CN115274102A - Artificial intelligent prevention and treatment system, method, device and medium for venous thromboembolism - Google Patents
Artificial intelligent prevention and treatment system, method, device and medium for venous thromboembolism Download PDFInfo
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Abstract
The embodiment of the specification provides a system, a method, a device and a medium for artificial intelligent prevention and treatment of venous thromboembolism. The system comprises an acquisition module, a risk level determination module and a prevention and control measure determination module. The acquisition module is used for acquiring patient information, and the patient information comprises basic information and information related to treatment; the risk grade determining module is used for processing the patient information through the risk evaluation model and determining a first venous thromboembolism risk grade and a first bleeding risk grade of the patient; the preventive measure determination module is used for determining the preventive measure of the venous thromboembolism of the patient based on the first venous thromboembolism risk level and the first bleeding risk level.
Description
Technical Field
The specification relates to the field of prevention and treatment of venous thromboembolism, in particular to an artificial intelligent prevention and treatment system, method, device and medium for venous thromboembolism.
Background
Venous Thromboembolism (VTE) is the combination of Deep Venous Thrombosis (DVT) and Pulmonary Embolism (PE), and is the manifestation of the same disease at different stages. Deep vein thrombosis is a venous reflux disorder caused by the failure of blood to normally coagulate in the deep vein, and is often found in the lower limbs. Pulmonary embolism is caused by thrombus shedding. Venous thromboembolism has the characteristics of high morbidity, high mortality, high missed diagnosis rate and high misdiagnosis rate, constitutes potential risks of medical quality and patient safety, and becomes a serious problem for clinical medical staff and hospital managers. However, as a preventable disease, the occurrence rate of venous thromboembolism can be remarkably reduced by actively and effectively preventing the venous thromboembolism, and the fatality rate can be remarkably reduced by standard diagnosis and treatment. By combining with Artificial Intelligence (AI) technology, a medical care synergistic venous thromboembolism prevention and treatment system is constructed, so that high-risk people can be detected in time, and medical care personnel can effectively intervene in the early stage of venous thromboembolism.
Therefore, how to combine the artificial intelligence technology to realize the risk assessment of the venous thromboembolism and assist the medical staff in preventing and treating the venous thromboembolism is an urgent problem to be solved.
Disclosure of Invention
One or more embodiments of the present specification provide an artificial intelligence prevention and treatment system for venous thromboembolism. The artificial intelligent prevention and treatment system for venous thromboembolism comprises: the system comprises an acquisition module, a risk level determination module and a prevention and control measure determination module; the acquisition module is used for acquiring patient information, and the patient information comprises basic information and information related to treatment; the risk level determination module is used for processing the patient information through a risk assessment model and determining a first venous thromboembolism risk level and a first bleeding risk level of the patient; the preventive measure determination module is configured to determine a preventive measure for venous thromboembolism of the patient based on the first venous thromboembolism risk level and the first bleeding risk level.
One or more embodiments of the present specification provide a method for artificial intelligence prevention and treatment of venous thromboembolism, including: acquiring patient information, wherein the patient information comprises basic information and information related to treatment; processing the patient information through a risk assessment model to determine a first venous thromboembolic disorder risk level and a first bleeding risk level for the patient; determining a measure of prevention of venous thromboembolism in the patient based on the first venous thromboembolism risk level and the first bleeding risk level.
One or more embodiments of the present description provide an artificial intelligence control device for venous thromboembolism, the device including at least one processor and at least one memory; the at least one memory is for storing computer instructions; the at least one processor is configured to execute at least a portion of the computer instructions to implement the method for artificial intelligence prevention and treatment of venous thromboembolism.
One or more embodiments of the present specification provide a computer-readable storage medium storing computer instructions, and when the computer instructions in the storage medium are read by a computer, the computer executes the method for artificial intelligence prevention and treatment of venous thromboembolism.
Drawings
This description will further illustrate by way of example embodiments, these exemplary embodiments will be described in detail by means of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:
FIG. 1 is a schematic diagram of an application scenario of an artificial intelligence prevention and treatment system for venous thromboembolism according to some embodiments of the present disclosure;
FIG. 2 is a block diagram of an exemplary system for artificial intelligence for prevention and treatment of venous thromboembolism according to some embodiments described herein;
FIG. 3 is an exemplary flow chart of a method for artificial intelligence for prevention and treatment of venous thromboembolism according to some embodiments described herein;
FIG. 4 is an exemplary block diagram of a risk assessment model according to some embodiments of the present description;
FIG. 5 is an exemplary block diagram of a pressure pump analysis model in accordance with certain embodiments described herein.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system," "device," "unit," and/or "module" as used herein is a method for distinguishing between different components, elements, parts, portions, or assemblies of different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of the present specification. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
Fig. 1 is a schematic view of an application scenario of an artificial intelligence prevention and treatment system for venous thromboembolism according to some embodiments of the present disclosure.
In some embodiments, the application scenario 100 of the venous thromboembolism artificial intelligence prevention and treatment system may include a storage device 110, a network 120, a processing device 130, a booster pump 140, and a user terminal 150.
In some embodiments, one or more components of the application scenario 100 may be connected and/or in communication with each other via a network 120 (e.g., a wireless connection, a wired connection, or a combination thereof). As shown in fig. 1, storage device 110 may be connected to processing device 130 through network 120. As another example, processing device 130 may be connected to user terminal 150 via network 120.
