CN115376649A - Method and device for predicting dose of intrathecal opioid analgesic - Google Patents
Method and device for predicting dose of intrathecal opioid analgesic Download PDFInfo
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Abstract
The invention relates to a dose prediction method and a device for intrathecal opioid analgesics, the method comprises the steps of obtaining historical data through a hospital stay record, and generating a training sample according to the historical data; determining factor variables, constructing a multiple linear regression analysis equation based on the factor variables, inputting the training samples into the multiple linear regression analysis equation for training, and obtaining an intrathecal dose prediction model; and inputting the variable information of the patient to be detected into the intrathecal dose prediction model to obtain a prediction result. The method predicts the required intrathecal analgesic dose of the patient based on the clinical characteristics of the patient and the gene detection result, uses individual dose for each patient, can approach the actually required dose of the patient to the maximum extent, and reduces the time for reaching the actually required intrathecal dose of the patient.
Description
Technical Field
The invention belongs to the technical field of intelligent models, and particularly relates to a method and a device for predicting dose of intrathecal opioid analgesic.
Background
Intrathecal analgesia is that a catheter is implanted into cerebrospinal fluid by an operation to bypass the blood brain barrier, and a small dose of opioid analgesics such as morphine and the like is directly injected into the cerebrospinal fluid to play a role of powerful analgesia. The traditional Chinese medicine composition is suitable for severe pain patients with poor opioid analgesic effect or intolerance to adverse reaction after systemic administration, is one of novel treatment means for cancer pain, and has the characteristics of small administration dosage, strong analgesic effect, light adverse reaction and the like.
Intrathecal analgesia has the advantages of strong analgesic effect and the like, but has the defects of difficult determination of administration dosage and the like. In the related art, currently, the clinical calculation of intrathecal analgesic dosage adopts 1/300 of the current oral dosage of patients, and the dosage is increased or decreased according to the treatment response of the patients. However, clinical practice has shown that the ratio of intrathecal to oral morphine doses varies widely between patients, randomly distributed around 1/300. Therefore, after intrathecal administration of the dose calculated by the ratio, the patient needs a long time to increase or decrease the dose, which easily causes serious consequences such as insufficient analgesia and drug poisoning, and the current situation that the dose of intrathecal analgesia is difficult to determine also causes that the average hospitalization day (13 days) of the intrathecal analgesia patient is far higher than the total average hospitalization day (6.7 days) of the pain department.
Disclosure of Invention
In view of the above, the present invention aims to overcome the defects of the prior art, and provides a method and a device for predicting the dose of intrathecal opioid analgesics, so as to solve the problem in the prior art that the dose of intrathecal analgesics is difficult to determine.
In order to achieve the purpose, the invention adopts the following technical scheme: a dose prediction method for intrathecal opioid analgesic comprising:
acquiring historical data through a hospitalization record, and generating a training sample according to the historical data;
determining factor variables, constructing a multiple linear regression analysis equation based on the factor variables, inputting the training samples into the multiple linear regression analysis equation for training, and obtaining an intrathecal dose prediction model;
and inputting the variable information of the patient to be detected into the intrathecal dose prediction model to obtain a prediction result.
Further, the factor variables include:
basic information of human body, pain characteristics, using information of analgesic before intrathecal analgesia, biochemical indexes, using information of intrathecal analgesia and gene SNP locus genotype influencing dosage of intrathecal analgesia.
Further, the basic information of the human body comprises sex, age, body mass index and Karschner function state score;
the pain characteristics include pain duration, pain score before intrathecal analgesia, and primary tumor system;
the using information of the intrathecal analgesic drug comprises an oral morphine equivalent dose and an oral morphine equivalent dose which is converted into intrathecal dose;
the biochemical indicators comprise liver function Child-pugh score and creatinine clearance rate;
the intrathecal analgesic drug usage information comprises the location of the intrathecal catheter, the type of intrathecal opioid analgesic and the dose of intrathecal analgesic drug;
the gene SNP locus genotypes influencing the intrathecal analgesic drug dosage comprise ABCB1rs1045642, rs1128503, rs2032582 and rs9282564.
