CN117672486A - Estimation system and method for target ultrafiltration volume in hemodialysis - Google Patents
Estimation system and method for target ultrafiltration volume in hemodialysis Download PDFInfo
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- 238000001631 haemodialysis Methods 0.000 title claims abstract description 30
- 230000000322 hemodialysis Effects 0.000 title claims abstract description 30
- 238000000034 method Methods 0.000 title claims description 16
- 238000000502 dialysis Methods 0.000 claims abstract description 61
- 238000011282 treatment Methods 0.000 claims abstract description 41
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- 208000009304 Acute Kidney Injury Diseases 0.000 description 1
- 208000033626 Renal failure acute Diseases 0.000 description 1
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- 208000012998 acute renal failure Diseases 0.000 description 1
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- A61M1/00—Suction or pumping devices for medical purposes; Devices for carrying-off, for treatment of, or for carrying-over, body-liquids; Drainage systems
- A61M1/14—Dialysis systems; Artificial kidneys; Blood oxygenators ; Reciprocating systems for treatment of body fluids, e.g. single needle systems for hemofiltration or pheresis
- A61M1/28—Peritoneal dialysis ; Other peritoneal treatment, e.g. oxygenation
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- A61M1/00—Suction or pumping devices for medical purposes; Devices for carrying-off, for treatment of, or for carrying-over, body-liquids; Drainage systems
- A61M1/34—Filtering material out of the blood by passing it through a membrane, i.e. hemofiltration or diafiltration
- A61M1/3403—Regulation parameters
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Abstract
The invention discloses an estimation system of target ultrafiltration volume in hemodialysis, which belongs to the technical field of dialysis volume analysis and comprises the following components: the data acquisition module is used for recording dialysis case data of a patient in time sequence; the data processing module is used for ensuring the quality of the data through data preprocessing; the strategy learning module is used for constructing a prediction model by using an artificial neural network algorithm, and calculating ultrafiltration volume according to personal information serving as an input characteristic; and the auxiliary decision-making module is used for carrying out prefabrication judgment based on the target ultrafiltration volume, and reporting to a system background for doctor assessment if the target ultrafiltration volume exceeds a target threshold value. According to the invention, an artificial neural network model is constructed by using a deep learning technology, and ultrafiltration volume of multiple treatments is balanced, so that fluctuation of estimated ultrafiltration volume is controlled under the condition of different treatment intervals of a patient, thereby improving quality and stability of dialysis treatment and achieving better dialysis treatment effect in a safety range.
Description
Technical Field
The invention belongs to the technical field of dialysis quantity analysis, and particularly relates to an estimation system of target ultrafiltration quantity in hemodialysis.
Background
Hemodialysis is one of the kidney replacement treatment modes of patients with acute and chronic renal failure, and is characterized in that in-vivo blood is drained to the outside of the body, mass exchange is carried out through dispersion, ultrafiltration, adsorption and convection principles, excessive in-vivo moisture and metabolic waste are removed, electrolyte and acid-base balance are maintained, and purified blood is returned, so that the whole process is called hemodialysis. The ultrafiltration volume of hemodialysis is the ultrafiltration volume per dialysis, and a simple calculation is that the ultrafiltration volume is equal to the body weight before dialysis minus the dry body weight, and is substantially equal to the body weight gain of the patient during the two dialysis sessions.
Chinese patent application publication No.: CN107106040a discloses a medical fluid treatment machine and related systems and methods, including: receiving patient assessment information regarding one or more subjective characteristics of a patient; determining a patient assessment score based on the received patient assessment information; and modifying operation of the medical fluid treatment machine based on the patient assessment score, the foregoing scheme disclosing historical data relating to patient assessment information. The clinical server may evaluate and analyze the historical data volume to detect treatment trends. Automated dialysis treatment can be improved over time based on statistical data;
however, in practical use, hemodialysis generally requires three times per week, and one, three, five or two, four and six treatments are selected, but the time interval from the last treatment in the week to the first treatment in the next week exceeds one day, the three treatments have different intervals, the weight increase of the patient is different, and the up-and-down fluctuation of the ultrafiltration volume occurs according to the traditional ultrafiltration volume estimation method, so that the stability of the heart function of the patient and the dialysis treatment quality are affected, and there is room for improvement.
