CN113611391A - Liver transplantation process blood transfusion prediction method, system, equipment and medium - Google Patents

Liver transplantation process blood transfusion prediction method, system, equipment and medium Download PDF

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CN113611391A
CN113611391A CN202110982124.4A CN202110982124A CN113611391A CN 113611391 A CN113611391 A CN 113611391A CN 202110982124 A CN202110982124 A CN 202110982124A CN 113611391 A CN113611391 A CN 113611391A
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data set
key information
content
prediction
prediction model
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罗雁威
刘乐平
桂嵘
姜贞贞
刘凤霞
傅云峰
李建
黄蓉
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Third Xiangya Hospital of Central South University
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Third Xiangya Hospital of Central South University
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24323—Tree-organised classifiers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders

Abstract

The embodiment of the disclosure provides a method, a system, equipment and a medium for predicting blood transfusion in a liver transplantation process, which belong to the technical field of medical care informatics and specifically comprise the following steps: training the XGBOOST algorithm on the sample data set, and establishing a prediction model; collecting a key information data set of a target person, wherein the key information data set comprises age, portal hypertension, activated partial thrombin time, direct bilirubin content, globulin content, hemoglobin content, glutamic-oxalacetic transaminase content, glutamic-pyruvic transaminase content and liver diagnosis information; and inputting the key information data set into the prediction model to obtain a predicted blood transfusion scheme. According to the scheme, the machine learning algorithm is utilized to learn the sample data set, the prediction model is established, then the collected key information data set of the target person is input into the prediction model, and the prediction blood transfusion scheme is obtained, so that the prediction efficiency and accuracy are improved, and the safety in the operation process is improved.