The network 120 may connect the components of the application scenario 100 and/or connect the application scenario 100 with external resource components. Information and/or data may be exchanged between one or more components of the venous thromboembolism artificial intelligence prevention system via network 120. For example, the network 120 may retrieve basic information of the patient from the storage device 110, and the like. In some embodiments, the network 120 may be any one or more of a wired network or a wireless network. In some embodiments, network 120 may include one or more network access points. For example, the network 120 may include wired or wireless network access points. The network may be a local area network, a wide area network, the internet, etc., and may be a combination of various network architectures.
The processing device 130 may be used to process information and/or data related to the application scenario 100, such as patient basic information, patient historical encounter records, and the like. The processing device 130 may include a risk assessment model and a pressure pump analysis model. Wherein the risk assessment model may be used to assess a first venous thromboembolic disorder risk level and a first bleeding risk level. The booster pump analysis model can be used for outputting the working parameters of the booster pump and the corresponding abnormal probability of the physical signs. In some embodiments, processing device 130 may process data, information, and/or processing results obtained from other devices or system components and execute program instructions based on the data, information, and/or processing results to perform one or more functions described herein.
The booster pump 140 may be used for patient control. The pressure pump 140 comprises an intermittent inflation pressure pump, which can be used to promote the reflux of fluids such as veins and lymph of the extremities by periodically inflating and deflating the cuff (inflation and deflation correspond to pressurization and depressurization, respectively). In some embodiments, the pressure pump 140 may obtain pressure pump operating parameters, such as pressure level, operating time, pressure interval, etc., output by the processing device 130. The pressure pump 140 can work according to the working parameters of the pressure pump, so as to realize the prevention and treatment of the venous thromboembolism.
Fig. 2 is a block diagram of an exemplary system for artificial intelligence for prevention and treatment of venous thromboembolism according to some embodiments described herein.
In some embodiments, the venous thromboembolism artificial intelligence prevention system 200 may include an acquisition module 210, a risk level determination module 220, and a prevention measure determination module 230.
The acquisition module 210 may be used to acquire patient information, including basic information and visit related information.
Basic information refers to information relating to the identity and physical condition of the patient. For example, the basic information may include one or more of gender, age, height, weight, blood pressure, and the like. The visit information refers to information related to the visit of the patient. For example, the visit information may include one or more of a care record, admission record, medical order, surgical record, medical record, examination report, and the like.
In some embodiments, the patient may actively upload the basic information to the terminal device, and the obtaining module 210 may obtain the basic information of the patient through the terminal device. In some embodiments, the obtaining module 210 may obtain the basic information of the patient through a medical system storing the basic information of the patient. A medical system refers to a system for collecting, organizing and storing patient information.
The risk level determination module 220 may be configured to process the patient information via a risk assessment model to determine a first venous thromboembolic disorder risk level and a first bleeding risk level for the patient.
The first venous thromboembolism risk level refers to a risk level determined according to the degree of risk that a patient is at risk for venous thromboembolism. For example, a first venous thromboembolic disorder risk rating may be classified on a scale of 1 to 10, with a higher rating number indicating a higher risk of the patient for venous thromboembolic disorders. As another example, the first venous thromboembolic disorder risk rating may be classified as mild, severe, very severe, or the like.
The first bleeding risk level refers to a risk level determined according to the degree of bleeding risk of the patient. For example, a first bleeding risk rating may be graded 1-10, with a higher rating number indicating a higher risk of bleeding for the patient. As another example, the first bleeding risk level may be classified as mild, severe, very severe, and the like.
In some embodiments, patient information may be input into a base layer of the risk assessment model, which outputs patient characteristic information. Then the characteristic information of the patient is respectively input into a first venous thromboembolism evaluation layer and a first hemorrhage evaluation layer of the risk evaluation model, the first venous thromboembolism evaluation layer outputs a first venous thromboembolism risk level, and the first hemorrhage evaluation layer outputs a first hemorrhage risk level.
The patient characteristic information refers to characteristic information which is extracted according to the patient information and is relevant to determining venous thromboembolism of the patient. In some embodiments, the patient characteristic information may include one or more of patient physical condition information, patient thrombus information, patient bleeding information, and the like.
The patient physical condition information refers to information related to the physical condition of the patient. For example, the patient health information may include one or more of a healthy patient health, a sub-healthy patient health, a bad patient health, and the like. The patient thrombus information is information related to the history of patient thrombus. For example, the patient thrombus information may include one or more of a patient thrombus site, a patient onset time, a patient cure time, a patient thrombus occurrence count, and the like. Patient bleeding information refers to information relating to the history of occurrence of bleeding in a patient. For example, the patient bleeding information may include one or more of a patient bleeding site, a patient bleeding time, a patient healing time, a number of patient bleedings occurring, and the like.
For more on the risk assessment model, the base layer of the risk assessment model, the first venous thromboembolism assessment layer, and the first hemorrhage assessment layer, please refer to fig. 4 and its related description.
The preventative action determination module 230 may be used to determine a preventative action for venous thromboembolism in the patient based on the first venous thromboembolism risk level and the first bleeding risk level.