Disclosure of Invention
The invention aims at: in order to solve the problem that the traditional ultrafiltration volume estimation method can cause up-and-down fluctuation of ultrafiltration volume so as to influence the stability of heart function of a patient and the dialysis treatment quality, the invention provides an estimation system of target ultrafiltration volume in hemodialysis.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a system for estimating a target ultrafiltration volume in hemodialysis, comprising:
the data acquisition module is used for recording dialysis case data of a patient in time sequence;
the data processing module is used for ensuring the quality of the data through data preprocessing;
the strategy learning module is used for constructing a prediction model by using an artificial neural network algorithm, and calculating ultrafiltration volume according to personal information serving as an input characteristic;
and the auxiliary decision-making module is used for carrying out prefabrication judgment based on the target ultrafiltration volume, and reporting to a system background for doctor assessment if the target ultrafiltration volume exceeds a target threshold value.
As a further description of the above technical solution:
the dialysis case data includes basic information of the patient, medication, dialysis regimen, anticoagulation regimen, allergy information, and history of dialysis.
As a further description of the above technical solution:
the data preprocessing specifically comprises the following steps:
and (3) index quality control: calculating key indexes by using a system, observing the rationality of the result value of the indexes, if the result value is within a correct interval, considering that the data is available, otherwise, controlling by using human working media;
and (3) human working medium control: the human working medium control is mainly used for assisting in index quality control, and the data after human working medium control can be added into a final data set, so that the accuracy of the data is further improved.
As a further description of the above technical solution:
the strategy learning module specifically comprises: taking the preprocessed historical data as an input parameter, and taking the actual ultrafiltration volume in the corresponding historical data as an output parameter for training;
the target ultrafiltration volume for the dialysis is considered to be accurate because of possible data errors in treatment, and otherwise, the target ultrafiltration volume is considered to be set incorrectly because the difference between the target ultrafiltration volume and the actual ultrafiltration volume in the historical data is within a threshold range.
As a further description of the above technical solution:
the auxiliary decision comprises the following specific steps: after the patient is weighed, the system gives the target ultrafiltration volume of the treatment according to personal information, medication condition, dialysis scheme, anticoagulation scheme, allergy information and pre-penetration information of the treatment, a doctor can set a system evaluation threshold value, the target ultrafiltration volume lower than the system evaluation threshold value is directly evaluated and selectively executed by a nurse, and when the target ultrafiltration volume higher than the system evaluation threshold value is estimated and generated by the system, the target ultrafiltration volume estimated and generated by the system is submitted to the doctor for evaluation and selectively executed, so that auxiliary support of the dialysis target ultrafiltration volume is realized.
As a further description of the above technical solution:
the system also comprises a model updating module which is used for recording the recommended value of the target ultrafiltration volume, the actually executed target ultrafiltration volume and the actually ultrafiltered volume in each dialysis treatment course, periodically evaluating the dialysis sufficiency of the patient, and feeding back to the model for updating and optimizing.
As a further description of the above technical solution:
the method for estimating the target ultrafiltration volume in hemodialysis specifically comprises the following steps:
recording dialysis related data of the patient in time sequence;
preprocessing dialysis related data;
constructing a prediction model by establishing an artificial neural network algorithm, taking the preprocessed historical data as an input parameter, and taking the actual ultrafiltration volume in the corresponding historical data as an output parameter for training;
after the patient is weighed, the system inputs the trained artificial neural network prediction model according to the corresponding information of the patient, and gives the target ultrafiltration volume of the treatment;
judging whether the output ultrafiltration volume exceeds a system evaluation threshold value set by a doctor, directly evaluating and selectively executing the ultrafiltration volume target below the system evaluation threshold value by nurses, submitting the ultrafiltration volume target generated by the system prediction to the doctor for evaluation and selectively executing the ultrafiltration volume target above the system evaluation threshold value, and realizing auxiliary support on the ultrafiltration volume target of dialysis;
recording a recommended value of the target ultrafiltration volume, the actually executed target ultrafiltration volume and the actually ultrafiltered volume in each dialysis treatment course, and feeding back to the artificial neural network prediction model for retraining by periodically evaluating the dialysis sufficiency of the patient, and updating and optimizing the model.