Description

Liver transplantation process blood transfusion prediction method, system, equipment and medium
Technical Field
The embodiment of the disclosure relates to the technical field of medical care informatics, in particular to a method, a system, equipment and a medium for predicting blood transfusion in a liver transplantation process.
Background
Liver transplantation is currently an effective method of treating end-stage liver disease, and long and complicated surgical procedures may result in perioperative bleeding. Most patients require concentrated red blood cell transfusions during or after surgery, which, while increasing the oxygen supply to the patient and improving tissue perfusion, is associated with a number of side effects, such as increased risk of deep vein thrombosis, increased fibrosis, cancer recurrence and increased mortality, which adversely affect the prognosis of the patient. In case of massive bleeding, a massive blood transfusion scheme is mostly adopted for volume treatment in clinic, but the blood products are transfused in a large amount, and adverse reactions such as bleeding, blood pressure reduction, slow heart rate, ventricular fibrillation and the like are easy to occur. Most of the research on predicting liver transplantation red blood cell infusion is based on traditional linear models and logistic regression, the interaction among a plurality of variables cannot be understood, and the prediction efficiency and accuracy are poor.
Therefore, an efficient and accurate liver transplantation process blood transfusion prediction method is urgently needed.
Disclosure of Invention
In view of the above, embodiments of the present disclosure provide a method, a system, a device, and a medium for predicting blood transfusion during liver transplantation, which at least partially solve the problem of poor prediction efficiency and prediction accuracy in the prior art.
In a first aspect, an embodiment of the present disclosure provides a method for predicting blood transfusion during liver transplantation, including:
training the XGBOOST algorithm on the sample data set, and establishing a prediction model;
collecting a key information data set of a target person, wherein the key information data set comprises age, portal hypertension, activated partial thrombin time, direct bilirubin content, globulin content, hemoglobin content, glutamic-oxalacetic transaminase content, glutamic-pyruvic transaminase content and liver diagnosis information;
and inputting the key information data set into the prediction model to obtain a predicted blood transfusion scheme.
According to a specific implementation manner of the embodiment of the present disclosure, before the step of training the XGBOOST algorithm on the sample data set and establishing the prediction model, the method further includes:
extracting initial medical record data of a preset number of sample persons from a medical record database, wherein the sample persons are persons who perform liver transplantation;
and screening out interference data in the initial medical record data according to a preset index to form the sample data set.
According to a specific implementation manner of the embodiment of the disclosure, the step of training the XGBOOST algorithm on the sample data set and establishing the prediction model includes:
analyzing the relevance of each data in the sample data set and the blood transfusion volume and generating a decision tree;
and establishing the prediction model according to the decision tree.
According to a specific implementation manner of the embodiment of the present disclosure, before the step of collecting the key information data set of the target person, the method further includes:
acquiring physical examination information of the target person;
and extracting the characteristics corresponding to preset keywords in the physical examination information and converting the characteristics into corresponding formats to form the key information data set.
According to a specific implementation manner of the embodiment of the present disclosure, after the step of inputting the key information dataset into the prediction model to obtain a predicted blood transfusion scheme, the method further includes:
generating a treatment plan based on the association of the predicted transfusion plan with the key information dataset.
In a second aspect, embodiments of the present disclosure provide a liver transplantation process blood transfusion prediction system, including:
the training module is used for training the XGBOOST algorithm to the sample data set and establishing a prediction model;
the system comprises an acquisition module, a data processing module and a data processing module, wherein the acquisition module is used for acquiring a key information data set of a target person, and the key information data set comprises age, portal hypertension, activated partial thrombin time, direct bilirubin content, globulin content, hemoglobin content, glutamic-oxalacetic transaminase content, glutamic-pyruvic transaminase content and liver diagnosis information;
and the prediction module is used for inputting the key information data set into the prediction model to obtain a predicted blood transfusion scheme.
In a third aspect, an embodiment of the present disclosure further provides an electronic device, where the electronic device includes:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of liver transplantation procedure transfusion prediction in the first aspect or any implementation form of the first aspect.
In a fourth aspect, the disclosed embodiments also provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the liver transplantation procedure transfusion prediction method in the first aspect or any implementation manner of the first aspect.
In a fifth aspect, the disclosed embodiments also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to perform the liver transplantation procedure transfusion prediction method in the foregoing first aspect or any implementation manner of the first aspect.
The liver transplantation process blood transfusion prediction scheme in the embodiment of the disclosure comprises the following steps: training the XGBOOST algorithm on the sample data set, and establishing a prediction model; collecting a key information data set of a target person, wherein the key information data set comprises age, portal hypertension, activated partial thrombin time, direct bilirubin content, globulin content, hemoglobin content, glutamic-oxalacetic transaminase content, glutamic-pyruvic transaminase content and liver diagnosis information; and inputting the key information data set into the prediction model to obtain a predicted blood transfusion scheme.
The beneficial effects of the embodiment of the disclosure are: according to the scheme, the machine learning algorithm is utilized to learn the sample data set, the prediction model is established, then the collected key information data set of the target person is input into the prediction model, and the prediction blood transfusion scheme is obtained, so that the prediction efficiency and accuracy are improved, and the safety in the operation process is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for predicting blood transfusion during liver transplantation according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram illustrating association between a key information data set and blood transfusion related to a method for predicting blood transfusion in a liver transplantation process according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a liver transplantation process blood transfusion prediction system according to an embodiment of the present disclosure;
fig. 4 is a schematic view of an electronic device provided in an embodiment of the present disclosure.
Detailed Description
The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present disclosure, and the drawings only show the components related to the present disclosure rather than the number, shape and size of the components in actual implementation, and the type, amount and ratio of the components in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
Liver transplantation is currently an effective method of treating end-stage liver disease, and long and complicated surgical procedures may result in perioperative bleeding. Most patients require concentrated red blood cell transfusions during or after surgery, which, while increasing the oxygen supply to the patient and improving tissue perfusion, is associated with a number of side effects, such as increased risk of deep vein thrombosis, increased fibrosis, cancer recurrence and increased mortality, which adversely affect the prognosis of the patient. In case of massive bleeding, a massive blood transfusion scheme is mostly adopted for volume treatment in clinic, but the blood products are transfused in a large amount, and adverse reactions such as bleeding, blood pressure reduction, slow heart rate, ventricular fibrillation and the like are easy to occur. Most of the research on predicting liver transplantation red blood cell infusion is based on traditional linear models and logistic regression, the interaction among a plurality of variables cannot be understood, and the prediction efficiency and accuracy are poor.
The embodiment of the disclosure provides a blood transfusion prediction method in a liver transplantation process, which can be applied to a blood transfusion prediction process in a medical operation scene.
Referring to fig. 1, a schematic flow chart of a method for predicting blood transfusion in a liver transplantation process according to an embodiment of the present disclosure is provided. As shown in fig. 1, the method mainly comprises the following steps:
s101, training the XGBOOST algorithm on the sample data set, and establishing a prediction model;
in specific implementation, in consideration of the fact that the probability that a target person may need blood transfusion in the liver transplantation process and a specific time point need to be predicted, the sample data set can be collected first, and then the XGBOOST algorithm can be adopted to learn data with large blood transfusion prediction relevance in the sample data set, so that the prediction model is established. Of course, other machine learning algorithms may be used to learn the sample data set as needed to establish the prediction model, which is not described herein again.