The prophylactic measure may be a prophylactic measure against venous thromboembolism in a patient. In some embodiments, the preventative measures may include one or more of active measures, wearing measures, external force measures, medication measures, and the like.
Active measures refer to the patient's timed out-of-bed activities. The active measures are suitable for patients with low venous thromboembolism risk level. For example, patients with venous thromboembolic disorders risk classification at grade 2 and below.
The wearing measure is that the patient wears graded pressure elastic socks. The wearing measures are suitable for patients with lower risk of venous thromboembolism. For example, patients with venous thromboembolic disorders risk classification 2-5. In some embodiments, the higher the risk level, the greater the compression level of the stretch sock used by the patient.
The external force measure means to prevent and treat the patient by using a pressure pump (for example, an intermittent inflation pressure pump). The external force measures are suitable for patients with higher venous thromboembolism risk level. For example, patients with venous thromboembolic disorders risk classification from 6 to 10.
The taking measures refer to taking anticoagulant drugs under the guidance of doctors. The taking measures are suitable for patients with higher risk level of the venous thromboembolism and patients with lower bleeding risk level. For example, patients with venous thromboembolic disease risk rating of 8-10 and bleeding risk rating of 5 and below.
In some embodiments, the preventative measure determination module 230 may determine a preventative measure for venous thromboembolism in the patient based on the first venous thromboembolism risk level and the level of the first bleeding risk level. For example, when the risk level of venous thromboembolism is 2 grade and below, the adopted preventive measures are active measures; when the risk level of the venous thromboembolism is a patient with a level 2-5, the adopted prevention and treatment measures are wearing measures; when the risk level of the venous thromboembolism is a patient with 6-10 grades, the adopted prevention and treatment measures are external force measures; when the risk grade of the venous thromboembolism disease is 8-10 grades and the bleeding risk grade is 5 grades or below, the adopted prevention and treatment measures are medication measures.
In some embodiments, when the treatment includes treatment of the patient with a booster pump, the venous thromboembolism artificial intelligence control system 200 may also include a booster pump control module 240. The booster pump prevention and treatment module 240 may be configured to process the patient information, the first venous thromboembolism risk level, and the first bleeding risk level through a booster pump analysis model, and determine operating parameters of the booster pump and a probability of a physical sign abnormality corresponding to the patient.
In some embodiments, the operating parameters of the booster pump may include one or more of a pressure level of the booster pump, an operating time period of the booster pump, a pressurization interval of the booster pump, and the like.
Abnormal signs refer to abnormal physical signs of a patient or obvious pathological symptoms of the patient. For example, the abnormal signs may refer to the abnormal heart rate, blood pressure, etc. of the patient. For another example, a sign abnormality can refer to a patient experiencing pain, skin discoloration, dyspnea, and the like.
In some embodiments, patient information can be input into a base layer of the booster pump analysis model, which outputs patient characteristic information. And then inputting the characteristic information of the patient, the first venous thromboembolism risk level and the first bleeding risk level into a parameter analysis layer of the booster pump analysis model, and outputting working parameters of the booster pump by the parameter analysis layer.
In some embodiments, the operational parameters of the pump and the patient characteristic information can be input into a probabilistic analysis layer of the analysis model of the pump, and the probabilistic analysis layer outputs the probability of the abnormal physical sign.
In some embodiments, operational parameters of the pressurizing pump and patient characteristic information may also be input into a second venous thromboembolic disorder evaluation layer, which outputs a second venous thromboembolic disorder risk level. The operational parameters of the compression pump and the patient characteristic information may be input into a second bleeding evaluation layer, which outputs a second bleeding risk level. And then inputting the second venous thromboembolism risk level, the second bleeding risk level, the working parameters of the pressure pump and the patient characteristic information into a probability analysis layer of the pressure pump analysis model, and outputting the abnormal probability of the physical sign by the probability analysis layer.
For more description of the pressure pump analysis model, the base layer of the pressure pump analysis model, the parameter analysis layer, the second venous thromboembolism assessment layer, the second hemorrhage assessment layer, and the probability analysis layer, please refer to fig. 5 and its related description.
In some embodiments, the pressure pump prevention and treatment module 240 may be further configured to send a reminding message to the user terminal of the target user when the abnormal probability of the physical sign is greater than the preset threshold, where the reminding message is used to remind the target user to monitor the prevention and treatment process.
In some embodiments, the preset threshold may be set empirically by one skilled in the art. For example, the preset threshold may be 80%, 85%, etc.
In some embodiments, the reminder information may be used to remind the target user to monitor the control process. The reminding information can be one or more of characters, images or videos.
The target user refers to a person who monitors the real-time process of the patient's preventive measures. For example, the target user may be a nurse, a doctor, or the like.
The monitoring of the course of prevention may be a monitoring of the course of implementation of a measure of prevention of venous thromboembolism of the patient by the target user. In some embodiments, when the target user monitors the implementation process of the preventive measure for venous thromboembolism of the patient and finds that the improvement effect of the preventive measure on venous thromboembolism of the patient is not good, the parameters of the preventive measure can be changed directly by the target user (e.g., a nurse or a doctor) or after the target user (e.g., a nurse) consults the doctor's advice.
In some embodiments, the reminder information may include a frequency of reminding a target user (e.g., nurse, doctor, etc.) to manually monitor the prevention process.
