CN116453669B - Nursing prediction method and device based on big data - Google Patents
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Abstract
The application discloses a nursing prediction method and a device based on big data, wherein the method extracts a nursing index curve chart set according to nursing disease names, extracts initial to-be-matched index data and to-be-matched index curve chart sets respectively in nursing index monitoring data and nursing index curve chart sets according to-be-matched nursing indexes, extracts the initial index curve chart set according to an index fluctuation range, calculates a current index data change value according to the current to-be-matched index data, acquires a comparison index data change value set, calculates a current index difference value set of the current index data change value and the comparison index data change value, calculates the minimum difference degree of the nursing index curve chart and the nursing index monitoring data according to the current index difference value set, extracts a target index curve chart set according to the minimum difference degree, and predicts the disease nursing result of the current patient according to the target index curve chart set. The application can achieve the technical effects of fully utilizing nursing big data and improving nursing of patients.
Description
Technical Field
The application relates to the technical field of data prediction, in particular to a nursing prediction method and device based on big data.
Background
With the development of electronic medical record informatization, massive nursing data starts to show explosive growth. Nursing big data may refer to all data related to nursing and vital health, for example: data generated by hospital care, community care, or disease control.
At present, in the process of nursing patients, index data such as vital signs of the patients are monitored mainly by means of timing, and then the condition recovery condition of the patients is mastered, and the condition occurrence of untimely treatment caused by condition deterioration of the patients is also effectively prevented, but the nursing method has the problems of lag nursing information, insufficient utilization of nursing big data and poor nursing effect of the patients.
Disclosure of Invention
The application aims to provide a nursing prediction method and device based on big data, which are used for solving or at least partially solving the technical problem of poor nursing effect of patients caused by insufficient utilization of nursing big data due to lag of nursing information in the prior art.
In order to solve the technical problems, the technical scheme of the application is as follows:
the first aspect provides a nursing prediction method based on big data, comprising:
acquiring nursing disease names and nursing index monitoring data of a current patient, and extracting a nursing index curve chart set from pre-constructed historical nursing big data according to the nursing disease names;
acquiring a pre-constructed nursing index set, and sequentially extracting nursing indexes to be matched from the pre-constructed nursing index set;
respectively extracting initial to-be-matched index data and a to-be-matched index curve chart set from the nursing index monitoring data and the nursing index curve chart set according to the to-be-matched nursing index;
extracting an initial index curve chart set from the index curve chart set to be matched according to the initial index data to be matched and the pre-constructed index fluctuation range;
extracting current index data to be matched from the nursing index monitoring data, and calculating a current index data change value in a preset monitoring period according to the current index data to be matched;
obtaining a comparison index data change value of each index curve in the initial index curve graph in the preset monitoring period to obtain a comparison index data change value set;
calculating the current index difference value of each comparison index data change value in the current index data change value and the comparison index data change value set to obtain a current index difference value set;
calculating the minimum difference between all nursing index graphs in the nursing index graph and the nursing index monitoring data according to the current index difference sets of all nursing indexes to be matched and a pre-constructed pathological data difference formula, wherein the pathological data difference formula is as follows:
wherein ,representing the minimum degree of difference, i representing the patient's serial number in the historic care big data,/->、/> and />Respectively representing the difference degree of the current patient and the 1 st, 2 nd and n th patients in the historical nursing big data, j represents the serial number of the preset monitoring period, and +.>Represents the j-th monitoring period, m represents the serial number of the nursing index to be matched, and +.>Representing the current index difference value of the mth care index to be matched between the current patient and the first patient in the jth monitoring period;
and extracting a target index curve chart set from the nursing index curve chart set according to the minimum difference degree, acquiring later-period nursing data of the target index curve chart set, and predicting the illness state nursing result of the current patient according to the later-period nursing data.
