieval information, sends the above-mentioned patient's disease type information and the above-mentioned complication retrieval information to the management terminal.

[0006] In a second aspect, the present application also provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, it implements the method described in any implementation of the first aspect.

[0007] In a third aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, wherein when the computer program is executed by a processor, the method described in any implementation of the first aspect is implemented.

[0008] The above-mentioned embodiments of the present application have the following beneficial effects: through the information query method applied to the intravenous catheter information system of some embodiments of the present application, the waste of medical resources can be reduced. Specifically, the reason for the waste of medical resources is that the central venous catheter has the characteristics of a special location (the catheter tip is located in the lower 1 / 3 of the superior vena cava, at the entrance of the heart), high treatment cost, wide application range for different patient treatment needs, and difficult complication judgment and treatment. The difficulty brought to medical care in clinical treatment will be greater. For example, misjudgment will cause waste of medical resources and affect the patient's treatment process. Based on this, the information query method applied to the intravenous catheter information system of some embodiments of the present application, first, the above-mentioned knowledge graph construction subsystem constructs a complication knowledge graph based on the acquired complication type information set. Thus, the knowledge graph construction subsystem can construct a complication knowledge graph so that the symptom recognition model can be trained through the complication knowledge graph in the future. Secondly, the above-mentioned symptom recognition subsystem trains the symptom recognition model based on the acquired complication type information set and the acquired complication knowledge graph. Thus, the symptom recognition subsystem can train the symptom recognition model so that the patient's symptom type information can be identified through the symptom recognition model in the future. Next, in response to receiving the patient's venous catheter information sent by the management terminal, the information transmission subsystem sends the patient's venous catheter information to the disease identification subsystem and the disease retrieval subsystem. Then, in response to receiving the patient's venous catheter information, the disease identification subsystem inputs the patient's venous catheter information into the disease identification model to obtain the patient's disease type information, and sends the patient's disease type information to the information transmission subsystem. Thus, the disease identification subsystem can identify more accurate patient disease type information according to the disease identification model. Afterwards, in response to receiving the patient's venous catheter information, the disease retrieval subsystem performs a search process on the complication type database based on the patient's venous catheter information to generate complication retrieval information, and sends the complication retrieval information to the information transmission subsystem. Thus, the disease retrieval subsystem can retrieve the complication retrieval information through the complication type database. Finally, in response to receiving the patient's disease type information and the complication retrieval information, the information transmission subsystem sends the patient's disease type information and the complication retrieval information to the management terminal. Thus, the patient's disease type information identified by the disease identification model and the complication retrieval information retrieved by the complication type database can be sent to the management terminal. Therefore, the management terminal can obtain more comprehensive and accurate complication information, thereby reducing the waste of medical resources and the impact on the patient's treatment process. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0010] Figure 1 is a flow chart of some embodiments of an information query method applied to a venous catheter information system according to the present application; Figure 2 is a schematic diagram of a computer device suitable for implementing some embodiments of the present application; Figure 3 It is a schematic diagram of application scenarios of some embodiments of the information query method applied to the intravenous catheter information system according to the present application. DETAILED DESCRIPTION

[0011] Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0012] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0013] It should be noted that the concepts such as "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0014] It should be noted that the modifications of "one" and "plurality" mentioned in the present application are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0015] The names of the messages or information exchanged between multiple devices in the embodiments of the present application are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0016] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments.

[0017] Figure 1The process 100 of some embodiments of the information query method applied to the intravenous catheter information system according to the present application is shown. The information query method applied to the intravenous catheter information system, the intravenous catheter information system includes: a data acquisition subsystem, a knowledge graph construction subsystem, a symptom identification subsystem, a symptom retrieval subsystem, and an information transmission subsystem, including the following steps: Step 101: The knowledge graph construction subsystem constructs a complication knowledge graph based on the acquired complication type information set.