In some embodiments, the frequency of manual monitoring of the prevention process can be determined based on the sign anomaly probability. In some embodiments, the greater the sign anomaly probability value, the greater the frequency of manual monitoring of the prevention and treatment process. For example, if the probability value of the abnormal physical signs is greater than 90%, the frequency of the manual monitoring and prevention process is set to be once every two minutes; if the abnormal probability value of the physical signs is more than 80% and less than 90%, setting the frequency of the manual monitoring and preventing process to be once every four minutes; and if the abnormal probability value of the physical sign is more than 70% and less than 80%, setting the fixed frequency of the manual monitoring and prevention process to be once every six minutes.
In some embodiments, manually monitoring the frequency of the prophylactic and therapeutic procedure can be based on confidence corrections of the second venous thromboembolic disorder evaluation layer and the second hemorrhage evaluation layer of the booster pump analysis model.
For more on the second venous thromboembolic disorder evaluation layer and the second hemorrhage evaluation layer, please refer to fig. 5 and the related description.
In some embodiments, the compression pump prevention and treatment module 240 can weight average the confidence levels of the second venous thromboembolic disorder evaluation layer and the second hemorrhage evaluation layer to obtain a composite confidence level. In some embodiments, the confidence of the second venous thromboembolic disorder assessment layer may have a weight value that is greater than a weight value of the confidence of the second hemorrhage assessment layer. For example, a weight value of the confidence of the second venous thromboembolism assessment layer may be preset to be 0.7, a weight value of the confidence of the second hemorrhage assessment layer may be 0.3, and if the confidence of the second venous thromboembolism assessment layer is 0.9 and the confidence of the second hemorrhage assessment layer is 0.8, the combined confidence =0.9 × 0.7+0.8 × 0.3=0.87.
In some embodiments, the booster pump control module 240 may correct the frequency of manually monitoring the control process based on the integrated confidence. For example, F = F may be set0Z, where F is the corrected frequency, F0And Z is a preset frequency and a comprehensive confidence coefficient. For example, F0Once every four minutes, Z =0.8, then F = F0Z =4/0.8=5, representing a correction of the frequency from once in four minutes to once in five minutes.
In some embodiments of the present description, the first venous thromboembolism risk level and the first bleeding risk level of the patient can be determined by acquiring the patient information and then intelligently analyzing the patient information, so as to realize risk assessment on venous thromboembolism, and further quickly and accurately determine the venous thromboembolism prevention and treatment measures suitable for the patient himself, and assist medical staff in prevention and treatment. In other embodiments of the present disclosure, a weighted value of the confidence level of the second venous thromboembolism disorder evaluation layer is set to be greater than a weighted value of the confidence level of the second hemorrhage evaluation layer, and then the confidence levels of the second venous thromboembolism disorder evaluation layer and the second hemorrhage evaluation layer are weighted and averaged to obtain a comprehensive confidence level, so as to correct the frequency of the manual monitoring and prevention process, and the corrected frequency is more accurate, thereby better assisting medical care personnel in monitoring the prevention and treatment process.
It should be noted that the above description of the artificial intelligence system for preventing and treating venous thromboembolism and the modules thereof is only for convenience of description and should not be construed as limiting the present specification to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. In some embodiments, the acquisition module 210, the risk level determination module 220, the control measure determination module 230, and the pressure pump control module 240 disclosed in fig. 2 may be different modules in a system, or may be a single module that performs the functions of two or more of the above-described modules. For example, each module may share one memory module, and each module may have its own memory module. Such variations are within the scope of the present disclosure.
Fig. 3 is an exemplary flow chart of a method for artificial intelligence for prevention and treatment of venous thromboembolism according to some embodiments described herein. In some embodiments, the process 300 may be performed by the venous thromboembolism artificial intelligence prevention system 200 or the processing device 130. As shown in fig. 3, the process 300 includes the following steps.
In step 310, patient information is obtained, wherein the patient information comprises basic information and information related to a doctor visit. In some embodiments, step 310 may be performed by acquisition module 210.
For the definition and description of the related terms, and the method for obtaining the patient information, please refer to the obtaining module 210 in fig. 2 and the related description thereof.
At step 320, the patient information is processed through the risk assessment model to determine a first venous thromboembolic disorder risk level and a first bleeding risk level for the patient.
For the definition and description of the relevant terms and the method of determining the first venous thromboembolic disorder risk level and the first bleeding risk level of a patient, please refer to the risk level determination module 220 in fig. 2 and its associated description.
A preventive measure for venous thromboembolism of the patient is determined based on the first venous thromboembolism risk level and the first bleeding risk level, step 330.
For the definition and description of the related terms, and the method of determining a measure of treatment for venous thromboembolism in a patient, please refer to the measure determination module 230 in fig. 2 and its related description.
In some embodiments, when the treatment includes treating the patient with a booster pump, the process 300 may further include steps 340-350 (not shown).
And 340, processing the patient information, the first venous thromboembolism risk level and the first bleeding risk level through a booster pump analysis model, and determining working parameters of a booster pump and the abnormal probability of the corresponding physical sign of the patient.
For the definition and description of the related terms, and the method for determining the operating parameters of the pressurizing pump and the abnormal probability of the corresponding physical signs of the patient, please refer to the pressurizing pump preventing and treating module 240 in fig. 2 and the related description thereof.
