CN117637152A

CN117637152A - Method and system for predicting sodium blood fluctuation

Info

Publication number: CN117637152A
Application number: CN202410063777.6A
Authority: CN
Inventors: 卢晓; 何昆仑; 董蔚; 徐洪丽; 许嘉宇
Original assignee: Chinese PLA General Hospital
Current assignee: Chinese PLA General Hospital
Priority date: 2024-01-17
Filing date: 2024-01-17
Publication date: 2024-03-01

Abstract

The application discloses a method, a system, a device, a storage medium and a product for predicting sodium blood fluctuation, wherein the method for predicting sodium blood fluctuation comprises the following steps: acquiring patient condition data; acquiring a sodium blood concentration sequence characteristic based on the patient condition data; dividing the sodium blood concentration sequence feature to obtain a sample data set; and inputting the sample data set into a pre-trained time-feature fusion model to output sodium bleeding information so as to detect the sodium bleeding features of the patient. The sodium concentration sequence characteristics are obtained by extracting the patient condition data, and then the sodium concentration sequence characteristics are segmented according to the sodium concentration sequence characteristics, so that the segmented sample data set is input into a pre-trained model to detect the sodium characteristics of the patient, the patient condition data are efficiently integrated and interpreted and analyzed by an intelligent means, high-precision sodium blood prediction is realized, and intervention measures for sodium adjustment are accurately realized.

Description

Method and system for predicting sodium blood fluctuation

Technical Field

The present application relates generally to the field of medical treatment, and more particularly, to a method, system, device, storage medium and product for predicting sodium blood fluctuation.

Background

At present, sodium blood level management mainly depends on doctor experience and simplified model, and a personalized and accurate prediction means is lacked

The relative stability of the electrolyte concentration of sodium, potassium, calcium, magnesium, chlorine and the like in the in-vivo environment is maintained. The cell function of the human body has certain requirement on electrolyte concentration, and the main cation in the plasma of the human body is Na, K, ca, mg, which plays a decisive role in maintaining the osmotic pressure of extracellular fluid and the distribution and transfer of body fluid; the main anions in the extracellular fluid are mainly Cl-and HCO 3-which have important effects on maintaining acid-base balance besides maintaining the tension of body fluid. In general, the total number of anions in the body fluid is equal to the total number of cations and remains electrically neutral, which results in different body damage, i.e. electrolyte imbalance, when any one of the electrolyte numbers changes. Sodium blood abnormalities are common electrolyte imbalances in the ICU, and are closely related to mortality and ICU residence time in stroke patients. Serious electrolyte imbalance can lead to blood pressure drop, coma, circulatory failure, and death.

In the prior art, sodium blood management in electrolyte mainly depends on doctor experience and simplified model, and has the problems of lower accuracy, larger error, incapability of real-time monitoring and incapability of intelligent prediction.

Disclosure of Invention

In view of the above-described drawbacks or deficiencies of the prior art, it is desirable to provide a method, system, apparatus, storage medium and product for predicting sodium in blood fluctuations.

In one aspect, the present application provides a method of predicting sodium in blood fluctuation, comprising:

acquiring patient condition data;

acquiring a sodium blood concentration sequence characteristic based on the patient condition data;

dividing the sodium blood concentration sequence feature to obtain a sample data set;

and inputting the sample data set into a pre-trained time-feature fusion model to output sodium bleeding information so as to detect the sodium bleeding features of the patient.

In some embodiments, inputting the sample dataset into a pre-trained time-feature fusion model to output sodium bleeding information comprises:

the sodium blood information at least comprises one or more of the following: abnormal sodium blood, sodium blood value detection and sodium blood intervention.

In some embodiments, obtaining a sodium blood concentration sequence profile based on the patient condition data comprises:

the sodium concentration sequence features include at least one or more of the following: serum potassium, serum chlorine, serum sodium, glucose, serum magnesium, urea, serum calcium, bicarbonate, sodium-water input volume and liquid ingress and egress volume.

In some embodiments, the sodium blood concentration sequence feature is segmented to obtain a sample dataset, in particular:

the sample data sets are classified according to time.

In a second aspect, the present application provides a system for predicting sodium in blood fluctuations, comprising:

the first acquisition module is used for acquiring patient condition data;

the second acquisition module is used for acquiring the sodium blood concentration sequence characteristic based on the patient condition data;

the segmentation module is used for segmenting the sodium blood concentration sequence characteristics to obtain a sample data set;

and the output module is used for inputting the sample data set into a pre-trained time-feature fusion model to output sodium bleeding information so as to detect the sodium bleeding features of the patient.

In a third aspect, the present application provides an apparatus for predicting sodium blood fluctuations, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a set of codes, or a set of instructions, the program, the set of codes, or the set of instructions being loaded and executed by the processor to implement the method for predicting sodium blood fluctuations described above

In a fourth aspect, the present application provides a non-transitory computer readable storage medium, which when executed by a processor of a mobile terminal, causes the mobile terminal to perform the above-described method of predicting sodium blood fluctuation.

