CN110910980A - Sepsis early warning device, equipment and storage medium - Google Patents
Sepsis early warning device, equipment and storage medium Download PDFInfo
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- CN110910980A CN110910980A CN201911182527.XA CN201911182527A CN110910980A CN 110910980 A CN110910980 A CN 110910980A CN 201911182527 A CN201911182527 A CN 201911182527A CN 110910980 A CN110910980 A CN 110910980A
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- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
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- G—PHYSICS
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Abstract
The application discloses early warning device, equipment and computer readable storage medium of sepsis blood disease, the device includes: the acquisition module is used for acquiring sample index data provided with the sepsis label; the calculation module is used for supplementing 0 to the vacancy value in the sample index data to obtain updated sample index data, and calculating a corresponding sample function parameter of the Gaussian function by using each updated sample index data; the training module is used for inputting each sample function parameter into a preset machine learning model and training to obtain a prediction model; the prediction module is used for calculating corresponding target function parameters according to the current index data of the target user and the Gaussian function, and inputting the target function parameters into the prediction model for prediction to obtain a prediction result; and the display is used for displaying the prediction result. The device can improve the prediction accuracy of the sepsis early warning device.
Description
Technical Field
The invention relates to the field of medical equipment, in particular to a sepsis early-warning device, equipment and a computer-readable storage medium.
Background
With the development of science and technology, medical auxiliary diagnosis becomes an important application and research direction of artificial intelligence. Currently, in order to more conveniently assist in diagnosing whether a user has sepsis, the prior art provides a sepsis early warning device. The current index data of the target user is input into a pre-trained prediction model, so that a corresponding prediction result can be obtained. However, in actual operation, the current index data of the user may be missing, and in this case, if the missing current index data is directly input to the prediction model, prediction may not be performed or the prediction result may be inaccurate. Therefore, the sepsis early-warning device provided by the prior art cannot accurately predict whether the user has sepsis or not.
Therefore, how to improve the prediction accuracy of the sepsis early-warning device is a technical problem that needs to be solved by those skilled in the art at present.
Disclosure of Invention
In view of this, the present invention provides a sepsis early-warning device, which can improve the accuracy of prediction of the sepsis early-warning device; another object of the present invention is to provide an apparatus for warning sepsis and a computer-readable storage medium, both of which have the above advantages.
In order to solve the above technical problem, the present invention provides a sepsis early warning device, comprising:
the acquisition module is used for acquiring sample index data provided with the sepsis label;
the calculation module is used for complementing the vacancy value in the sample index data with 0 to obtain updated sample index data, and calculating a corresponding sample function parameter of the Gaussian function by using each updated sample index data;
the training module is used for inputting each sample function parameter into a preset machine learning model and training to obtain a prediction model;
the prediction module is used for calculating corresponding target function parameters according to the current index data of the target user and the Gaussian function, and inputting the target function parameters into the prediction model for prediction to obtain a prediction result;
and the display is used for displaying the prediction result.
Preferably, the acquiring module specifically includes:
the first acquisition unit is used for extracting structured disease data in an LIS library and a nursing system;
a second collecting unit for collecting unstructured disease data in the case book by using a natural language processing technology;
and the setting unit is used for obtaining the sample index data provided with the sepsis label according to the structural disease data, the unstructured disease data and the diagnosis result of the disease history.
Preferably, further comprising:
and the cleaning unit is used for performing data cleaning on the sample index data provided with the sepsis label.
Preferably, further comprising:
the receiving module is used for receiving first index data of the sepsis label marked by a professional;
the parameter calculation module is used for calculating corresponding first function parameters according to the first index data and the Gaussian function;
and the updating module is used for inputting the first function parameter into the prediction model for training so as to update the prediction model.
Preferably, further comprising:
and the warning indicator is used for sending out corresponding prompt information when the prediction result is that the target user is ill.