Further, the method also comprises the following steps:
and preprocessing the construction factor data.
Further, the preprocessing the construction factor data includes:
converting a dose of the opioid analgesic prior to intrathecal analgesia in the patient to an oral morphine equivalent dose;
setting a preset part of the factor variables as dummy variables;
wherein the predetermined set of part of the factor variables comprises gender, primary tumor system, oral morphine equivalent dose, liver function Child-trough score, creatinine clearance, location of intrathecal catheter, type of intrathecal opioid analgesic, ABCB1rs1045642, rs1128503, rs2032582, and rs9282564.
Further, the constructing a multiple linear regression analysis equation based on the factor variables includes:
inputting factor variables by using SPSS statistical analysis software, taking the dose of the intrathecal analgesic drug as a dependent variable and taking other factor variables except the intrathecal analgesic drug as independent variables, and performing multivariate linear stepwise regression analysis on the correlation between the independent variables and the dependent variables by using a regression method.
Furthermore, the gene influencing the intrathecal analgesic drug dosage is SNP locus genotype and is obtained by gene sequencing.
Further, the method also comprises the following steps:
using mean absolute error, root mean square error and R 2 The score algorithm evaluates the predictive power of the predictive model.
Embodiments of the present application provide a dose prediction device for intrathecal opioid analgesics comprising:
the acquisition module is used for acquiring historical data through hospitalization records and generating training samples according to the historical data;
the training module is used for determining factor variables, constructing a multiple linear regression analysis equation based on the factor variables, inputting the training samples into the multiple linear regression analysis equation for training, and obtaining an intrathecal dose prediction model;
and the prediction module is used for inputting the variable information of the patient to be tested into the intrathecal dose prediction model to obtain a prediction result.
An embodiment of the present application provides a computer device, including: a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of any of the methods for dose prediction of intrathecal opioid analgesics described above.
By adopting the technical scheme, the invention can achieve the following beneficial effects:
the invention provides a dose prediction method and a device for intrathecal opioid analgesics, which predict the required intrathecal analgesic dose of a patient based on clinical characteristics and gene detection results of the patient, use individualized dose for each patient, can approach the actually required dose of the patient to the maximum extent and reduce the time for reaching the actually required intrathecal dose of the patient. In addition, the required dose of each patient is calculated through the intrathecal dose prediction model, the dose determination efficiency can be improved, and insufficient analgesia and serious adverse reactions are avoided.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic representation of the steps of the dose prediction process for intrathecal opioid analgesics of the invention;
FIG. 2 is a schematic diagram of the configuration of a dose prediction device for intrathecal opioid analgesics of the invention;
fig. 3 is a schematic diagram of the hardware configuration of the implementation environment of the method for predicting the dose of intrathecal opioid analgesics of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without making any creative effort, shall fall within the protection scope of the present invention.
The traditional method of applying the same dosage strategy to all patients may lead to the patients who need higher dosage to spend a lot of time increasing dosage, suffer from the problem of poor pain control caused by insufficient analgesic dosage during the period, and also cause the patients who need lower dosage to have serious adverse reactions such as nausea and vomiting, excessive sedation, respiratory depression and the like after using normal dosage, thereby affecting the treatment outcome of the patients. Therefore, there is a need for a method that can rapidly determine the required dose for a patient to improve the dose determination efficiency and avoid insufficient analgesia and severe adverse reactions.
The following describes a specific dose prediction method and device for intrathecal opioid analgesics provided in the examples of the present application with reference to the drawings.
As shown in fig. 1, the dose prediction method for intrathecal opioid analgesics provided in the examples of the present application comprises:
s101, acquiring historical data through a hospital stay record, and generating a training sample according to the historical data;
patients treated with intrathecal analgesia by morphine in the pain department of hospitals were prospectively included in this application for a total of 68 patients. It is understood that the number of patients may be determined or changed according to actual conditions, and the application is not limited thereto.