In another aspect, the present application provides a computer program product which, when executed by a processor of a mobile terminal, enables the mobile terminal to perform the above-described method of predicting sodium blood fluctuation.

In summary, the method, the system, the device, the storage medium and the product for predicting sodium blood fluctuation are based on the method, the system and the device for predicting sodium blood fluctuation, the sodium blood concentration sequence characteristics are obtained by extracting the patient disease data, and then the sample data set after the segmentation is segmented according to the sodium blood concentration sequence characteristics, so that the sodium blood characteristics of the patient are detected by inputting the sample data set after the segmentation into a pre-trained model, the disease data of the patient are efficiently integrated and interpreted and analyzed by an intelligent means, the high-precision sodium blood prediction is realized, and meanwhile, the intervention measure of accurate sodium blood regulation is realized.

Drawings

Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings, in which:

FIG. 1 is a flow chart of a method of predicting sodium blood fluctuations provided by an embodiment of the present application;

FIG. 2 is a schematic diagram of segmentation of a sodium blood concentration sequence feature provided in an embodiment of the present application;

FIG. 3 is a schematic diagram of a time-feature fusion model provided in an embodiment of the present application;

FIG. 4 is a block diagram of a system for predicting sodium blood fluctuations provided by an embodiment of the present application;

fig. 5 is a schematic structural diagram of a device for predicting sodium blood fluctuation according to an embodiment of the present application.

Detailed Description

The present application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be noted that, for convenience of description, only the portions related to the invention are shown in the drawings.

It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

The present application may relate to the field of medical treatment in general, and is directed to efficient integration and interpretation analysis of patient condition data, implementing high-accuracy sodium blood prediction, and the following examples of the present application exemplarily illustrate methods of predicting sodium blood fluctuations.

Referring in detail to fig. 1, the present application provides a method for predicting sodium blood fluctuation, comprising:

s101, acquiring patient condition data.

Specifically, the data is from the open source clinical database of the intensive care medical information center: MIMIMIIC-III and MIMIIC-IV. The database is highly authoritative following the enhanced epidemiological observational study report guidelines. For example, if the MIMIC-III database is used in the patient condition data extraction process, the ICD9 is used to diagnose stroke patients as follows: 4340 43401, 43410, 43411, 43490, 43491, 4370, 4373, 4374; in the MIMIC-IV database, the ICD10 based diagnosis of stroke patients is encoded as follows: i60 I61, I63. Here, the ninth version of the international disease classification, ICD9, is exchanged for the ICD10 version in the MIMIC-IV dataset due to the update of the disease classification.

S102, acquiring the sequence characteristics of the sodium concentration based on the patient condition data.

Specifically, 2346 and 896 stroke patients were included from MIMIMIC-III and MIMIMIC-IV, respectively. All time series recordings of 10 features closely related to the current sodium concentration were extracted.

Specifically, the information affecting the sequence characteristics of the sodium concentration in blood includes a plurality of, including at least one or more of the following, serum potassium, serum chlorine, serum sodium, glucose, serum magnesium, urea, serum calcium, bicarbonate, sodium-water input volume and liquid in and out volume. Here, the method of obtaining the sodium-water input volume and the liquid in-out volume may be oral intake, intravenous injection, tube feeding, liquid medicine intake, or the like.

In other embodiments, the sodium blood concentration sequence profile further requires pretreatment.

Specifically, the data excluding the abnormality of the sodium concentration sequence characteristics or the data excluding the information affecting the sodium concentration sequence characteristics is empty. Or according to the age, excluding the data with the age under 18 years in the sequence characteristics of the sodium concentration in blood.

And S103, segmenting the sodium blood concentration sequence characteristic to obtain a sample data set.

Specifically, for example, as shown in fig. 2, all time series data of one patient are exemplified. First, all time series recordings of 10 features of one patient closely related to the blood sodium concentration were extracted. In the sub-sample segmentation schematic diagram, black dots represent time points of each detection index record, small boxes with different colors represent different indexes recorded at different time points, and red triangles are recorded data of target task blood sodium. Second, the current time point with serum sodium record is represented by a red triangle as t, pushed forward for 24 hours, 48 hours and 72 hours, respectively, and all data of 10 features are collected during these three time windows, respectively, to create different sub-data sets, i.e., sample data sets. Here, the sample data set may be generated in a plurality, e.g., 31428 training sample data sets by MIMIC-IV, 11547 internally validated sample data sets by MIMIC-IV, 28618 tested sample data sets by MIMIC-III.

the sample data sets are classified according to time.