In order to solve the above technical problem, the present invention further provides a sepsis warning apparatus, comprising:
a memory for storing a computer program;
a processor, configured to implement the following steps when executing the computer program:
acquiring sample index data provided with a sepsis label;
supplementing 0 to the vacancy value in the sample index data to obtain updated sample index data, and calculating a corresponding sample function parameter of the Gaussian function by using each updated sample index data;
inputting each sample function parameter into a preset machine learning model, and training to obtain a prediction model;
calculating corresponding target function parameters according to the current index data of the target user and the Gaussian function, and inputting the target function parameters into the prediction model for prediction to obtain a prediction result;
and displaying the prediction result.
To solve the above technical problem, the present invention further provides a computer-readable storage medium, having a computer program stored thereon, where the computer program, when executed by a processor, implements the following steps:
acquiring sample index data provided with a sepsis label;
supplementing 0 to the vacancy value in the sample index data to obtain updated sample index data, and calculating a corresponding sample function parameter of the Gaussian function by using each updated sample index data;
inputting each sample function parameter into a preset machine learning model, and training to obtain a prediction model;
calculating corresponding target function parameters according to the current index data of the target user and the Gaussian function, and inputting the target function parameters into the prediction model for prediction to obtain a prediction result;
and displaying the prediction result.
Compared with the prior art, on one hand, the sepsis early warning device obtains updated sample index data by supplementing a vacancy value in the sample index data with 0, then calculates sample function parameters by using a Gaussian function to obtain potential functions corresponding to the sample index data, and then inputs the sample function parameters into a machine learning model to train to obtain a prediction model; when a target user is predicted, calculating a corresponding target function parameter according to current index data of the target user and a Gaussian function, and predicting the target function parameter by using a prediction model to obtain a prediction result; thus, sepsis can be more accurately predicted; on the other hand, the sample function parameters are calculated according to the sample index data, and then the model is trained by utilizing the sample function parameters, so that the data dimensionality participating in the training is less, and the mode of training the prediction model is more convenient and faster.
In order to solve the technical problem, the invention also provides an early warning device for sepsis and a computer-readable storage medium, which have the beneficial effects.
Drawings
In order to more clearly illustrate the embodiments or technical solutions of the present invention, 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 provided drawings without creative efforts.
Fig. 1 is a flowchart of an apparatus for warning sepsis according to an embodiment of the present invention;
fig. 2 is a block diagram illustrating an apparatus for warning sepsis according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The core of the embodiment of the invention is to provide the sepsis early-warning device, which can improve the prediction accuracy of the sepsis early-warning device; another core of the present invention is to provide an apparatus for warning sepsis and a computer-readable storage medium, which have the above-mentioned advantages.
In order that those skilled in the art will better understand the disclosure, the invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a structural view of a sepsis warning apparatus according to an embodiment of the present invention, and as shown in fig. 1, the sepsis warning apparatus includes:
the obtaining module 10 is used for obtaining sample index data provided with the sepsis label;
the calculating module 20 is configured to complement 0 to the vacancy value in the sample index data to obtain updated sample index data, and calculate a sample function parameter of a corresponding gaussian function by using each updated sample index data;
the training module 30 is configured to input each sample function parameter into a preset machine learning model, and train to obtain a prediction model;
the prediction module 40 is configured to calculate a corresponding target function parameter according to the current index data of the target user and the gaussian function, and input the target function parameter into the prediction model for prediction to obtain a prediction result;
and a display 50 for displaying the prediction result.
It should be noted that the sample index data refers to index data provided with a sepsis label, and each sample index data includes a plurality of types of disease data. The disease data includes inspection data and examination data, and the inspection data is data obtained by laboratory experiments, such as the number of red blood cells, the number of white blood cells and the like; the examination data is data obtained by directly observing or examining instruments, such as body temperature, heartbeat frequency and the like. One sample index data includes a plurality of disease data, and the present embodiment does not limit the data types of the disease data specifically included in the sample index data. It is understood that, in actual operation, the more types of disease data associated with sepsis in the sample index data, the more quickly and accurately the early warning can be performed; and the larger the data size of the sample index data is, the more accurate the trained prediction model is.