[0018] In some embodiments, the knowledge graph construction subsystem constructs a complication knowledge graph based on the acquired complication type information set. The complication type information in the complication type information set may be information characterizing the complication type stored in a complication type database. The complication type database may be a database storing complication type information. The complication type information in the complication type information set may include, but is not limited to, patient basic information and complication type. For example, the patient basic information may include, but is not limited to, at least one of the following: whether the drug is exuding, whether the patient is in pain, the location of the patient's pain, thermal imaging, and B-ultrasound. The complication type may be, but is not limited to, phlebitis, drug exudation / extravasation, catheter blockage, catheter-related venous thrombosis, catheter-related bloodstream infection (CRBSI), central venous catheter malposition / displacement, medical adhesive-related skin injury, and catheter clamp syndrome. Here, the data acquisition subsystem may be a system for collecting the initial complication type information set. The knowledge graph construction subsystem may be a system for constructing a complication knowledge graph. The symptom identification subsystem may be a system for identifying the symptom type information of the patient. The symptom retrieval subsystem may be a system for retrieving the complication type database. The information transmission subsystem may be a system for transmitting information.

[0019] In practice, the above-mentioned knowledge graph construction subsystem can construct a complication knowledge graph based on the acquired complication type information set through the following steps: The first step is to perform data preprocessing on each complication type information in the above complication type information set to generate complication preprocessing information and obtain a complication preprocessing information set. In practice, first, data preprocessing is performed on each complication type information in the above complication type information set, and the above knowledge graph construction subsystem can remove the information representing the empty space in the complication type information to obtain the complication preprocessing information. Then, the above knowledge graph construction subsystem can determine the obtained individual complication preprocessing information as complication preprocessing information.

[0020] The second step is to perform entity recognition processing on the above complication pretreatment information set to generate a complication entity information set. In practice, the above knowledge graph construction subsystem can perform entity recognition processing on the above complication pretreatment information set through a preset entity recognition algorithm to generate a complication entity information set. Among them, the complication pretreatment information in the complication pretreatment information set corresponds one-to-one to the complication entity information in the complication entity information set. For example, the preset entity recognition algorithm can be: NER (Named Entity Recognition) algorithm (for example, hidden Markov model, conditional random field, bidirectional long short-term memory network).

[0021] The third step is to perform relation extraction on the complication preprocessing information set based on the complication entity information set to generate a complication entity relation information set. In practice, the knowledge graph construction subsystem can perform relation extraction on the complication preprocessing information set based on the complication entity information set through a preset relation extraction algorithm to generate a complication entity relation information set. For example, the preset relation extraction algorithm can be, but is not limited to: a convolutional neural network, a long short-term memory network, and an attention model.

[0022] The fourth step is to perform attribute extraction processing on the above complication preprocessing information set based on the above complication entity information set to generate a complication attribute information set.

[0023] In practice, the above-mentioned knowledge graph construction subsystem is based on the above-mentioned complication entity information set, and can extract attributes from the above-mentioned complication preprocessing information set through the following sub-steps to generate a complication attribute information set: In the first sub-step, for each complication pre-processing information in the complication pre-processing information set, the following attribute extraction steps are performed: In the first attribute extraction step, the complication preprocessing information is subjected to text recognition processing to generate complication text information. In practice, the knowledge graph construction subsystem can perform text recognition processing on the complication preprocessing information through a preset text recognition algorithm to generate complication text information. For example, the preset text recognition algorithm can be: OCR (Optical Character Recognition) algorithm.

[0024] In the second attribute extraction step, based on the complication entity information corresponding to the complication preprocessing information, the complication text information is removed to generate complication text removal information. In practice, the knowledge graph construction subsystem can remove the information in the complication text information that is identical to the corresponding complication entity information to obtain the complication text removal information.

[0025] In the third attribute extraction step, the information in the complication text removal information that satisfies the first preset attribute condition is determined as the first complication attribute information. The first preset attribute condition may be: information characterizing the type of clinical manifestation. Here, the type of clinical manifestation may be, but is not limited to: drug exudation, drug extravasation, catheter blockage, catheter malposition, catheter displacement, etc.

[0026] In the fourth attribute extraction step, the information satisfying the second preset attribute condition in the above complication text removal information is determined as the second complication attribute information. The second preset attribute condition may be: information characterizing the patient's feeling type. Here, the patient's feeling type may be, but is not limited to: patient pain, patient chest pain, patient stomach pain, patient acute chest pain, patient shortness of breath, etc.

[0027] In the fifth attribute extraction step, the information in the complication text removal information that satisfies the third preset attribute condition is determined as the third complication attribute information. The third preset attribute condition may be: information characterizing the tool evaluation type. Here, the tool evaluation type may be, but is not limited to: thermal imaging, B-ultrasound, etc.