S102, determining factor variables, constructing a multiple linear regression analysis equation based on the factor variables, inputting the training samples into the multiple linear regression analysis equation for training, and obtaining a intrathecal dose prediction model;
s103, inputting the variable information of the patient to be detected into the intrathecal dose prediction model to obtain a prediction result.
The working principle of the dose prediction method for intrathecal opioid analgesics is: firstly, acquiring historical data through a hospitalization record, and generating a training sample according to the historical data; determining factor variables for constructing a multiple linear regression analysis equation, constructing the multiple linear regression analysis equation based on the factor variables, inputting a training sample into the multiple linear regression analysis equation for training, and obtaining an intrathecal dose prediction model; and finally, inputting the variable information of the patient to be detected into the intrathecal dose prediction model to obtain a prediction result.
According to the intrathecal dose prediction method, a intrathecal dose prediction model for predicting the intrathecal opioid analgesic dose required by a patient is established, a fast, effective and safe dose scheme can be provided for the intrathecal analgesic patient, and compared with a traditional method for carrying out dose titration according to the treatment response of the patient, the speed for determining the optimal intrathecal dose can be accelerated.
In some embodiments, the factor variables include:
basic information of a human body, pain characteristics, usage information of an analgesic before intrathecal analgesia, biochemical indexes, usage information of the intrathecal analgesia, and gene SNP locus genotype influencing dosage of the intrathecal analgesia.
The basic information of the human body comprises gender (gender), age (year), body Mass Index (BMI) and Kaplan function state score (KPS);
the pain characteristics include duration of pain (pain), pain score before intrathecal analgesia, and primary tumor system (organ);
the intrathecal pre-analgesic drug usage information comprising an Oral Morphine Equivalent Dose (OMED) and an oral morphine equivalent dose (transdose) to be converted to an intrathecal dose;
the biochemical indicators comprise liver function Child-pugh score (childpugh) and creatinine clearance rate (Ccr);
the intrathecal analgesic drug usage information includes the location of the intrathecal catheter (position), the type of intrathecal opioid analgesic (ITdrug) and the dose of intrathecal analgesic (ITdose);
the gene SNP locus genotypes influencing the intrathecal analgesic drug dosage comprise ABCB1rs1045642, rs1128503, rs2032582 and rs9282564.
It is understood that the basic information of the human body, the pain characteristics, the usage information of the analgesic before intrathecal analgesia, the biochemical indexes, the usage information of the intrathecal analgesia, and the genotype of the SNP locus that affects the dosage of the intrathecal analgesia may also include other factors, which are not limited herein.
In some embodiments, further comprising:
and preprocessing the construction factor data.
As a preferred embodiment, the preprocessing the construction factor data includes:
converting a dose of a patient's opioid analgesic prior to intrathecal analgesia to an oral morphine equivalent dose;
setting a preset part of the factor variables as dummy variables;
wherein the predetermined set of part of the factor variables comprises gender, primary tumor system, oral morphine equivalent dose, liver function Child-trough score, creatinine clearance, location of intrathecal catheter, type of intrathecal opioid analgesic, ABCB1rs1045642, rs1128503, rs2032582, and rs9282564.
Specifically, the dose of opioid analgesic prior to intrathecal analgesia in the patient was converted to an Oral Morphine Equivalent Dose (OMED) using conversion ratios as shown in Table 1.
TABLE 1 dosage conversion ratio of opioids
| Medicine | Oral administration (mg) | Parenteral systemic administration (mg) |
| Morphine (morphine) | 30 | 10 |
| Hydromorphone | - | 2 |
| Oxycodone | 15 | - |
| Fentanyl | - | 15ug/h (transdermal absorption) |
Some of the factor variables were set as dummy variables including gender (gender), primary tumor system (organ), oral Morphine Equivalent Dose (OMED), child-trough score (childtrough), creatinine clearance (Ccr), location of intrathecal catheter (position), type of intrathecal opioid analgesic (ITdrug), ABCB1rs1045642, rs1128503, rs2032582, rs9282564 site genotype, etc. The specific variable assignment method is shown in table 2.