Specifically, after the sample index data is obtained, the calculation module 20 supplements 0 to the vacancy value in the sample index data, that is, supplements the vacancy value in the sample index data with 0 to obtain updated sample index data; and then calculating the corresponding sample function parameters of the Gaussian function by using the updated sample index data.
Specifically, the process of calculating the sample function parameters is as follows:
wherein f isim(t) implicit function y representing disease data of type m for user i corresponding to sample index data at time tim(t) is the actual observed value, ktIs a shared function.
vec(Yi)≡yi～N(0,Σi)；
Wherein, yiIs the longitudinal data after straightening, KMAnd
the covariance matrix is the former control index M, the latter control time T, D is the diagonal matrix of noise, and I is the identity matrix.
Wherein the content of the first and second substances,
is the grid time XiAnd observation time
The correlation matrix of (a) is calculated,
is the grid time XiThe core of the self correlation matrix is to find out
A set of parameters, wherein η is a length scale parameter.
After the sample function parameters are obtained, namely the potential correlation functions are obtained, the training module 30 inputs the sample function parameters into a preset machine learning model, and the sample function parameters are used for training to obtain a prediction model; that is, the prediction model is trained based on the sample function parameters.
It can be understood that, the prediction model trained in this embodiment is trained by using sample function parameters, and therefore, when the prediction module 40 is used to predict sepsis of the current index data of the target user, the corresponding target function parameter needs to be calculated according to the current index data of the target user and the gaussian function, and then the target function parameter is input into the prediction model to perform prediction, so as to obtain a prediction result.
Display 60 is used to display the prediction made by prediction module 50 so that the medical personnel can visually obtain the prediction through the display. Specifically, the prediction result can be highlighted for high-risk and suspected patients by displaying the prediction result on the display. In addition, abnormal disease data can be further highlighted; personal information, laboratory examination information, medical record information, nursing records, the comparison condition of the updated current index data and the standard value and the like of the target user can be further displayed. It should be noted that, the presetting of the display method to display the corresponding prediction result by using the display is a technical content known by those skilled in the art, and the embodiment is not limited thereto.
Compared with the prior art, in one aspect, the sepsis early warning device provided by the embodiment of the invention obtains updated sample index data by supplementing 0 to a vacancy value in the sample index data, then calculates a sample function parameter by using a Gaussian function to obtain a potential function corresponding to the sample index data, and then inputs the sample function parameter into a machine learning model to train to obtain a prediction model; when a target user is predicted, calculating corresponding target function parameters according to current index data of the target user and a Gaussian function, and predicting the target function parameters by using a prediction model to obtain a prediction result; thus, sepsis can be more accurately predicted; on the other hand, the sample function parameters are calculated according to the sample index data, and then the model is trained by using the sample function parameters, so that the data dimension participating in training is less, the mode of training the prediction model is more convenient and faster, and the convenience degree of predicting the sepsis toxemia of the target user is improved.
On the basis of the foregoing embodiment, this embodiment further describes and optimizes the technical solution, and specifically, in this embodiment, the obtaining module specifically includes:
the first acquisition unit is used for extracting structured disease data in an LIS library and a nursing system;
the second acquisition unit is used for acquiring unstructured disease data in the case book by utilizing a natural language processing technology;
and the setting unit is used for obtaining sample index data provided with the sepsis label according to the structured disease data, the unstructured disease data and the diagnosis result of the disease history.
It should be noted that, the sample index data includes structured disease data and unstructured disease data, in this embodiment, a first collection unit is used to extract structured disease data in an LIS library (a Laboratory Information Management System, which is a set of Laboratory Information Management System specially designed for hospital clinical Laboratory) and a care System (a care Information System is an Information System that uses Information technology, computer technology and network communication technology to collect, store, process, transmit and query care Management and service technology Information, and is an important subsystem of a hospital Information System, and a second collection unit is used to collect unstructured disease data in a disease history book by using natural language processing technology; then, the setting unit obtains sample index data with a sepsis label according to the structured disease data, the unstructured disease data and the diagnosis result of the disease history, namely, sets a corresponding label for the disease data including the structured disease data and the unstructured disease data according to the diagnosis result of the disease history (whether sepsis occurs) to be used as the sample index data.