[0028] In the sixth attribute extraction step, the first complication attribute information, the second complication attribute information and the third complication attribute information are determined as complication attribute information.

[0029] The second sub-step is to determine the obtained attribute information of each complication as a complication attribute information set.

[0030] Therefore, firstly, the text information representing the complication pretreatment information can be identified through text recognition technology. Then, three types of attribute information can be identified to identify the attribute information corresponding to the complication from three different perspectives: clinical manifestation, patient perception, and tool evaluation. Therefore, it is convenient to consider the three different types of attribute information according to the patient's intravenous catheter information in the future, so as to identify a more accurate type of complication.

[0031] The fifth step is to perform knowledge fusion processing on the above-mentioned complication entity information set, the above-mentioned complication entity relationship information set and the above-mentioned complication attribute information set to generate a complication knowledge fusion information set. In practice, first, for each complication entity information in the complication entity information set, the above-mentioned knowledge graph construction subsystem can fuse the various complication entity relationship information corresponding to the above-mentioned complication entity information in the above-mentioned complication entity relationship information set, the complication attribute information corresponding to the above-mentioned complication entity information in the above-mentioned complication attribute information set, and the above-mentioned limb information to generate complication knowledge fusion information. Then, the above-mentioned knowledge graph construction subsystem can determine the various complication knowledge fusion information generated as the complication knowledge fusion information set.

[0032] The sixth step is to model the above complication knowledge fusion information set to generate a complication knowledge graph. In practice, the above knowledge graph construction subsystem can model the above complication knowledge fusion information set through a preset modeling algorithm to generate a complication knowledge graph. For example, the preset modeling algorithm can be: using the various complication entity information included in the complication knowledge fusion information set as nodes, and using the various complication entity relationship information included in the complication knowledge fusion information set as the relationship between nodes to construct a complication knowledge graph. Here, the complication attribute information included in the complication knowledge fusion information set can serve as a supplementary description of the nodes in the complication knowledge graph.

[0033] Before step 101, the method further includes: In the first step, the data acquisition subsystem obtains an initial complication type information set, and stores the initial complication type information set in a complication type database.

[0034] In some embodiments, the data acquisition subsystem may obtain an initial complication type information set from a terminal device via a wired connection or a wireless connection, and store the initial complication type information set in a complication type database. The initial complication type information in the initial complication type information set may be information characterizing the complication type obtained from the terminal device. The initial complication type information in the initial complication type information set may include, but is not limited to: basic patient information and complication type.

[0035] It should be noted that the above wireless connection methods may include but are not limited to 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or to be developed in the future.

[0036] In the second step, the above-mentioned knowledge graph construction subsystem obtains the complication type information set from the above-mentioned complication type database.

[0037] In some embodiments, the knowledge graph construction subsystem obtains a complication type information set from the complication type database. In practice, the knowledge graph construction subsystem may obtain all complication type information included in the complication type database from the complication type database to obtain a complication type information set.

[0038] Step 102: the symptom identification subsystem trains a symptom identification model based on the acquired complication type information set and the acquired complication knowledge graph.

[0039] In some embodiments, the above-mentioned symptom identification subsystem trains a symptom identification model based on the acquired complication type information set and the acquired complication knowledge graph, wherein the complication type information set is acquired from a complication type database, and the complication knowledge graph is acquired from the above-mentioned knowledge graph construction subsystem.

[0040] In practice, the above-mentioned symptom identification subsystem can train the symptom identification model through the following steps based on the acquired complication type information set and the acquired complication knowledge graph: The first step is to obtain the sample venous catheter information corresponding to each complication type information in the complication type information set to obtain the sample venous catheter information set. In practice, first, for each complication type information in the complication type information set, the symptom identification subsystem can determine the patient basic information included in the complication type information as the sample venous catheter information. Then, the symptom identification subsystem can determine the determined individual sample venous catheter information as the sample venous catheter information set.

[0041] The second step is to determine the initial disease recognition model, which includes: initial feature extraction model, initial vectorization model, initial graph embedding model, initial aggregation model, and initial matching model.

[0042] Here, the initial feature extraction model may be a neural network model that takes the target sample venous catheter information as input and takes the initial venous catheter feature information as output. For example, the initial feature extraction model may be a SIFT (Scale-Invariant Feature Transform) algorithm.