Table 2 dummy variable assignment form
In some embodiments, the constructing a multiple linear regression analysis equation based on the factor variables includes:
inputting factor variables by using SPSS statistical analysis software, taking the dose of the intrathecal analgesic drug as a dependent variable and taking other factor variables except the intrathecal analgesic drug as independent variables, and performing multivariate linear stepwise regression analysis on the correlation between the independent variables and the dependent variables by using a regression method.
Specifically, the intrathecal dose prediction model is a multiple linear regression equation in the form of y = kx 1 +kx 2 ...kx n + b, wherein x is an independent variable, i.e. sex, age, body mass index, karya function status score, pain duration, pain score before intrathecal analgesia, primary tumor system, oral morphine equivalent dose to be converted into intrathecal dose, liver function Child-trough score, creatinine clearance rate, location of intrathecal catheter, type of intrathecal opioid analgesic, a plurality of ABCB1rs1045642, rs1128503, rs2032582 and rs9282564, and the dose of intrathecal analgesic is a dependent variable, i.e. y; and selecting data of a plurality of independent variables to perform multiple linear regression analysis to obtain an optimal multiple regression equation, namely a intrathecal dose prediction model.
The results of the multiple linear regression analysis showed that factors such as the patient's age (year), BMI, duration of pain (pain), oral morphine equivalent dose (transdose) to be converted to intrathecal dose, karya function status score (KPS) and oral morphine equivalent dose (ome) ≧ 600mg (ome = 3) correlated with intrathecal opioid analgesic dose amounts as shown in table 3. An intrathecal dose prediction model was established based on the multiple linear regression analysis results, i.e., intrathecal morphine dose (mg) =19.752-0.134 age-0.305 bmi +1.141 pain duration (year) +0.005 to convert to intrathecal dose of oral morphine equivalent-0.0378 kps value-4.035 oral morphine equivalent greater than or equal to 600mg.
TABLE 3 multiple Linear regression analysis results
As shown in table 4, the analysis results show that the intrathecal dose prediction model has significant statistical significance.
TABLE 4 statistical analysis results of the prediction model
In some embodiments, the gene affecting the intrathecal analgesic drug dose is obtained by gene sequencing for SNP locus genotype. The primer sequences of the detection sites are as follows:
in some embodiments, further comprising:
using mean absolute error, root mean square error and R 2 The score algorithm evaluates the predictive power of the predictive model.
The present application can also verify the performance of intrathecal dose prediction models, specifically using Mean Absolute Error (MAE), root Mean Square Error (RMSE), and R 2 The score evaluates the predictive power of the predictive model. MAE, RMSE and R of this model 2 The scores were 2.24,3.22 and 64.9%, respectively, indicating that the model has better predictive performance.
As shown in fig. 2, embodiments of the present application provide a dose prediction device for intrathecal opioid analgesics comprising:
an obtaining module 201, configured to obtain historical data through a hospital stay record, and generate a training sample according to the historical data;
the training module 202 is configured to determine factor variables, construct a multiple linear regression analysis equation based on the factor variables, and input the training samples into the multiple linear regression analysis equation for training to obtain an intrathecal dose prediction model;
and the prediction module 203 is used for inputting the variable information of the patient to be detected into the intrathecal dose prediction model to obtain a prediction result.
The working principle of the dose prediction device for intrathecal opioid analgesics provided by the embodiment of the application is that the obtaining module 201 obtains historical data through hospitalization records, and generates training samples according to the historical data; the training module 202 determines factor variables, constructs a multiple linear regression analysis equation based on the factor variables, and inputs the training samples into the multiple linear regression analysis equation for training to obtain an intrathecal dose prediction model; the prediction module 203 inputs the variable information of the patient to be measured into the intrathecal dose prediction model to obtain a prediction result.