S102, collecting a key information data set of a target person, wherein the key information data set comprises age, portal hypertension, activated partial thrombin time, direct bilirubin content, globulin content, hemoglobin content, glutamic-oxalacetic transaminase content, glutamic-pyruvic transaminase content and liver diagnosis information;
in specific implementation, considering that a blood transfusion scheme of the target person in the operation process needs to be predicted, the target person has different conditions, the bleeding probability or the bleeding condition in the operation process is different, the demographic characteristics such as age and the like, the disease diagnosis characteristics such as liver diagnosis information and the like, the clinical characteristics such as portal hypertension and the like, and the experimental indexes such as activated partial thrombin time, direct bilirubin content, globulin content, hemoglobin content, glutamic-oxalacetic transaminase content, glutamic-pyruvic transaminase content and the like are all key factors influencing the bleeding amount, preoperative data of the target person can be collected to form the key information data set.
And S103, inputting the key information data set into the prediction model to obtain a predicted blood transfusion scheme.
After the key information data set is collected, the key information data set can be input into the prediction model, the prediction model calculates according to the values of various types of data in the key information data set, the prediction blood transfusion scheme is output, and the prediction blood transfusion scheme can be obtained according to the prediction blood transfusion scheme
According to the liver transplantation process blood transfusion prediction method provided by the embodiment, the machine learning algorithm is utilized to learn the sample data set, the prediction model is established, the collected key information data set of the target person is input into the prediction model, the prediction blood transfusion scheme is obtained, the prediction efficiency and accuracy are improved, and the safety in the operation process is improved.
On the basis of the foregoing embodiment, in step S101, before training the XGBOOST algorithm on the sample data set and building the prediction model, the method further includes:
extracting initial medical record data of a preset number of sample persons from a medical record database, wherein the sample persons are persons who perform liver transplantation;
and screening out interference data in the initial medical record data according to a preset index to form the sample data set.
In specific implementation, the preset index may be a key factor affecting bleeding amount in the liver transplantation operation process, and may extract initial medical record data of a preset number of sample persons from medical record databases of one or more hospitals, where the sample persons are persons performing liver transplantation, and for example, initial medical record reports of 500 persons performing liver transplantation may be extracted from medical record databases of hospitals in an area a, an area B, and an area C, respectively. Considering that a large amount of redundant and irrelevant information may exist in the initial medical record report, after all the initial medical record reports are aggregated, interference data in the initial medical record data can be screened out according to the preset index, and the sample data set is formed. Considering that the medical record data of different regions or different hospitals may have different formats when stored, the same type of data in the sample data set needs to be converted into a unified format, for example, the creatinine value 1mg/dL is equal to 88.4 μmol/L, hepatocellular carcinoma and primary liver cancer are combined into liver malignant tumor, and the diagnosis variable is converted into an ordinal variable, such as: 1 ═ cirrhosis, 2 ═ hepatomalignant tumors, 3 ═ liver failure, 4 ═ alcoholic liver disease, 5 ═ viral hepatitis, 6 ═ hepatic space occupying lesion, 7 ═ biliary liver disease, 8 ═ others.
Further, in step S101, training the XGBOOST algorithm on the sample data set, and establishing a prediction model, includes:
analyzing the relevance of each data in the sample data set and the blood transfusion volume and generating a decision tree;
and establishing the prediction model according to the decision tree.
In specific implementation, when the XGBOOST algorithm is used to learn the sample data set, the correlation between various types of data in the sample data set and the correlation between each type of data in the sample data set and the blood transfusion volume may be analyzed, a split point is set to generate a decision tree, and then the prediction model is established according to the decision tree.
On the basis of the foregoing embodiment, before the step S102 of collecting the key information data set of the target person, the method further includes:
acquiring physical examination information of the target person;
and extracting the characteristics corresponding to preset keywords in the physical examination information and converting the characteristics into corresponding formats to form the key information data set.
In specific implementation, considering that the target person performs a relevant examination before performing a liver transplantation operation, after the target person performs the examination, obtaining physical examination information of the target person, then identifying text information in the physical examination information, then extracting features corresponding to the preset keywords, and then converting the extracted features into a format corresponding to the prediction model to form the key information data set.
Optionally, in step S103, after the key information data set is input into the prediction model to obtain a predicted blood transfusion scheme, the method further includes:
generating a treatment plan based on the association of the predicted transfusion plan with the key information dataset.
In particular, the SHAP package can be used to explain the importance of each key information in the predicted transfusion scheme output by the prediction model to the mass transfusion of the patient, as shown in FIG. 2, and then the treatment scheme is generated, and the clinician can improve the index of the patient according to the treatment scheme to make the corresponding treatment.
In correspondence with the above method embodiment, referring to fig. 3, the disclosed embodiment also provides a liver transplantation process blood transfusion prediction system 30, including:
the training module 301 is used for training the XGBOOST algorithm to the sample data set and establishing a prediction model;
an acquisition module 302, configured to acquire a key information dataset of a target person, where the key information dataset includes age, portal hypertension, activated partial thrombin time, direct bilirubin content, globulin content, hemoglobin content, glutamic-oxalacetic transaminase content, glutamic-pyruvic transaminase content, and liver diagnosis information;
and the prediction module 303 is configured to input the key information data set into the prediction model to obtain a predicted blood transfusion scheme.
The apparatus shown in fig. 3 may correspondingly execute the content in the above method embodiment, and details of the part not described in detail in this embodiment refer to the content described in the above method embodiment, which is not described again here.
Referring to fig. 4, an embodiment of the present disclosure also provides an electronic device 40, including: at least one processor and a memory communicatively coupled to the at least one processor. Wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of liver transplantation procedure transfusion prediction in the preceding method embodiments.
The disclosed embodiments also provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the liver transplantation procedure transfusion prediction method in the aforementioned method embodiments.
The disclosed embodiments also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to perform the liver transplantation procedure transfusion prediction method in the aforementioned method embodiments.
Referring now to FIG. 4, a block diagram of an electronic device 40 suitable for use in implementing embodiments of the present disclosure is shown. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 4, the electronic device 40 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 401 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage means 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the electronic apparatus 40 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Generally, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touch pad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, tape, hard disk, etc.; and a communication device 409. The communication device 409 may allow the electronic device 40 to communicate wirelessly or by wire with other devices to exchange data. While the figures illustrate an electronic device 40 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication device 409, or from the storage device 408, or from the ROM 402. The computer program performs the above-described functions defined in the methods of the embodiments of the present disclosure when executed by the processing device 401.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to perform the steps associated with the method embodiments.
Alternatively, the computer readable medium carries one or more programs which, when executed by the electronic device, enable the electronic device to perform the steps associated with the method embodiments.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.
The above description is only for the specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present disclosure should be covered within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims (8)