Specifically, the sample data set includes a sub-sample data set representing a 24-hour time window, a sub-sample data set representing a 48-hour time window, and a sub-sample data set representing a 72-hour time window.

S104, inputting the sample data set into a pre-trained time-feature fusion model to output sodium bleeding information so as to detect the sodium bleeding features of the patient.

Specifically, data is input into a pre-trained time-feature fusion model through a plurality of collected sample data sets, so that sodium blood information of a patient is obtained, sodium blood values and whether the patient is abnormal or not are output, and intervention measures are output. The time-feature fusion model is here trained with a training sample dataset obtained in advance and validated by an internal validation dataset. As shown in fig. 3, the time-feature fusion model is composed of an extraction module, a multi-head attention module and an output module. The extraction module is used for extracting multi-dimensional third-order tensor data, is responsible for extracting and constructing tensor format data, and enhances the capability of the model in processing complex data relations. The multi-head attention module is used for focusing on capturing intricate dependency relationships and links inside data through a complex attention mechanism. And the output module is used for integrating the information processed by the first several modules to generate final output, and effectively integrating and interpreting the analyzed data.

Specifically, according to the sodium blood information at least including whether sodium blood is abnormal, sodium blood value detection and sodium blood intervention measures, the sodium blood value is obtained, whether the sodium blood value is abnormal or not is obtained, the intervention measures are output, the limitation of the prior art is overcome, and accurate sodium blood management is realized.

In summary, the method for predicting sodium blood fluctuation of the invention obtains the sodium blood concentration sequence feature by extracting the patient disease data, and then segments the sodium blood concentration sequence feature, so that the segmented sample data set is input into a pre-trained model to detect the sodium blood feature of the patient, and the disease data of the patient is efficiently integrated and interpreted for analysis, thereby realizing high-precision sodium blood prediction and accurately realizing intervention measures for sodium blood adjustment.

With further reference to fig. 4, a schematic diagram of a system 200 for predicting sodium blood fluctuations in accordance with one embodiment of the present application is shown, comprising: a first acquisition module 201, a second acquisition module 202, a segmentation module 203, and an output module 204.

A first acquisition module 201 for acquiring patient condition data;

a second acquisition module 202 for acquiring a sodium blood concentration sequence feature based on the patient condition data;

the segmentation module 203 segments the sodium blood concentration sequence feature to obtain a sample data set;

an output module 204 for inputting the sample data set into a pre-trained time-feature fusion model for outputting sodium blood information to detect sodium blood features of the patient.

The division of the modules or units mentioned in the above detailed description is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation instructions of possible implementations of systems, methods and computer program products according to various embodiments of the present application. 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, blocks shown in two separate connections may in fact be performed substantially in parallel, or they may sometimes be performed in the reverse order, depending on 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 foregoing description is only of the preferred embodiments of the present application and is presented as a description of the principles of the technology being utilized. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this application is not limited to the specific combinations of features described above, but it is intended to cover other embodiments in which any combination of features described above or equivalents thereof is possible without departing from the spirit of the disclosure. Such as the above-described features and technical features having similar functions (but not limited to) disclosed in the present application are replaced with each other.

With further reference to fig. 5, a schematic diagram of an apparatus 300 for predicting sodium blood fluctuations according to one embodiment of the present application is shown.

The execution body of the method for predicting sodium blood fluctuation in this embodiment is a device for predicting sodium blood fluctuation, and the device for predicting sodium blood fluctuation in this embodiment may be implemented in software and/or hardware, and the device for predicting sodium blood fluctuation in this embodiment may be configured in an electronic device, or may be configured in a server for controlling an electronic device, where the server communicates with the electronic device to control the electronic device.

The electronic device in this embodiment may include, but is not limited to, a personal computer, a platform computer, a smart phone, and the like, and the embodiment is not particularly limited to the electronic device.

The apparatus 300 for predicting sodium blood fluctuations of the present embodiment comprises a processor and a memory, the processor and the memory being interconnected, wherein the memory is adapted to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the method according to any of the preceding claims.

In the embodiments of the present application, the processor is a processing device that performs logic operations, such as a Central Processing Unit (CPU), a field programmable logic array (FPGA), a Digital Signal Processor (DSP), a single chip Microcomputer (MCU), an application specific logic circuit (ASIC), an image processor (GPU), or the like, and has data processing capability and/or program execution capability. It will be readily appreciated that the processor is typically communicatively coupled to a memory, on which is stored any combination of one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory can include, for example, random Access Memory (RAM) and/or cache memory (cache) and the like. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), USB memory, flash memory, and the like. One or more computer instructions may be stored on the memory and executed by the processor to perform the relevant analysis functions. Various applications and various data, such as various data used and/or generated by the applications, may also be stored in the computer readable storage medium.