The present application provides a computer device comprising: a memory, which may include volatile memory in a computer readable medium, random Access Memory (RAM), and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The computer device stores an operating system, and the memory is an example of a computer-readable medium. The computer program, when executed by the processor, causes the processor to perform a dose prediction method for intrathecal opioid analgesics, the configuration shown in figure 3 being a block diagram of only a portion of the configuration associated with the protocol of the present application and not constituting a limitation on the computer apparatus to which the protocol of the present application may be applied, a particular computer apparatus may include more or less components than shown in the figures, or combine certain components, or have a different arrangement of components.
In one embodiment, the dose prediction method for intrathecal opioid analgesics provided herein may be embodied in the form of a computer program that may be run on a computer device as shown in fig. 3.
In some embodiments, the computer program, when executed by the processor, causes the processor to perform the steps of: acquiring historical data through a hospitalization record, and generating a training sample according to the historical data; determining factor variables, constructing a multiple linear regression analysis equation based on the factor variables, inputting the training samples into the multiple linear regression analysis equation for training, and obtaining an intrathecal dose prediction model; and inputting the variable information of the patient to be detected into the intrathecal dose prediction model to obtain a prediction result.
The present application also provides a computer storage medium, examples of which include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassette tape storage or other magnetic storage devices, or any other non-transmission medium, that can be used to store information that can be accessed by a computing device.
In some embodiments, the present invention further provides a computer-readable storage medium storing a computer program, which when executed by a processor, acquires historical data from a hospital record, and generates a training sample according to the historical data; determining factor variables, constructing a multiple linear regression analysis equation based on the factor variables, inputting the training samples into the multiple linear regression analysis equation for training, and obtaining an intrathecal dose prediction model; and inputting the variable information of the patient to be detected into the intrathecal dose prediction model to obtain a prediction result.
In summary, the present invention provides a dose prediction method and device for intrathecal opioid analgesics, which predicts the required intrathecal analgesic dose for the patient based on the clinical characteristics of the patient and the genetic test results, uses individualized doses for each patient, can approach the actually required dose for the patient to the maximum extent, and reduces the time to reach the actually required intrathecal dose for the patient. In addition, the required dose of each patient is calculated through the intrathecal dose prediction model, the dose determination efficiency can be improved, and insufficient analgesia and serious adverse reactions are avoided.
It is to be understood that the embodiments of the method provided above correspond to the embodiments of the apparatus described above, and the corresponding specific contents may be referred to each other, which is not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A method for dose prediction for intrathecal opioid analgesic comprising:
acquiring historical data through a hospitalization record, and generating a training sample according to the historical data;
determining factor variables, constructing a multiple linear regression analysis equation based on the factor variables, inputting the training samples into the multiple linear regression analysis equation for training, and obtaining an intrathecal dose prediction model;
and inputting the variable information of the patient to be detected into the intrathecal dose prediction model to obtain a prediction result.
2. The method of claim 1, wherein the factor variables comprise:
basic information of human body, pain characteristics, using information of analgesic before intrathecal analgesia, biochemical indexes, using information of intrathecal analgesia and gene SNP locus genotype influencing dosage of intrathecal analgesia.
3. The method of claim 2,
the human body basic information comprises gender, age, body mass index and Karschner function state score;
the pain characteristics include pain duration, pain score before intrathecal analgesia, and primary tumor system;
the using information of the intrathecal analgesic drug comprises an oral morphine equivalent dose and an oral morphine equivalent dose which is converted into intrathecal dose;
the biochemical indexes comprise liver function Child-pugh score and creatinine clearance rate;
the intrathecal analgesic drug usage information comprises the location of the intrathecal catheter, the type of intrathecal opioid analgesic and the dose of intrathecal analgesic drug;
the gene SNP locus genotypes influencing the intrathecal analgesic drug dosage comprise ABCB1rs1045642, rs1128503, rs2032582 and rs9282564.