And 350, when the abnormal probability of the physical sign is greater than a preset threshold value, sending reminding information to a user terminal of the target user, wherein the reminding information is used for reminding the target user to monitor the prevention and treatment process.
For the definition and description of the related terms, please refer to the booster pump control module 240 in fig. 2 and the related description thereof.
It should be noted that the above description of the process 300 is for illustration and description only and is not intended to limit the scope of the present disclosure. Various modifications and changes to flow 300 will be apparent to those skilled in the art in light of this description. However, such modifications and variations are still within the scope of the present specification. For example, process 300 may perform only steps 310-330 and not steps 340-350.
FIG. 4 is an exemplary block diagram of a risk assessment model according to some embodiments of the present description.
As shown in structure 400, a risk assessment model is used to assess a patient's venous thromboembolic disorder risk level and hemorrhage risk level. In some embodiments, the input to the risk assessment model may include patient information and the output of the risk assessment model may include a first venous thromboembolic disorder risk level and a first bleeding risk level. In some embodiments, the risk assessment model may include a base layer, a first venous thromboembolic disorder assessment layer, and a first hemorrhage assessment layer. The output of the base layer serves as the input to the first venous thromboembolic disorder assessment layer and the first bleeding assessment layer, and the output of the first venous thromboembolic disorder assessment layer and the first bleeding assessment layer serves as the output of the risk assessment model.
The base layer is used to determine patient characteristic information. The input to the base layer may include patient information. The output of the base layer may include patient characteristic information.
The first venous thromboembolic disorder evaluation layer is used to determine a first venous thromboembolic disorder risk rating. The input to the first venous thromboembolic disorder assessment layer may include patient characteristic information. The output of the first venous thromboembolic disorder evaluation layer may include a first venous thromboembolic disorder risk rating.
The first bleeding evaluation layer is used to determine a first bleeding risk level. The input to the first bleeding assessment layer may include patient characteristic information. The output of the first bleeding evaluation layer may include a first bleeding risk level. In some embodiments, the risk level may be divided according to the size of the degree.
In some embodiments, the confidence of the output results of the first venous thromboembolism assessment layer and the first hemorrhage assessment layer is less than a threshold value, and a reminder message is sent to a user terminal (e.g., a terminal used by a doctor) to remind the user to manually assess risk. In some embodiments, the confidence threshold may be preset manually. For example, if the confidence preset threshold of the first venous thromboembolism evaluation layer is 0.7 and the actual confidence of the output result is 0.5 less than the threshold, sending a reminding message to a doctor terminal to remind the doctor to manually evaluate the risk level of the first venous thromboembolism.
In some embodiments, the base layer, the first venous thromboembolic disorder evaluation layer, and the first hemorrhage evaluation layer of the risk assessment model may be trained separately.
In some embodiments, the training data of the base layer may be sets of labeled training samples, which may be patient information. The patient information may be derived from patient history information stored in a memory device. In some embodiments, the training sample may include at least sample physical condition information, sample care records, sample surgery records, and the like, for the patient. The labels of the training samples of the base layer are patient characteristic information. In some embodiments of the present description, the label may be obtained from patient history information recorded by a storage device, or the label may be obtained by manual labeling.
In some embodiments, the training data of the first venous thromboembolism evaluation layer and the first hemorrhage evaluation layer may be sets of labeled training samples, which may be patient characteristic information output by the base layer. The training sample of the first venous thromboembolism assessment layer is labeled with a first venous thromboembolism risk rating. The label of the training sample of the first venous thromboembolism disorder evaluation layer can be obtained by calculating the thrombus related parameters of the input patient characteristic information, for example, calculating the occurrence probability of thrombus according to the occurrence number and time of thrombus, and evaluating the risk grade of the first venous thromboembolism disorder according to the occurrence probability of thrombus. The training sample of the first bleeding assessment layer is labeled with a first bleeding risk level. The label of the training sample of the first bleeding evaluation layer may be obtained by calculating bleeding related parameters of the inputted patient characteristic information, for example, calculating bleeding severity according to bleeding occurrence site and healing time, and evaluating the first bleeding risk level according to the bleeding severity.
In some embodiments, a loss function is constructed from the label and the results of the layers of the initial risk assessment model, and parameters of the risk assessment model are iteratively updated by gradient descent or other methods based on the loss function. And completing model training when preset conditions are met to obtain a trained risk assessment model. The preset condition may be that the loss function converges, the number of iterations reaches a threshold, and the like.
In some embodiments, the base layer, the first venous thromboembolism evaluation layer, and the first hemorrhage evaluation layer are obtained by co-training.
In some embodiments, the risk assessment model may train the base layer, the first venous thromboembolism assessment layer, and the first hemorrhage assessment layer based on a quantity of labeled training data. Specifically, the training data with labels, i.e. the patient information, can be input into the base layer to obtain the patient characteristic information output by the base layer; then the characteristic information of the patient is respectively input into the first venous thromboembolism disorder evaluation layer and the first hemorrhage evaluation layer, and a first venous thromboembolism disorder risk grade output by the first venous thromboembolism disorder evaluation layer and a first hemorrhage risk grade output by the first hemorrhage evaluation layer are obtained. Parameters of the base layer, the first venous thromboembolism disorder evaluation layer and the first bleeding evaluation layer are updated through training, and when a trained model meets preset conditions, the trained base layer, the first venous thromboembolism disorder evaluation layer and the first bleeding evaluation layer are obtained. The preset condition may be that the loss function is less than a threshold, convergence, or that the training period reaches a threshold.