In one embodiment, the extracting an initial index curve graph set from the index graph set to be matched according to the initial index data to be matched and the pre-constructed index fluctuation range includes:
acquiring a nursing monitoring period of the initial index data to be matched;
extracting an index data set of each index curve in the index curve graph to be matched in the nursing monitoring period;
calculating the difference value of the initial index data to be matched and each index data in the index data set to obtain a difference value set;
extracting a target difference set smaller than a pre-constructed index fluctuation range from the difference set;
and extracting an initial index curve graph set corresponding to the target difference set.
In one embodiment, the calculating the current index data change value in the preset monitoring period according to the current index data to be matched includes:
drawing a current index curve to be matched according to the current index data to be matched;
extracting the monitoring initial time and the monitoring ending time of the preset monitoring period;
determining a transverse straight line according to the monitoring initial moment;
determining a longitudinal straight line according to the monitoring termination time;
and calculating the area surrounded by the transverse straight line, the longitudinal straight line and the current index curve to be matched, and taking the surrounded area as the current index data change value.
In one embodiment, the determining a transverse straight line according to the monitoring initial time includes:
extracting initial index data points of the current index curve to be matched at the monitoring initial moment;
and making a straight line passing through the initial index data point according to the direction of the transverse coordinate axis of the coordinate system where the current index curve to be matched is located, and obtaining the transverse straight line.
In one embodiment, the determining a longitudinal straight line according to the monitoring termination time includes:
extracting termination index data points of the current index curve to be matched at the monitoring termination moment;
and making a straight line passing through the termination index data point according to the direction of the longitudinal coordinate axis of the coordinate system where the current index curve to be matched is located, so as to obtain the longitudinal straight line.
In one embodiment, the calculating the area enclosed by the transverse straight line, the longitudinal straight line and the current index curve to be matched includes:
calculating a monitoring integration interval according to the monitoring initial time and the monitoring termination time;
and calculating a micro-integral value of the current index curve to be matched in the monitoring integral interval according to a pre-constructed integral formula, and taking the micro-integral value as the area surrounded by the transverse straight line, the longitudinal straight line and the current index curve to be matched.
In one embodiment, the acquiring post-care data of the target metric curve set includes:
sequentially extracting target index curves in the target index curve graph set according to the nursing index;
and extracting the curve data after the monitoring termination time from the target index curve, and taking the curve data after the monitoring termination time as the later-period nursing data.
Based on the same inventive concept, a second aspect of the present application provides a nursing prediction apparatus based on big data, comprising:
the monitoring data acquisition module is used for acquiring the nursing disease name and nursing index monitoring data of the current patient, and extracting a nursing index curve chart set from the pre-constructed historical nursing big data according to the nursing disease name;
the nursing index extraction module to be matched is used for obtaining a pre-constructed nursing index set, and sequentially extracting the nursing indexes to be matched in the pre-constructed nursing index set;
the initial to-be-matched index data extraction module is used for respectively extracting initial to-be-matched index data and a to-be-matched index curve chart set in the nursing index monitoring data and the nursing index chart set according to the to-be-matched nursing index;
the initial index curve graph set extraction module is used for extracting an initial index curve graph set in the index curve graph set to be matched according to the initial index data to be matched and the pre-constructed index fluctuation range;
the current index data change value calculation module is used for extracting current index data to be matched from the nursing index monitoring data and calculating a current index data change value in a preset monitoring period according to the current index data to be matched;
the control index data change value set obtaining module is used for obtaining control index data change values of each index curve in the initial index curve graph in the preset monitoring period to obtain a control index data change value set;
the current index difference value set obtaining module is used for calculating the current index difference value of each comparison index data change value in the current index data change value and the comparison index data change value set to obtain a current index difference value set;
the minimum difference calculation module is used for calculating the minimum difference between all nursing index graphs in the nursing index graph and the nursing index monitoring data according to the current index difference set of all nursing indexes to be matched and a pre-constructed pathological data difference formula, wherein the pathological data difference formula is as follows:
wherein ,representing the minimum degree of difference, i representing the patient's serial number in the historic care big data,/->、/> and />Respectively representing the difference degree of the current patient and the 1 st, 2 nd and n th patients in the historical nursing big data, j represents the serial number of the preset monitoring period, and +.>Represents the j-th monitoring period, m represents the serial number of the nursing index to be matched, and +.>Representing the current index difference value of the mth care index to be matched between the current patient and the first patient in the jth monitoring period;
and the nursing prediction module is used for extracting a target index curve chart set from the nursing index curve chart set according to the minimum difference degree, acquiring later-period nursing data of the target index curve chart set and predicting the illness state nursing result of the current patient according to the later-period nursing data.