4. The method of claim 3, further comprising:
and preprocessing the construction factor data.
5. The method of claim 4, wherein preprocessing the construction factor data comprises:
converting a dose of the opioid analgesic prior to intrathecal analgesia in the patient to an oral morphine equivalent dose;
setting a preset part of the factor variables as dummy variables;
wherein the predetermined set of part of the factor variables comprises gender, primary tumor system, oral morphine equivalent dose, liver function Child-trough score, creatinine clearance rate, location of intrathecal catheter, type of intrathecal opioid analgesic, ABCB1rs1045642, rs1128503, rs2032582, and rs9282564.
6. The method of claim 3, wherein the constructing a multiple linear regression analysis equation based on the factor variables comprises:
inputting factor variables by using SPSS statistical analysis software, taking the dose of the intrathecal analgesic drug as a dependent variable and taking other factor variables except the intrathecal analgesic drug as independent variables, and performing multivariate linear stepwise regression analysis on the correlation between the independent variables and the dependent variables by using a regression method.
7. The method of claim 2,
the gene influencing the intrathecal analgesic drug dosage is SNP locus genotype which is obtained by gene sequencing.
8. The method of claim 1, further comprising:
using mean absolute error, root mean square error and R 2 The score algorithm evaluates the predictive power of the predictive model.
9. A dose prediction device for an intrathecal opioid analgesic comprising:
the acquisition module is used for acquiring historical data through hospitalization records and generating training samples according to the historical data;
the training module is used for determining factor variables, constructing a multiple linear regression analysis equation based on the factor variables, inputting the training samples into the multiple linear regression analysis equation for training, and obtaining an intrathecal dose prediction model;
and the prediction module is used for inputting the variable information of the patient to be tested into the intrathecal dose prediction model to obtain a prediction result.
10. A computer device, comprising: a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the method for dose prediction for intrathecal opioid analgesic as claimed in any of claims 1 to 8.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105142517A (en) * | 2013-04-29 | 2015-12-09 | 弗劳恩霍夫应用研究促进协会 | Non-invasive method for prediction of opioid-analgesia and opioid-blood-concentrations |
| CN109087692A (en) * | 2018-10-24 | 2018-12-25 | 上海交通大学医学院附属瑞金医院北院 | A kind of insulin dose based on growth hormone irritant test determines method and device |
| CN110010252A (en) * | 2019-04-01 | 2019-07-12 | 上海交通大学医学院附属新华医院 | Warfarin dosage prediction technique and device |
| WO2020118790A1 (en) * | 2018-12-12 | 2020-06-18 | 北京大学第三医院 | System for assessing ovarian reserve function of subject, and method |
| CN111312341A (en) * | 2020-01-17 | 2020-06-19 | 中南大学湘雅三医院 | Warfarin dose prediction method and prediction device |
| CN111613289A (en) * | 2020-05-07 | 2020-09-01 | 浙江大学医学院附属第一医院 | Individualized drug dose prediction method, individualized drug dose prediction device, electronic equipment and storage medium |
| WO2020216307A1 (en) * | 2019-04-23 | 2020-10-29 | 上海联影医疗科技有限公司 | Method, system and device for acquiring radiological image, and storage medium |
| CN112786145A (en) * | 2021-03-04 | 2021-05-11 | 华中科技大学同济医学院附属协和医院 | Accurate prediction method for tacrolimus dosage of organ transplantation patient |