In this embodiment of the present application, each module may be implemented by a processor executing related computer instructions, for example, the acquisition module may be implemented by the processor executing acquired instructions, the input module may be implemented by the processor executing instructions of the rule model, and the neural network may be implemented by the processor executing instructions of the neural network algorithm.

In the embodiment of the application, each module may run on the same processor or may run on multiple processors; the modules may be run on processors of the same architecture, e.g., all on processors of the X86 system, or on processors of different architectures, e.g., the image processing module runs on the CPU of the X86 system and the machine learning module runs on the GPU. The modules may be packaged in one computer product, for example, the modules are packaged in one computer software and run in one computer (server), or may be packaged separately or partially in different computer products, for example, the image processing modules are packaged in one computer software and run in one computer (server), and the machine learning modules are packaged separately in separate computer software and run in another computer (server); the computing platform when each module executes may be local computing, cloud computing, or hybrid computing composed of local computing and cloud computing.

The computer system includes a Central Processing Unit (CPU) 301 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage section 308 into a Random Access Memory (RAM) 303. In the RAM303, various programs and data required for operation instructions of the system are also stored. The CPU301, ROM302, and RAM303 are connected to each other through a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.

The following components are connected to the I/O interface 305; an input section 306 including a keyboard, a mouse, and the like; an output portion 307 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 308 including a hard disk or the like; and a communication section 309 including a network interface card such as a LAN card, a modem, or the like. The communication section 309 performs communication processing via a network such as the internet. The drive 310 is also connected to the I/O interface 305 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 310 as needed, so that a computer program read therefrom is installed into the storage section 308 as needed.

In particular, according to embodiments of the present application, the process described above with reference to flowchart fig. 1 may be implemented as a computer software program. For example, embodiments of the present application 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 shown in the flowcharts. In such an embodiment, the computer program contains program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 309, and/or installed from the removable medium 311. The above-described functions defined in the system of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 301.

The electronic device provided by the embodiment of the application is provided with a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of the above.

It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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 context of this document, 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 the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium 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: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

In one embodiment, a computer program product is provided, which, when executed by a processor of an electronic device, causes an apparatus for predicting sodium blood fluctuations to perform the steps of: acquiring patient condition data; acquiring a sodium blood concentration sequence characteristic based on the patient condition data; dividing the sodium blood concentration sequence feature to obtain a sample data set; and inputting the sample data set into a pre-trained time-feature fusion model to output sodium bleeding information so as to detect the sodium bleeding features of the patient.

It is to be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are merely for convenience in describing and simplifying the description based on the orientation or positional relationship shown in the drawings, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus are not to be construed as limiting the invention.

Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.

Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing particular implementations only and is not intended to be limiting of the invention. Terms such as "disposed" or the like as used herein may refer to either one element being directly attached to another element or one element being attached to another element through an intermediate member. Features described herein in one embodiment may be applied to another embodiment alone or in combination with other features unless the features are not applicable or otherwise indicated in the other embodiment.

The present invention has been described in terms of the above embodiments, but it should be understood that the above embodiments are for purposes of illustration and description only and are not intended to limit the invention to the embodiments described. Those skilled in the art will appreciate that many variations and modifications are possible in light of the teachings of the invention, which variations and modifications are within the scope of the invention as claimed.

Claims

1. A method of predicting sodium oscillation in blood, comprising:

acquiring patient condition data;

2. The method of predicting sodium blood fluctuations of claim 1, wherein inputting the sample dataset into a pre-trained time-feature fusion model outputs sodium blood information comprising:

3. The method of predicting sodium in blood fluctuations of claim 1, wherein obtaining a sodium concentration sequence signature based on the patient condition data comprises:

4. The method of predicting sodium in blood fluctuations of claim 1, wherein the sodium concentration sequence feature is segmented to obtain a sample dataset, in particular:

the sample data sets are classified according to time.

5. A system for predicting sodium oscillation in blood, comprising:

the first acquisition module is used for acquiring patient condition data;

6. The system for predicting sodium in blood fluctuations of claim 5, wherein inputting the sample dataset into a pre-trained time-feature fusion model outputs sodium information comprising:

7. The system for predicting sodium in blood fluctuations of claim 5, wherein obtaining a sequence of sodium in blood based on the patient condition data comprises:

8. An apparatus for predicting sodium blood fluctuations comprising a processor and a memory, wherein the memory has stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, the program, the set of codes, or the set of instructions being loaded and executed by the processor to implement the method for predicting sodium blood fluctuations of any one of claims 1-4.

9. A non-transitory computer readable storage medium, characterized in that instructions in the storage medium, when executed by a processor of a mobile terminal, enable the mobile terminal to perform the method of predicting sodium blood fluctuation according to any one of claims 1-4.

10. A computer program product, characterized in that instructions in the computer program product, when executed by a processor of a mobile terminal, enable the mobile terminal to perform the method of predicting sodium blood fluctuations according to any one of claims 1-4.