As a further description of the above technical solution:
the method also comprises the step of considering the target ultrafiltration volume of the dialysis to be accurate because of possible data errors in treatment and considering the target ultrafiltration volume setting error otherwise, wherein the difference between the target ultrafiltration volume and the actual ultrafiltration volume in the historical data is in a threshold value range.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
1. according to the invention, an artificial neural network model is constructed by using a deep learning technology, and ultrafiltration volume of multiple treatments is balanced, so that fluctuation of estimated ultrafiltration volume is controlled under the condition of different treatment intervals of a patient, thereby improving quality and stability of dialysis treatment and achieving better dialysis treatment effect in a safety range.
2. In the invention, the ultrafiltration volume is set by not only the weight increase of this time, but also comprehensively considering the recent integral weight change, balancing the dialysis ultrafiltration volume for the time of calendar, comprehensively considering the physical sign change of the patient, setting the proper ultrafiltration volume, smoothing the ultrafiltration volume curve, and hopefully improving the dialysis treatment effect and long-term survival condition of the hemodialysis patient.
Drawings
FIG. 1 is a flow chart of a method for estimating a target ultrafiltration volume in hemodialysis according to the present invention;
fig. 2 is a logic block diagram of a target ultrafiltration volume estimation system in hemodialysis according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides a technical solution: a system for estimating a target ultrafiltration volume in hemodialysis, comprising: the data acquisition module is used for recording dialysis case data of a patient in time sequence;
the data processing module is used for ensuring the quality of the data through data preprocessing;
the strategy learning module is used for constructing a prediction model by using an artificial neural network algorithm, and calculating ultrafiltration volume according to personal information serving as an input characteristic;
the auxiliary decision-making module is used for carrying out prefabrication judgment based on the target ultrafiltration volume, reporting to a system background and submitting a doctor to evaluate if the target ultrafiltration volume exceeds a target threshold;
the dialysis case data comprise basic information of a patient, medication conditions, a dialysis scheme, an anticoagulation scheme, allergy information and a history dialysis record;
the data preprocessing specifically comprises the following steps:
and (3) index quality control: calculating key indexes by using a system, observing the rationality of the result value of the indexes, if the result value is within a correct interval, considering that the data is available, otherwise, controlling by using human working media;
and (3) human working medium control: the human working medium control is mainly auxiliary to index quality control, and the data after human working medium control can be added into a final data set, so that the accuracy of the data is further improved;
the strategy learning module specifically comprises: taking the preprocessed historical data as an input parameter, and taking the actual ultrafiltration volume in the corresponding historical data as an output parameter for training;
the difference between the target ultrafiltration volume and the actual ultrafiltration volume in the historical data is within a threshold range due to possible data errors in treatment, wherein the total threshold range of the embodiment is 0.01-0.1kg, and the target ultrafiltration volume in the dialysis is considered to be accurate, otherwise, the target ultrafiltration volume is considered to be wrong in setting.