Therefore, the acquisition sub-module provided in this embodiment acquires the sample index data by extracting lis structured disease data in the library and the care system through the first acquisition unit and acquiring unstructured disease data in the medical record through the second acquisition unit by using the natural language processing technology, so that the data source of the sample index data is wider.
As a preferred embodiment, the present embodiment further comprises:
and the cleaning unit is used for performing data cleaning on the sample index data provided with the sepsis label.
That is, after the sample index data provided with the sepsis label is obtained, the data of each sample index data is further cleaned by the cleaning unit. Specifically, data cleaning refers to finding and correcting recognizable errors in sample index data, including data consistency checking, invalid value processing and the like, and sample index data are normalized according to corresponding machine learning models, so that the sample index data are conveniently trained by the machine learning models, and convenience and accuracy of training are improved.
On the basis of the above embodiments, the present embodiment further describes and optimizes the technical solution, and specifically, the present embodiment further includes:
the receiving module is used for receiving first index data of the sepsis label marked by the professional;
the parameter calculation module is used for calculating a corresponding first function parameter according to the first index data and the Gaussian function;
and the updating module is used for inputting the first function parameter into the prediction model for training so as to update the prediction model.
In this embodiment, the method further includes a receiving module for receiving first index data labeled with the sepsis label by the professional, where the first index data is the index data labeled with the sepsis label by the professional; then, calculating a corresponding first function parameter according to the first index data and the Gaussian function through a parameter calculation module; and inputting the first function parameter into the prediction model through the updating module for training so as to update the prediction model.
It is understood that, since the label of the first index data is labeled by the professional, the first index data is more accurate index data with a sepsis label than the sample index data; therefore, the updating module inputs the first function parameter calculated by the parameter calculating module into the prediction model for training, and the prediction model can be continuously updated, so that the prediction of the prediction model is more accurate.
On the basis of the above embodiments, the present embodiment further describes and optimizes the technical solution, and specifically, the present embodiment further includes:
and the warning indicator is used for sending out corresponding prompt information when the prediction result is that the target user is ill.
Specifically, the embodiment further includes an alarm, and when the prediction result obtained by predicting the current index data of the target user by the prediction module is that the target user is ill, the alarm sends out corresponding prompt information to prompt the medical staff about the abnormal condition of the target user.
It should be noted that, the alarm in this embodiment may be specifically a buzzer or an indicator light, and the sound frequency of the buzzer or the light frequency of the indicator light is used to set the corresponding prompt information, so as to achieve the prompt effect. It can be understood that the buzzer and the indicator lamp are used as common prompting devices, the prompting effect can be achieved simply and intuitively, and the price is low.
In the embodiment, the corresponding prompt information is sent out by further utilizing the warning indicator when the prediction result is that the target user is sick, so that prompt can be performed more intuitively and timely.
The above detailed description is made on the embodiment of the sepsis warning device provided by the present invention, and the present invention further provides a sepsis warning apparatus and a computer readable storage medium corresponding to the device, and since the embodiment of the apparatus and the computer readable storage medium portion corresponds to the embodiment of the device portion, please refer to the description of the embodiment of the device portion for the embodiment of the apparatus and the computer readable storage medium portion, and details are not repeated here.
Fig. 2 is a block diagram illustrating a sepsis warning apparatus according to an embodiment of the present invention, and as shown in fig. 2, the sepsis warning apparatus includes:
a memory 21 for storing a computer program;
the processor 22, when executing the computer program stored in the memory, implements the steps of:
a memory for storing a computer program;
a processor for implementing the following steps when executing the computer program stored in the memory:
acquiring sample index data provided with a sepsis label;
supplementing 0 to the vacancy value in the sample index data to obtain updated sample index data, and calculating a corresponding sample function parameter of the Gaussian function by using each updated sample index data;
inputting each sample function parameter into a preset machine learning model, and training to obtain a prediction model;
calculating corresponding target function parameters according to current index data of a target user and a Gaussian function, and inputting the target function parameters into a prediction model for prediction to obtain a prediction result;
and displaying the prediction result.