1. A liver transplantation process blood transfusion prediction method is characterized by comprising the following steps:
training the XGBOOST algorithm on the sample data set, and establishing a prediction model;
collecting a key information data set of a target person, wherein the key information data set comprises age, portal hypertension, activated partial thrombin time, direct bilirubin content, globulin content, hemoglobin content, glutamic-oxalacetic transaminase content, glutamic-pyruvic transaminase content and liver diagnosis information;
and inputting the key information data set into the prediction model to obtain a predicted blood transfusion scheme.
2. The method of claim 1, wherein before training the sample data set to the XGBOOST algorithm and building the prediction model, the method further comprises:
extracting initial medical record data of a preset number of sample persons from a medical record database, wherein the sample persons are persons who perform liver transplantation;
and screening out interference data in the initial medical record data according to a preset index to form the sample data set.
3. The method of claim 2, wherein training the sample data set to the XGBOOST algorithm, the step of building a prediction model comprises:
analyzing the relevance of each data in the sample data set and the blood transfusion volume and generating a decision tree;
and establishing the prediction model according to the decision tree.
4. The method of claim 1, wherein the step of collecting a key information dataset of the target person is preceded by the method further comprising:
acquiring physical examination information of the target person;
and extracting the characteristics corresponding to preset keywords in the physical examination information and converting the characteristics into corresponding formats to form the key information data set.
5. The method of claim 1, wherein after the step of inputting the key information dataset into the predictive model to arrive at a predicted transfusion protocol, the method further comprises:
generating a treatment plan based on the association of the predicted transfusion plan with the key information dataset.
6. A liver transplantation process blood transfusion prediction system, comprising:
the training module is used for training the XGBOOST algorithm to the sample data set and establishing a prediction model;
the system comprises an acquisition module, a data processing module and a data processing module, wherein the acquisition module is used for acquiring a key information data set of a target person, and the key information data set comprises age, portal hypertension, activated partial thrombin time, direct bilirubin content, globulin content, hemoglobin content, glutamic-oxalacetic transaminase content, glutamic-pyruvic transaminase content and liver diagnosis information;
and the prediction module is used for inputting the key information data set into the prediction model to obtain a predicted blood transfusion scheme.
7. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the liver transplantation procedure transfusion prediction method of any one of the preceding claims 1-5.
8. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the liver transplantation procedure transfusion prediction method of any one of the preceding claims 1-5.
CN202110982124.4A 2021-08-25 2021-08-25 Liver transplantation process blood transfusion prediction method, system, equipment and medium Pending CN113611391A (en)

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