The auxiliary decision comprises the following specific steps: after the patient is weighed, the system gives out target ultrafiltration volume of the treatment according to personal information, medication condition, dialysis scheme, anticoagulation scheme, allergy information and pre-penetration information of the treatment, a doctor can set a system evaluation threshold, the threshold in the embodiment is 5% of the dry weight of the patient, the target ultrafiltration volume lower than the system evaluation threshold is directly evaluated and selectively executed by a nurse, and when the target ultrafiltration volume higher than the system evaluation threshold is higher than the system evaluation threshold, the target ultrafiltration volume estimated and generated by the system is submitted to doctor evaluation and selectively executed, so that auxiliary support of dialysis target ultrafiltration volume is realized;
the model updating module is used for recording the recommended value of the target ultrafiltration volume, the actually executed target ultrafiltration volume and the actually ultrafiltrate volume in each dialysis treatment course, periodically evaluating the dialysis sufficiency of a patient, and feeding back to the model for updating and optimizing;
the method for estimating the target ultrafiltration volume in hemodialysis specifically comprises the following steps:
recording dialysis related data of the patient in time sequence;
preprocessing dialysis related data;
constructing a prediction model by establishing an artificial neural network algorithm, taking the preprocessed historical data as an input parameter, and taking the actual ultrafiltration volume in the corresponding historical data as an output parameter for training;
after the patient is weighed, the system inputs the trained artificial neural network prediction model according to the corresponding information of the patient, and gives the target ultrafiltration volume of the treatment;
judging whether the output ultrafiltration volume exceeds a system evaluation threshold value set by a doctor, directly evaluating and selectively executing the ultrafiltration volume target below the system evaluation threshold value by nurses, submitting the ultrafiltration volume target generated by the system prediction to the doctor for evaluation and selectively executing the ultrafiltration volume target above the system evaluation threshold value, and realizing auxiliary support on the ultrafiltration volume target of dialysis;
recording a recommended value of the target ultrafiltration volume, the actually executed target ultrafiltration volume and the actually ultrafiltered volume in each dialysis treatment course, and feeding back to an artificial neural network prediction model for retraining by periodically evaluating the dialysis sufficiency of a patient, and updating and optimizing the model;
the method also comprises the step of considering the target ultrafiltration volume of the dialysis to be accurate because of possible data errors in treatment and considering the target ultrafiltration volume setting error otherwise, wherein the difference between the target ultrafiltration volume and the actual ultrafiltration volume in the historical data is in a threshold value range.
Compared with the traditional ultrafiltration evaluation method, the method comprehensively considers the physical sign change of the patient, sets a proper ultrafiltration volume, smoothes the ultrafiltration volume curve, is hopeful to improve the dialysis treatment effect and the long-term survival condition of the hemodialysis patient, not only sets the ultrafiltration volume by the weight increase of the time, but also comprehensively considers the recent integral weight change and balances the dialysis ultrafiltration volume of the calendar.
Further, an embodiment of the present application further provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, which when executed implements a system for estimating a target ultrafiltration volume in hemodialysis as described in the above embodiments.
Further, embodiments of the present application also provide a computer-readable storage medium storing computer-executable instructions for causing a computer to perform a system for estimating a target ultrafiltration volume in hemodialysis as described in the above embodiments.
The present invention is preferably implemented in software, but may also be implemented in hardware or a combination of hardware and software. The invention may also be embodied as computer readable code on a computer readable medium. The computer readable medium is any data storage device that can store data which can be thereafter read by a computer system. Examples of a computer-readable medium include a read-only memory, a random-access memory, a compact disc (CD-ROM), a Digital Versatile Disc (DVD), magnetic cassettes, an optical data storage device, and a carrier wave. The computer readable medium can also be distributed over a network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion;
in the present disclosure, the terms "first," "second," and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance; the term "plurality" means two or more, unless expressly defined otherwise. The terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; "coupled" may be directly coupled or indirectly coupled through intermediaries. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (10)
1. A system for estimating a target ultrafiltration volume in hemodialysis, comprising:
the data acquisition module is used for recording dialysis case data of a patient in time sequence;
the data processing module is used for ensuring the quality of the data through data preprocessing;
the strategy learning module is used for constructing a prediction model by using an artificial neural network algorithm, and calculating ultrafiltration volume according to personal information serving as an input characteristic;
and the auxiliary decision-making module is used for carrying out prefabrication judgment based on the target ultrafiltration volume, and reporting to a system background for doctor assessment if the target ultrafiltration volume exceeds a target threshold value.
2. The system of claim 1, wherein the dialysis case data comprises basic information of a patient, medication, dialysis regimen, anticoagulation regimen, allergy information, and history of dialysis.