[0043] The initial vectorization model may be a model that takes the initial venous catheter feature information as input and takes the initial venous catheter vector information as output. For example, the initial vectorization model may be an IF-IDF (Term Frequency-Inverse Document Frequency) model.

[0044] The initial graph embedding model may be a model that takes the complication knowledge graph as input and takes the initial knowledge graph embedding information set as output. For example, the initial graph embedding model may be: TransE (Translation Distance Model for Knowledge Graph Embedding) model.

[0045] The initial aggregation model may be a neural network model that takes the initial venous catheter vector information and the initial knowledge graph embedding information set as input and takes the initial aggregation information as output. For example, the initial aggregation model may be a graph convolutional neural network model.

[0046] The initial matching model may be a model that takes the initial venous catheter vector information and the initial aggregation information as input and takes the initial disease type information as output. For example, the initial matching model may be, but is not limited to, a cosine similarity algorithm or a Euclidean distance algorithm.

[0047] The third step is to select target sample venous catheter information from the above sample venous catheter information set.

[0048] The fourth step is to input the selected target sample venous catheter information into the initial feature extraction model to obtain the initial venous catheter feature information.

[0049] The fifth step is to input the initial venous catheter feature information into the initial vectorization model to obtain the initial venous catheter vector information.

[0050] In the sixth step, the complication knowledge graph is input into the initial graph embedding model to obtain the initial knowledge graph embedding information set.

[0051] In the seventh step, the initial venous catheter vector information and the initial knowledge graph embedding information set are input into the initial aggregation model to obtain the initial aggregation information.

[0052] In the eighth step, the initial venous catheter vector information and the initial aggregation information are input into the initial matching model to obtain the initial disease type information.

[0053] In the ninth step, based on a preset loss function, the difference value between the initial disease type information and the complication type information corresponding to the target sample intravenous catheter information is determined. For example, the preset loss function may be, but is not limited to: mean square error loss function (MSE), hinge loss function, cross entropy loss function (CrossEntropy), 0-1 loss function, absolute value loss function, log loss function, square loss function, exponential loss function, etc.

[0054] In step 10, in response to determining that the difference value is greater than or equal to the preset difference value, the network parameters of the initial disease recognition model are adjusted. For example, the difference value can be calculated for the preset difference value. On this basis, the parameters of the initial disease recognition model are adjusted using methods such as back propagation and gradient descent. For example, the preset difference value can be 0.1.

[0055] Optionally, in response to determining that the difference value is less than a preset difference value, the above-mentioned disease recognition subsystem may also determine the initial disease recognition model as the disease recognition model.

[0056] Thus, firstly, the feature information in the target sample venous catheter information can be extracted by the initial feature extraction model to reduce the influence of redundant information. Secondly, the initial venous catheter feature information can be vectorized by the initial vectorization model so as to be subsequently identified with the knowledge graph. Then, the knowledge graph can be embedded by the initial graph embedding model so as to be aggregated with the initial venous catheter feature information. Then, the features of entities adjacent to the initial venous catheter feature information can be aggregated from the knowledge graph by the initial aggregation model. After that, relatively accurate initial disease type information can be matched from the knowledge graph by the initial matching model. Then, a relatively accurate disease recognition model can be trained by training the initial disease recognition model including the initial feature extraction model, the initial vectorization model, the initial graph embedding model, the initial aggregation model, and the initial matching model, so that relatively accurate patient disease type information can be identified by the relatively accurate disease recognition model. Thus, the waste of medical resources can be reduced, and the impact on the patient's treatment process can be reduced.

[0057] Step 103 , in response to receiving the patient's venous catheter information sent by the management terminal, the information transmission subsystem sends the patient's venous catheter information to the symptom identification subsystem and the symptom retrieval subsystem.

[0058] In some embodiments, the information transmission subsystem sends the patient's venous catheter information to the symptom identification subsystem and the symptom retrieval subsystem in response to receiving the patient's venous catheter information sent by the management terminal. The management terminal may be a terminal that sends the patient's venous catheter information to the information transmission subsystem.