The risk assessment model can preliminarily determine a first venous thromboembolism risk level and a first bleeding risk level of a patient based on patient information, so that manual judgment time is reduced, a prompt is timely sent to medical workers, and the medical workers are helped to timely perform measures for preventing and treating venous thromboembolism.
Fig. 5 is an exemplary block diagram of a booster pump analysis model according to some embodiments herein.
As shown in structure 500, the booster pump analysis model can obtain the probability of sign abnormalities. The booster pump analysis model may include a base layer, a parametric analysis layer, and a probabilistic analysis layer connected in sequence. Patient information may be entered into the base layer, which outputs patient characteristic information. And then inputting the characteristic information of the patient, the first venous thromboembolism risk level and the first bleeding risk level into a parameter analysis layer, and outputting the working parameters of the pressurizing pump by the parameter analysis layer. And inputting the working parameters of the pressurizing pump and the characteristic information of the patient into a probability analysis layer, and outputting the abnormal probability of the physical sign by the probability analysis layer. In some embodiments, the pressure pump analysis model, the base layer, the parametric analysis layer, and the probabilistic analysis layer may include a model derived from a Convolutional Neural Network (CNN), a Deep Neural Network (DNN), or a combination thereof, and the like.
In some embodiments, the booster pump analysis model may further include a second venous thromboembolism assessment layer and a second bleeding assessment layer, both connected between the parametric analysis layer and the probabilistic analysis layer. Patient characteristic information and compression pump operating parameters may be input into a second venous thromboembolic disorder assessment layer, which outputs a second venous thromboembolic disorder risk rating. Patient characteristic information and compression pump operating parameters may be entered into a second bleeding assessment layer, which outputs a second bleeding risk level. And then inputting the second venous thromboembolism risk level, the second bleeding risk level, the patient characteristic information and the working parameters of the pressurizing pump into a probability analysis layer, and outputting the abnormal probability of the physical signs by the probability analysis layer. In some embodiments, the second venous thromboembolic disorder evaluation layer and the second hemorrhage evaluation layer may include models derived from convolutional neural networks, deep neural networks, or combinations thereof, and the like.
In some embodiments, the base layer, the parametric analysis layer, the probabilistic analysis layer, the second venous thromboembolism assessment layer, and the second hemorrhage assessment layer may be obtained through training. Training of the base layer, the parametric analysis layer, the probabilistic analysis layer, the second venous thromboembolism assessment layer, and the second bleeding assessment layer may be performed by the processing device 130, which training may be based on the following methods.
Respectively obtaining training samples and labels thereof of a base layer, a parameter analysis layer, a probability analysis layer, a second venous thromboembolism evaluation layer and a second hemorrhage evaluation layer.
The training samples of the base layer may be historical patient information. The labels of the training samples of the base layer may be corresponding historical patient characteristic information in the historical patient information. The historical patient characteristic information can be obtained by manually extracting the historical patient information.
The training samples of the parametric analysis layer may be historical patient characteristic information, a historical first venous thromboembolic disorder risk level, and a historical first hemorrhage risk level. The labels of the training samples of the parametric analysis layer may be the operating parameters of the historical booster pump.
The training samples of the probabilistic analysis layer may be the operating parameters and the historical patient characteristic information of the historical booster pump, or may be the operating parameters, the historical patient characteristic information, the historical second venous thromboembolism risk level and the historical second bleeding risk level of the historical booster pump. The label of the training sample of the probability analysis layer can be the abnormal probability of the historical signs.
The training samples of the second venous thromboembolic disorder evaluation layer may be historical patient characteristic information and historical pressurizing pump operating parameters. The label of the training sample of the second venous thromboembolism assessment layer may be a historical second venous thromboembolism risk rating.
The training samples of the second bleeding evaluation layer may be historical patient characteristic information and historical pressurizing pump operating parameters. The label of the training sample of the second bleeding evaluation tier may be a historical second bleeding risk level.
In some embodiments, the operating parameters of the historical booster pump, the historical sign anomaly probability, the historical second venous thromboembolic disorder risk level, and the historical second hemorrhage risk level may all be obtained by a medical system. For example, the medical system may store operating parameters of the historical booster pump that the physician determines based on the historical patient characteristic information, the historical first venous thromboembolic disorder risk level, and the historical first hemorrhage risk level.
And respectively inputting a plurality of training samples with labels into the corresponding initial base layer, initial parameter analysis layer, initial probability analysis layer, initial second venous thromboembolism evaluation layer and initial second hemorrhage evaluation layer. And iteratively updating the parameters of the initial base layer, the initial parameter analysis layer, the initial probability analysis layer, the initial second venous thromboembolism evaluation layer and the initial second hemorrhage evaluation layer through training. And when the trained model meets the preset condition, finishing training, and acquiring a trained base layer, a parameter analysis layer, a probability analysis layer, a second venous thromboembolism evaluation layer and a second hemorrhage evaluation layer. In some embodiments, the preset condition may be that the loss function is less than a threshold, convergence, or that the training period reaches a threshold.