The sepsis early-warning device provided by the embodiment of the invention has the beneficial effect of the sepsis early-warning device.
In order to solve the above technical problem, the present invention further provides a computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the following steps:
acquiring sample index data provided with a sepsis label;
supplementing 0 to the vacancy value in the sample index data to obtain updated sample index data, and calculating a corresponding sample function parameter of the Gaussian function by using each updated sample index data;
inputting each sample function parameter into a preset machine learning model, and training to obtain a prediction model;
calculating corresponding target function parameters according to current index data of a target user and a Gaussian function, and inputting the target function parameters into a prediction model for prediction to obtain a prediction result;
and displaying the prediction result.
The computer-readable storage medium provided by the embodiment of the invention has the beneficial effects of the sepsis early-warning device.
The sepsis warning apparatus, and the computer-readable storage medium according to the present invention are described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to aid in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the device disclosed by the embodiment, the description is relatively simple because the device corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
Claims (7)
1. A sepsis warning device, comprising:
the acquisition module is used for acquiring sample index data provided with the sepsis label;
the calculation module is used for supplementing 0 to the vacancy value in the sample index data to obtain updated sample index data, and calculating a corresponding sample function parameter of the Gaussian function by using each updated sample index data;
the training module is used for inputting each sample function parameter into a preset machine learning model and training to obtain a prediction model;
the prediction module is used for calculating corresponding target function parameters according to the current index data of the target user and the Gaussian function, and inputting the target function parameters into the prediction model for prediction to obtain a prediction result;
and the display is used for displaying the prediction result.
2. The apparatus according to claim 1, wherein the obtaining module specifically includes:
the first acquisition unit is used for extracting structured disease data in an LIS library and a nursing system;
a second collecting unit for collecting unstructured disease data in the case book by using a natural language processing technology;
and the setting unit is used for obtaining the sample index data provided with the sepsis label according to the structural disease data, the unstructured disease data and the diagnosis result of the disease history.
3. The apparatus of claim 2, further comprising:
and the cleaning unit is used for performing data cleaning on the sample index data provided with the sepsis label.
4. The apparatus of claim 1, further comprising:
the receiving module is used for receiving first index data of the sepsis label marked by a professional;
the parameter calculation module is used for calculating corresponding first function parameters according to the first index data and the Gaussian function;
and the updating module is used for inputting the first function parameter into the prediction model for training so as to update the prediction model.
5. The apparatus of any of claims 1 to 4, further comprising:
and the warning indicator is used for sending out corresponding prompt information when the prediction result is that the target user is ill.
6. An apparatus for warning sepsis, comprising:
a memory for storing a computer program;
a processor for implementing the following steps when executing the computer program stored in the memory:
acquiring sample index data provided with a sepsis label;
supplementing 0 to the vacancy value in the sample index data to obtain updated sample index data, and calculating a corresponding sample function parameter of a Gaussian function by using each updated sample index data;
inputting each sample function parameter into a preset machine learning model, and training to obtain a prediction model;
calculating corresponding target function parameters according to the current index data of the target user and the Gaussian function, and inputting the target function parameters into the prediction model for prediction to obtain a prediction result;
and displaying the prediction result.
7. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the steps of:
acquiring sample index data provided with a sepsis label;
supplementing 0 to the vacancy value in the sample index data to obtain updated sample index data, and calculating a corresponding sample function parameter of a Gaussian function by using each updated sample index data;
inputting each sample function parameter into a preset machine learning model, and training to obtain a prediction model;
calculating corresponding target function parameters according to the current index data of the target user and the Gaussian function, and inputting the target function parameters into the prediction model for prediction to obtain a prediction result;
and displaying the prediction result.
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