[0059] In practice, in response to receiving the patient's venous catheter information sent by the management terminal, the information transmission subsystem may send the patient's venous catheter information to the symptom identification subsystem and the symptom retrieval subsystem through the following steps: The first step is to store the patient's intravenous catheter information in an asynchronous query queue, wherein the asynchronous query queue may be a queue for performing asynchronous query.

[0060] In the second step, in response to determining that the asynchronous query queue and the patient's venous catheter information meet a preset queue condition, the patient's venous catheter information is sent to the symptom identification subsystem and the symptom retrieval subsystem. The preset queue condition may be that the patient's venous catheter information is located at the first of the asynchronous query queue.

[0061] Therefore, asynchronous communication can be achieved through the asynchronous query queue, which can optimize the utilization of computing resources, improve system performance, and reduce the waiting time of the management terminal due to a long wait for query results.

[0062] Step 104 , in response to receiving the patient's venous catheter information, the disease recognition subsystem inputs the patient's venous catheter information into the disease recognition model to obtain the patient's disease type information, and sends the patient's disease type information to the information transmission subsystem.

[0063] In some embodiments, in response to receiving the patient's venous catheter information, the disease identification subsystem inputs the patient's venous catheter information into the disease identification model to obtain the patient's disease type information, and sends the patient's disease type information to the information transmission subsystem.

[0064] Step 105, in response to receiving the patient's intravenous catheter information, the symptom retrieval subsystem searches the complication type database based on the patient's intravenous catheter information to generate complication retrieval information, and sends the complication retrieval information to the information transmission subsystem.

[0065] In some embodiments, the above-mentioned disease retrieval subsystem, in response to receiving the patient's venous catheter information, searches the complication type database based on the above-mentioned patient's venous catheter information to generate complication retrieval information, and sends the above-mentioned complication retrieval information to the above-mentioned information transmission subsystem.

[0066] In practice, in response to receiving the patient's intravenous catheter information, the above-mentioned disease retrieval subsystem can perform a search process on the complication type database based on the above-mentioned patient's intravenous catheter information through the following steps to generate complication retrieval information: In the first step, the patient's intravenous catheter information is cleaned to generate patient cleaned information. In practice, the disease retrieval subsystem can use a preset data cleaning algorithm to clean the patient's intravenous catheter information to generate patient cleaned information. For example, the preset data cleaning algorithm can be: delete missing values.

[0067] The second step is to perform feature extraction processing on the patient cleansing information to generate a patient feature information set. In practice, the disease retrieval subsystem can perform feature extraction processing on the patient cleansing information through a preset feature extraction algorithm to generate a patient feature information set. For example, the preset feature extraction algorithm can be, but is not limited to: a histogram feature extraction algorithm, a wavelet transform algorithm, and a SIFT (Scale-Invariant Feature Transform) algorithm.

[0068] Step 3: For each patient characteristic information in the above patient characteristic information set, perform the following retrieval sub-steps: The first sub-step is to determine the complication similarity between the patient characteristic information and each complication type information included in the complication type database, and obtain a complication similarity set. In practice, the symptom retrieval subsystem can determine the complication similarity between the patient characteristic information and each complication type information included in the complication type database by using a preset similarity algorithm, and obtain a complication similarity set. For example, the preset similarity algorithm can be, but is not limited to: Euclidean distance algorithm, cosine similarity algorithm, Pearson correlation coefficient algorithm.

[0069] In the second sub-step, the complication type information corresponding to each complication similarity that satisfies a preset similarity condition in the above complication similarity set is determined as the target complication type information. The preset similarity condition may be: the complication similarity is greater than a similarity threshold. For example, the similarity threshold may be 0.8.

[0070] The fourth step is to determine the obtained target complication type information as a target complication type information set.

[0071] The fifth step is to classify the target complication type information set to generate a complication classification information set. In practice, the above-mentioned disease retrieval subsystem can classify the target complication type information set by a preset classification algorithm to generate a complication classification information set. For example, the preset classification algorithm can be: each target complication type information representing the same complication type is divided into a group.

[0072] In the sixth step, for each complication classification information group in the complication classification information group set, the sum of the similarities of each complication corresponding to the complication classification information group is determined as the target complication similarity.

[0073] In the seventh step, the largest target complication similarity among the obtained target complication similarities and the complication classification information in the corresponding complication classification information group are determined as complication retrieval information. In practice, the above-mentioned disease retrieval subsystem can randomly select a complication classification information from the largest target complication similarity among the obtained target complication similarities and the corresponding complication classification information group as complication retrieval information.