In some embodiments, the base layer, the parametric analysis layer and the probabilistic analysis layer in the pressure pump analysis model may be jointly trained, so as to obtain a trained pressure pump analysis model. In some embodiments, the base layer, the parametric analysis layer, the probabilistic analysis layer, the second venous thromboembolism evaluation layer, and the second hemorrhage evaluation layer in the pressure pump analysis model may be jointly trained to obtain a trained pressure pump analysis model. In some embodiments, the second venous thromboembolic disorder assessment layer has a greater weight on the loss term than the second hemorrhage assessment layer. The joint training may be performed by the processing device 130. The joint training may be implemented based on the following method.
In some embodiments, the training samples of the pressure pump analysis model may be historical patient information, historical first venous thromboembolic disorder risk level, and historical first bleeding risk level, and the labels of the training samples may be historical sign anomaly probabilities corresponding to the historical patient information, the historical first venous thromboembolic disorder risk level, and the historical first bleeding risk level.
In some embodiments, the historical patient information in the training sample is input into a base layer in the pressure pump analysis model, the output of the base layer, the historical first venous thromboembolism risk level and the historical first bleeding risk level in the training sample are input into a parametric analysis layer in the pressure pump analysis model, then the output of the parametric analysis layer and the output of the base layer are input into a probabilistic analysis layer, or the output of the parametric analysis layer and the output of the base layer are input into a second venous thromboembolism evaluation layer and a second bleeding evaluation layer in the pressure pump analysis model, then the output of the second venous thromboembolism evaluation layer, the output of the second bleeding evaluation layer, the output of the base layer and the output of the parametric analysis layer are input into the probabilistic analysis layer, and a loss function is constructed based on the output of the probabilistic analysis layer and a parameter of the base layer, the parametric analysis layer and the probabilistic analysis layer is iteratively updated based on the loss function, or the base layer, the parametric analysis layer, the probabilistic analysis layer, the second venous thromboembolism evaluation layer and the second parametric analysis layer are iteratively updated based on the loss function until a preset bleeding condition of the training pump analysis model is completed. In some embodiments, the preset condition may be that the loss function is less than a threshold, convergence, or that the training period reaches a threshold.
In some embodiments, the base layer parameters of the trained booster pump analysis model are the same as the base layer parameters of the trained risk assessment model.
In some embodiments, whether to send a prompting message to the user terminal for prompting the user terminal to manually set the pressure pump operating parameters of the pressure pump analysis model may be determined based on whether the sign anomaly probability obtained by the pressure pump analysis model is greater than a preset threshold or the confidence of the pressure pump operating parameter result of the pressure pump analysis model. In some embodiments, the confidence level of the parametric analysis layer of the pressure pump analysis model may be taken as the confidence level of the pressure pump operating parameter results. The confidence level of the parametric analysis layer may refer to a confidence level of the parametric analysis layer of the pressure pump analysis model. For example, if the abnormal probability of the physical sign obtained by the pressure pump analysis model is greater than a preset threshold (e.g., 90%), sending a reminding message to the user terminal, where the reminding message is used to remind the user terminal to manually reset the current pressure pump operating parameters of the pressure pump analysis model. For another example, if the confidence of the result of the operating parameter of the pressure pump analysis model is low, a reminding message is sent to the user terminal, and the reminding message is used for reminding the user terminal to manually reset the current operating parameter of the pressure pump analysis model.
In some embodiments, the confidence level of the parameter analysis layer of the risk assessment model, the confidence levels of the first venous thromboembolic disorder assessment layer and the first hemorrhage assessment layer of the risk assessment model may be fused to determine the confidence level of the pressurizing pump operating parameter result. In some embodiments, the manner of fusion may be a weighted average of the confidence levels of the parametric analysis layer of the risk assessment model, the first venous thromboembolic disorder assessment layer, and the first hemorrhage assessment layer of the risk assessment model. For example, the confidence of the result of the operation parameter of the pressure pump may be obtained by performing weighted averaging such that the confidence weight of the parameter analysis layer is set to be the maximum, the confidence weight of the first venous thromboembolic disorder evaluation layer is set to be the second, and the confidence weight of the first hemorrhage evaluation layer is set to be the minimum.
In some embodiments of the present description, obtaining the parameters of the pressure pump analysis model by a combined training mode is beneficial to solve the problems that the label is difficult to obtain when the base layer is trained alone, the label and the training sample are difficult to obtain when the parameter analysis layer, the second venous thromboembolism evaluation layer and the second hemorrhage evaluation layer are trained alone, and the training sample is difficult to obtain when the probability analysis layer is trained alone; the confidence of the working parameter result of the pressurizing pump is determined, so that the confidence of a parameter analysis layer of the risk assessment model and the confidence of a first venous thromboembolism assessment layer and a first bleeding assessment layer of the risk assessment model are fused, and the finally determined working parameter of the pressurizing pump is more accurate and meets the actual prevention and treatment measure needs of a patient.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.
Also, the description uses specific words to describe embodiments of the specification. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.
Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features are required than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document is inconsistent or contrary to the present specification, and except where the application history document is inconsistent or contrary to the present specification, the application history document is not inconsistent or contrary to the present specification, but is to be read in the broadest scope of the present claims (either currently or hereafter added to the present specification). It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present specification can be seen as consistent with the teachings of the present specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.