3. The system for estimating a target ultrafiltration volume in hemodialysis according to claim 1, wherein said data preprocessing specifically comprises the steps of:
and (3) index quality control: calculating key indexes by using a system, observing the rationality of the result value of the indexes, if the result value is within a correct interval, considering that the data is available, otherwise, controlling by using human working media;
and (3) human working medium control: the human working medium control is mainly used for assisting in index quality control, and the data after human working medium control can be added into a final data set, so that the accuracy of the data is further improved.
4. The system for estimating a target ultrafiltration volume in hemodialysis according to claim 1, wherein said strategy learning module specifically comprises: taking the preprocessed historical data as an input parameter, and taking the actual ultrafiltration volume in the corresponding historical data as an output parameter for training;
the target ultrafiltration volume for the dialysis is considered to be accurate because of possible data errors in treatment, and otherwise, the target ultrafiltration volume is considered to be set incorrectly because the difference between the target ultrafiltration volume and the actual ultrafiltration volume in the historical data is within a threshold range.
5. The system for estimating a target ultrafiltration volume in hemodialysis according to claim 1, wherein said auxiliary decision comprises in particular: after the patient is weighed, the system gives the target ultrafiltration volume of the treatment according to personal information, medication condition, dialysis scheme, anticoagulation scheme, allergy information and pre-penetration information of the treatment, a doctor can set a system evaluation threshold value, the target ultrafiltration volume lower than the system evaluation threshold value is directly evaluated and selectively executed by a nurse, and when the target ultrafiltration volume higher than the system evaluation threshold value is estimated and generated by the system, the target ultrafiltration volume estimated and generated by the system is submitted to the doctor for evaluation and selectively executed, so that auxiliary support of the dialysis target ultrafiltration volume is realized.
6. The system for estimating a target ultrafiltration volume in hemodialysis according to claim 1, further comprising a model updating module for recording a recommended value of the target ultrafiltration volume, an actual target ultrafiltration volume to be performed, and an actual ultrafiltration volume for each dialysis session, periodically evaluating the dialysis adequacy of the patient, and feeding back to the model for updating and optimizing.
7. A method for estimating a target ultrafiltration volume in hemodialysis, applied to the system for estimating a target ultrafiltration volume in hemodialysis according to any one of claims 1 to 6, characterized by comprising the steps of:
recording dialysis related data of the patient in time sequence;
preprocessing dialysis related data;
constructing a prediction model by establishing an artificial neural network algorithm, taking the preprocessed historical data as an input parameter, and taking the actual ultrafiltration volume in the corresponding historical data as an output parameter for training;
after the patient is weighed, the system inputs the trained artificial neural network prediction model according to the corresponding information of the patient, and gives the target ultrafiltration volume of the treatment;
judging whether the output ultrafiltration volume exceeds a system evaluation threshold value set by a doctor, directly evaluating and selectively executing the ultrafiltration volume target below the system evaluation threshold value by nurses, submitting the ultrafiltration volume target generated by the system prediction to the doctor for evaluation and selectively executing the ultrafiltration volume target above the system evaluation threshold value, and realizing auxiliary support on the ultrafiltration volume target of dialysis;
recording a recommended value of the target ultrafiltration volume, the actually executed target ultrafiltration volume and the actually ultrafiltered volume in each dialysis treatment course, and feeding back to the artificial neural network prediction model for retraining by periodically evaluating the dialysis sufficiency of the patient, and updating and optimizing the model.
8. The method according to claim 7, further comprising considering that the target ultrafiltration volume for the hemodialysis is accurate because of possible data errors during treatment and the difference between the target ultrafiltration volume in the history data and the actual ultrafiltration volume is within a threshold range, and otherwise considering that the target ultrafiltration volume for the hemodialysis is erroneously set.
9. An electronic device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements a method for estimating a target ultrafiltration volume in hemodialysis according to any of claims 1-6 when executing the program.
10. A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method of estimating a target ultrafiltration volume in hemodialysis according to any one of claims 1-6.
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