[0074] Therefore, first, the patient's intravenous catheter information can be cleaned to remove missing values. Secondly, the patient's cleaned information can be subjected to feature extraction, and the feature information of the patient's cleaned information can be extracted so as to subsequently identify more accurate complication retrieval information. Then, the similarity between each piece of information in the complication type database and the patient's feature information can be calculated, and each piece of information with a similarity higher than a similarity threshold can be selected. After that, the obtained information can be classified so that each target complication type information of the same category can be divided into a group. Then, the similarities corresponding to each target complication type information of each group can be summed up. Finally, the complication classification information corresponding to the obtained maximum target complication similarity can be determined as the complication retrieval information. Thus, the complication retrieval information with the highest similarity to the patient's intravenous catheter information can be obtained.

[0075] Step 106: In response to receiving the patient's symptom type information and the complication retrieval information, the information transmission subsystem sends the patient's symptom type information and the complication retrieval information to the management terminal.

[0076] In some embodiments, the information transmission subsystem sends the patient's symptom type information and the complication retrieval information to the management terminal in response to receiving the patient's symptom type information and the complication retrieval information.

[0077] In practice, the above information transmission subsystem may perform the following sending steps in response to receiving a query request from the management terminal: The first sending step is to send the received patient symptom type information and complication search information to the management terminal in response to receiving the patient symptom type information and complication search information. The query request may represent a request from the management terminal to receive the patient symptom type information and complication search information.

[0078] The second sending step is to send the preset search information to the management terminal in response to not receiving the patient's disease type information or complication search information. The preset search information can be information indicating "the search has not been completed yet, please wait".

[0079] Therefore, since the asynchronous query will not directly send the query result to the management terminal after the query is completed, the management terminal needs to send a query request to receive the query result. When the information transmission subsystem receives the query request from the management terminal, it will send the query result to the management terminal. When the information transmission subsystem receives the query request from the management terminal but has not yet received the patient's disease type information or complication retrieval information, it indicates that the current query operation is not completed and the management terminal needs to wait. In this way, the asynchronous query operation is completed.

[0080] The above-mentioned embodiments of the present application have the following beneficial effects: through the information query method applied to the intravenous catheter information system of some embodiments of the present application, the waste of medical resources can be reduced. Specifically, the reason for the waste of medical resources is that the central venous catheter has the characteristics of a special location (the catheter tip is located in the lower 1 / 3 of the superior vena cava, at the entrance of the heart), high treatment cost, wide application range for different patient treatment needs, and difficult complication judgment and treatment. The difficulty brought to medical care in clinical treatment will be greater. For example, misjudgment will cause waste of medical resources and affect the patient's treatment process. Based on this, the information query method applied to the intravenous catheter information system of some embodiments of the present application, first, the above-mentioned knowledge graph construction subsystem constructs a complication knowledge graph based on the acquired complication type information set. Thus, the knowledge graph construction subsystem can construct a complication knowledge graph so that the symptom recognition model can be trained through the complication knowledge graph in the future. Secondly, the above-mentioned symptom recognition subsystem trains the symptom recognition model based on the acquired complication type information set and the acquired complication knowledge graph. Thus, the symptom recognition subsystem can train the symptom recognition model so that the patient's symptom type information can be identified through the symptom recognition model in the future. Next, in response to receiving the patient's venous catheter information sent by the management terminal, the information transmission subsystem sends the patient's venous catheter information to the disease identification subsystem and the disease retrieval subsystem. Then, in response to receiving the patient's venous catheter information, the disease identification subsystem inputs the patient's venous catheter information into the disease identification model to obtain the patient's disease type information, and sends the patient's disease type information to the information transmission subsystem. Thus, the disease identification subsystem can identify more accurate patient disease type information according to the disease identification model. Afterwards, in response to receiving the patient's venous catheter information, the disease retrieval subsystem performs a search process on the complication type database based on the patient's venous catheter information to generate complication retrieval information, and sends the complication retrieval information to the information transmission subsystem. Thus, the disease retrieval subsystem can retrieve the complication retrieval information through the complication type database. Finally, in response to receiving the patient's disease type information and the complication retrieval information, the information transmission subsystem sends the patient's disease type information and the complication retrieval information to the management terminal. Thus, the patient's disease type information identified by the disease identification model and the complication retrieval information retrieved by the complication type database can be sent to the management terminal. Therefore, the management terminal can obtain more comprehensive and accurate complication information, thereby reducing the waste of medical resources and the impact on the patient's treatment process. The present application also provides a computer device 200. Figure 2As shown, the computer device 200 includes: a bus 201, a processor 202, a memory 203 and a communication interface 204. The processor 202, the memory 203 and the communication interface 204 communicate with each other through the bus 201. The computer device 200 can be a server or a terminal device. It should be understood that the present application does not limit the number of processors and memories in the computer device 200.