Claims (7)
1. An artificial intelligent prevention and treatment system for venous thromboembolism is characterized by comprising an acquisition module, a risk grade determination module and a prevention and treatment measure determination module;
the acquisition module is used for acquiring patient information, and the patient information comprises basic information and information related to treatment;
the risk level determination module is used for processing the patient information through a risk assessment model and determining a first venous thromboembolism risk level and a first bleeding risk level of the patient;
the preventive measure determination module is used for determining the preventive measure of the venous thromboembolism of the patient based on the first venous thromboembolism risk level and the first bleeding risk level.
2. The venous thromboembolism artificial intelligence prevention and treatment system of claim 1, wherein the risk assessment model includes a foundation layer, a first venous thromboembolism assessment layer, and a first hemorrhage assessment layer,
the base layer is used for processing the patient information and determining patient characteristic information;
the first venous thromboembolism evaluation layer is used for processing the patient characteristic information and determining the first venous thromboembolism risk level; and
the first bleeding evaluation layer is used for processing the patient characteristic information and determining the first bleeding risk level.
3. The venous thromboembolism artificial intelligence prevention and treatment system of claim 1 wherein the prevention and treatment includes prevention and treatment of the patient using a booster pump, the system further comprising a booster pump prevention and treatment module:
the pressure pump prevention and treatment module is used for processing the patient information, the first venous thromboembolism risk level and the first bleeding risk level through a pressure pump analysis model, and determining working parameters of the pressure pump and abnormal probability of physical signs corresponding to the patient.
4. The venous thromboembolism artificial intelligence prevention and treatment system of claim 3, wherein the booster pump prevention and treatment module is further to:
and when the abnormal probability of the physical sign is greater than a preset threshold value, sending reminding information to a user terminal of a target user, wherein the reminding information is used for reminding the target user to monitor the prevention and treatment process.
5. An artificial intelligence prevention and treatment method for venous thromboembolism, which is characterized by comprising the following steps:
acquiring patient information, wherein the patient information comprises basic information and information related to treatment;
processing the patient information through a risk assessment model to determine a first venous thromboembolic disorder risk level and a first bleeding risk level for the patient;
determining a measure of prevention of venous thromboembolism in the patient based on the first venous thromboembolism risk level and the first bleeding risk level.
6. An artificial intelligence prevention and treatment device for venous thromboembolism, which is characterized by comprising at least one processor and at least one memory;
the at least one memory is for storing computer instructions;
the at least one processor is configured to execute at least a portion of the computer instructions to implement the artificial intelligence method for venous thromboembolism as defined in claim 5.
7. A computer-readable storage medium storing computer instructions, wherein when the computer instructions stored in the storage medium are read by a computer, the computer performs the method for artificial intelligence management of venous thromboembolism according to claim 5.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006052952A2 (en) * | 2004-11-09 | 2006-05-18 | The Brigham And Women's Hospital, Inc. | System and method for determining whether to issue an alert to consider prophylaxis for a risk condition |
| CN105992552A (en) * | 2013-11-27 | 2016-10-05 | 美敦力公司 | Precision dialysis monitoring and synchonization system |
| CN107145755A (en) * | 2017-05-16 | 2017-09-08 | 陈韵岱 | Cardiovascular chronic diseases management method based on Intelligent Decision Support Technology |
| CN108498076A (en) * | 2018-04-19 | 2018-09-07 | 金华市中心医院 | Inpatient's venous thromboembolism safety management system and application |
| CN112164457A (en) * | 2020-10-23 | 2021-01-01 | 南通市第二人民医院 | Hemodialysis chamber instrument management method and device |
| CN113470825A (en) * | 2021-07-23 | 2021-10-01 | 北京惠每云科技有限公司 | Venous thromboembolism prediction system, method, equipment and medium |
| CN113571187A (en) * | 2014-11-14 | 2021-10-29 | Zoll医疗公司 | Medical premonitory event estimation system and externally worn defibrillator |
-
2022
- 2022-06-06 CN CN202210630539.XA patent/CN115274102B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2006052952A2 (en) * | 2004-11-09 | 2006-05-18 | The Brigham And Women's Hospital, Inc. | System and method for determining whether to issue an alert to consider prophylaxis for a risk condition |
| CN105992552A (en) * | 2013-11-27 | 2016-10-05 | 美敦力公司 | Precision dialysis monitoring and synchonization system |
| CN113571187A (en) * | 2014-11-14 | 2021-10-29 | Zoll医疗公司 | Medical premonitory event estimation system and externally worn defibrillator |
| CN107145755A (en) * | 2017-05-16 | 2017-09-08 | 陈韵岱 | Cardiovascular chronic diseases management method based on Intelligent Decision Support Technology |
| CN108498076A (en) * | 2018-04-19 | 2018-09-07 | 金华市中心医院 | Inpatient's venous thromboembolism safety management system and application |
| CN112164457A (en) * | 2020-10-23 | 2021-01-01 | 南通市第二人民医院 | Hemodialysis chamber instrument management method and device |
| CN113470825A (en) * | 2021-07-23 | 2021-10-01 | 北京惠每云科技有限公司 | Venous thromboembolism prediction system, method, equipment and medium |
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