| CN113035369A (en) * | 2021-03-10 | 2021-06-25 | 浙江大学 | Construction method of kidney transplantation anti-infective drug dosage prediction model |
| CN113270203A (en) * | 2021-04-20 | 2021-08-17 | 郑州大学第一附属医院 | Drug dose prediction method, device, electronic device and storage medium |
| CN113610845A (en) * | 2021-09-09 | 2021-11-05 | 汕头大学医学院附属肿瘤医院 | Tumor local control prediction model construction method, prediction method and electronic equipment |
| CN113889221A (en) * | 2021-12-08 | 2022-01-04 | 首都医科大学宣武医院 | System for accurately selecting treatment dosage of tacrolimus of myasthenia gravis patient and application of system |
| CN114255883A (en) * | 2021-10-25 | 2022-03-29 | 郑州市中心医院 | Voriconazole maintenance dose prediction mathematical model and construction method and application thereof |
-
2022
- 2022-09-29 CN CN202211205118.9A patent/CN115376649B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105142517A (en) * | 2013-04-29 | 2015-12-09 | 弗劳恩霍夫应用研究促进协会 | Non-invasive method for prediction of opioid-analgesia and opioid-blood-concentrations |
| CN109087692A (en) * | 2018-10-24 | 2018-12-25 | 上海交通大学医学院附属瑞金医院北院 | A kind of insulin dose based on growth hormone irritant test determines method and device |
| WO2020118790A1 (en) * | 2018-12-12 | 2020-06-18 | 北京大学第三医院 | System for assessing ovarian reserve function of subject, and method |
| CN110010252A (en) * | 2019-04-01 | 2019-07-12 | 上海交通大学医学院附属新华医院 | Warfarin dosage prediction technique and device |
| WO2020216307A1 (en) * | 2019-04-23 | 2020-10-29 | 上海联影医疗科技有限公司 | Method, system and device for acquiring radiological image, and storage medium |
| CN111312341A (en) * | 2020-01-17 | 2020-06-19 | 中南大学湘雅三医院 | Warfarin dose prediction method and prediction device |
| CN111613289A (en) * | 2020-05-07 | 2020-09-01 | 浙江大学医学院附属第一医院 | Individualized drug dose prediction method, individualized drug dose prediction device, electronic equipment and storage medium |
| CN112786145A (en) * | 2021-03-04 | 2021-05-11 | 华中科技大学同济医学院附属协和医院 | Accurate prediction method for tacrolimus dosage of organ transplantation patient |
| CN113035369A (en) * | 2021-03-10 | 2021-06-25 | 浙江大学 | Construction method of kidney transplantation anti-infective drug dosage prediction model |
| CN113270203A (en) * | 2021-04-20 | 2021-08-17 | 郑州大学第一附属医院 | Drug dose prediction method, device, electronic device and storage medium |
| CN113610845A (en) * | 2021-09-09 | 2021-11-05 | 汕头大学医学院附属肿瘤医院 | Tumor local control prediction model construction method, prediction method and electronic equipment |
| CN114255883A (en) * | 2021-10-25 | 2022-03-29 | 郑州市中心医院 | Voriconazole maintenance dose prediction mathematical model and construction method and application thereof |
| CN113889221A (en) * | 2021-12-08 | 2022-01-04 | 首都医科大学宣武医院 | System for accurately selecting treatment dosage of tacrolimus of myasthenia gravis patient and application of system |
Non-Patent Citations (4)
| Title |
|---|
| 王春兰;: "阿片类药物治疗癌性疼痛的止痛效果及影响因素分析" * |
| 王春兰;: "阿片类药物治疗癌性疼痛的止痛效果及影响因素分析", 中国医师杂志, vol. 20, no. 07, pages 1083 - 1085 * |
| 程弦;白树堂;: "CYP2C9及APOE基因多态性与华法林稳态剂量和模型预测剂量的相关性研究" * |
| 程弦;白树堂;: "CYP2C9及APOE基因多态性与华法林稳态剂量和模型预测剂量的相关性研究", 中国胸心血管外科临床杂志, vol. 26, no. 06, pages 543 - 547 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116230158A (en) * | 2023-03-27 | 2023-06-06 | 中国医学科学院肿瘤医院 | Pain assessment and medication prediction system and application method thereof |
| CN116230158B (en) * | 2023-03-27 | 2024-01-26 | 中国医学科学院肿瘤医院 | Pain assessment and medication prediction system and application method thereof |
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