[0081] The bus 201 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 The bus 201 is represented by only one line, but it does not mean that there is only one bus or one type of bus. The bus 201 may include a path for transmitting information between various components of the computer device 200 (for example, the memory 203, the processor 202, and the communication interface 204).

[0082] The processor 202 may include any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0083] The memory 203 may include a volatile memory, such as a random access memory (RAM). The memory 203 may also include a non-volatile memory, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid state drive (SSD).

[0084] The memory 203 stores executable program codes, and the processor 202 executes the executable program codes to respectively implement the functions of the acquisition module, the sampling module, the determination module, and the mixing module, thereby implementing the above-mentioned information query method applied to the intravenous catheter information system. That is, the memory 203 stores instructions for executing the above-mentioned information query method applied to the intravenous catheter information system.

[0085] The communication interface 204 uses a transceiver module such as, but not limited to, a network interface card or a transceiver to implement communication between the computer device 200 and other devices or a communication network.

[0086] An embodiment of the present application further provides a chip, which includes a processor and a data interface. The processor reads instructions stored in a memory through the data interface to execute the above-mentioned information query method applied to the intravenous catheter information system.

[0087] The embodiment of the present application also provides a computer-readable storage medium. The computer-readable storage medium may be any available medium that can be stored by a computing device or a data storage device such as a data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk). The computer-readable storage medium includes instructions that instruct the computing device to execute the information query method applied to the intravenous catheter information system. Figure 3 It is a schematic diagram of application scenarios of some embodiments of the information query method applied to the intravenous catheter information system according to the present application.

[0088] exist Figure 3In the application scenario, first, the knowledge graph construction subsystem constructs the complication knowledge graph 302 based on the acquired complication type information set 301. In this application scenario, the complication type information in the complication type information set 301 can be "drug extravasation, patient no pain, thermal imaging, drug extravasation". The complication knowledge graph 302 can be "complication knowledge graph 1". Secondly, the symptom identification subsystem trains the symptom identification model 303 based on the acquired complication type information set 301 and the acquired complication knowledge graph 302. In this application scenario, the symptom identification model 303 can be "symptom identification model 1". Then, in response to receiving the patient's intravenous catheter information 304 sent by the management terminal, the information transmission subsystem sends the patient's intravenous catheter information 304 to the symptom identification subsystem and the symptom retrieval subsystem. In this application scenario, the patient's intravenous catheter information 304 can be "drug extravasation, patient no pain, thermal imaging 1". Then, in response to receiving the patient's intravenous catheter information 3044, the symptom identification subsystem inputs the patient's intravenous catheter information 304 into the symptom identification model 303 to obtain the patient's symptom type information 305, and sends the patient's symptom type information 305 to the information transmission subsystem. In this application scenario, the patient's symptom type information 305 can be "drug leakage". Afterwards, in response to receiving the patient's intravenous catheter information 304, the symptom retrieval subsystem performs a search process on the complication type database 306 based on the patient's intravenous catheter information 304 to generate complication retrieval information 307, and sends the complication retrieval information 307 to the information transmission subsystem. In this application scenario, the complication type database 306 can be "complication type database 1". The complication retrieval information 307 can be "drug leakage". Finally, in response to receiving the patient's symptom type information 305 and the complication retrieval information 307, the information transmission subsystem sends the patient's symptom type information 305 and the complication retrieval information 307 to the management terminal. The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0089] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the protection scope of the technical solutions of the embodiments of the present application.

Claims

1. An information query method applied to a venous catheter information system, the venous catheter information system comprising: Data collection subsystem, knowledge graph construction subsystem, disease identification subsystem, disease retrieval subsystem, information transmission subsystem, including: The knowledge graph construction subsystem constructs a complication knowledge graph based on the acquired complication type information set; The symptom identification subsystem trains a symptom identification model based on the acquired complication type information set and the acquired complication knowledge graph; The information transmission subsystem, in response to receiving the patient's venous catheter information sent by the management terminal, sends the patient's venous catheter information to the symptom identification subsystem and the symptom retrieval subsystem; In response to receiving the patient's venous catheter information, the disease identification subsystem inputs the patient's venous catheter information into the disease identification model to obtain patient disease type information, and sends the patient disease type information to the information transmission subsystem; In response to receiving the patient's intravenous catheter information, the disease retrieval subsystem performs a retrieval process on a complication type database based on the patient's intravenous catheter information to generate complication retrieval information, and sends the complication retrieval information to the information transmission subsystem; In response to receiving the patient's symptom type information and the complication retrieval information, the information transmission subsystem sends the patient's symptom type information and the complication retrieval information to the management terminal.

2. The information query method applied to the intravenous catheter information system according to claim 1, wherein: Before the knowledge graph construction subsystem constructs the complication knowledge graph based on the acquired complication type information set, the method further includes: The data acquisition subsystem acquires an initial complication type information set, and stores the initial complication type information set in a complication type database; The knowledge graph construction subsystem obtains a complication type information set from the complication type database.

3. The information query method applied to the intravenous catheter information system according to claim 1, wherein: The complication knowledge graph is constructed based on the acquired complication type information set, including: Performing data preprocessing on each complication type information in the complication type information set to generate complication preprocessing information, thereby obtaining a complication preprocessing information set; Performing entity recognition processing on the complication preprocessing information set to generate a complication entity information set, wherein the complication preprocessing information in the complication preprocessing information set corresponds one-to-one to the complication entity information in the complication entity information set; Based on the complication entity information set, performing relationship extraction processing on the complication preprocessing information set to generate a complication entity relationship information set; Based on the complication entity information set, performing attribute extraction processing on the complication preprocessing information set to generate a complication attribute information set; Performing knowledge fusion processing on the complication entity information set, the complication entity relationship information set and the complication attribute information set to generate a complication knowledge fusion information set; The complication knowledge fusion information set is modeled to generate a complication knowledge graph.

4. The information query method applied to the intravenous catheter information system according to claim 3, wherein: The step of performing attribute extraction processing on the complication preprocessing information set based on the complication entity information set to generate a complication attribute information set includes: For each complication preprocessing information in the complication preprocessing information set, the following attribute extraction steps are performed: Performing text recognition processing on the complication preprocessing information to generate complication text information; Based on the complication entity information corresponding to the complication preprocessing information, removing the complication text information to generate complication text removal information; Determining the information in the complication text removal information that satisfies the first preset attribute condition as the first complication attribute information; Determining the information in the complication text removal information that satisfies the second preset attribute condition as the second complication attribute information; Determining the information in the complication text removal information that satisfies the third preset attribute condition as the third complication attribute information; determining the first complication attribute information, the second complication attribute information and the third complication attribute information as complication attribute information; The obtained individual complication attribute information is determined as a complication attribute information set.

5. The information query method applied to the intravenous catheter information system according to claim 1, wherein: In response to receiving the patient's venous catheter information sent by the management terminal, sending the patient's venous catheter information to the symptom identification subsystem and the symptom retrieval subsystem includes: Storing the patient's intravenous catheter information in an asynchronous query queue; In response to determining that the asynchronous query queue and the patient's venous catheter information meet a preset queue condition, the patient's venous catheter information is sent to the symptom identification subsystem and the symptom retrieval subsystem.

6. The information query method applied to the intravenous catheter information system according to claim 1, wherein: In response to receiving the patient's symptom type information and the complication search information, sending the patient's symptom type information and the complication search information to the management terminal includes: In response to receiving the query request from the management terminal, the following sending steps are performed: In response to receiving the patient's symptom type information and complication search information, sending the received patient's symptom type information and complication search information to the management terminal; In response to not receiving the patient's symptom type information or complication search information, the preset search information is sent to the management terminal.

7. A computer device, wherein: The computer device comprises a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium, wherein: The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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