Based on the same inventive concept, a third aspect of the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed, implements the method of the first aspect.
Based on the same inventive concept, a fourth aspect of the present application provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, said processor implementing the method according to the first aspect when executing said program.
Compared with the prior art, the application has the following advantages and beneficial technical effects:
the application provides a nursing prediction method based on big data, firstly acquiring nursing disease name and nursing index monitoring data of a current patient, then extracting nursing index curve graph sets corresponding to the nursing disease name in pre-constructed historical nursing big data by utilizing the nursing disease name, further realizing the purpose of respectively extracting initial to-be-matched index data and to-be-matched index curve graph sets in the nursing index monitoring data and the nursing index curve graph sets by extracting to-be-matched nursing index one by one, firstly extracting the initial index curve graph set in the to-be-matched index curve graph sets according to the initial to-be-matched index data and the pre-constructed index fluctuation range when comparing the initial to-be-matched index data and the pre-constructed index fluctuation range, calculating the current index data change value in a preset monitoring period according to the current index data to be matched, acquiring a comparison index data change value set of each index curve in the initial index curve graph in the preset monitoring period, calculating the current index difference value of each comparison index data change value in the current index data change value and the comparison index data change value set to obtain a current index difference value set, calculating the minimum difference degree of all nursing index curves and the nursing index monitoring data in the nursing index curve graph according to the current index difference value sets of all nursing indexes to be matched and a pre-constructed pathological data difference degree formula, extracting a target index curve graph set in the nursing index curve graph according to the minimum difference degree, and taking the later-stage nursing data of the target index curve chart set as a prediction result of the current patient. Therefore, the nursing prediction method based on the big data can solve the problems of lag nursing information, insufficient utilization of nursing big data and poor nursing effect of patients in the nursing method.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a nursing prediction method based on big data provided by the implementation of the application.
Detailed Description
The embodiment of the application provides a nursing prediction method and device based on big data, which are used for solving the problem of poor nursing effect of patients caused by insufficient utilization of nursing big data due to lag of nursing information in the existing nursing method.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
Referring to fig. 1, a flow chart of a nursing prediction method based on big data according to an embodiment of the application is shown. In this embodiment, the nursing prediction method based on big data includes:
s1: acquiring nursing disease names and nursing index monitoring data of a current patient, and extracting a nursing index curve chart set from pre-constructed historical nursing big data according to the nursing disease names;
s2: acquiring a pre-constructed nursing index set, and sequentially extracting nursing indexes to be matched from the pre-constructed nursing index set;
s3: respectively extracting initial to-be-matched index data and a to-be-matched index curve chart set from the nursing index monitoring data and the nursing index curve chart set according to the to-be-matched nursing index;
s4: extracting an initial index curve chart set from the index curve chart set to be matched according to the initial index data to be matched and the pre-constructed index fluctuation range;
s5: extracting current index data to be matched from the nursing index monitoring data, and calculating a current index data change value in a preset monitoring period according to the current index data to be matched;
s6: obtaining a comparison index data change value of each index curve in the initial index curve graph in the preset monitoring period to obtain a comparison index data change value set;
s7: calculating the current index difference value of each comparison index data change value in the current index data change value and the comparison index data change value set to obtain a current index difference value set;
s8: calculating the minimum difference between all nursing index graphs in the nursing index graph and the nursing index monitoring data according to the current index difference sets of all nursing indexes to be matched and a pre-constructed pathological data difference formula, wherein the pathological data difference formula is as follows:
wherein ,representing the minimum degree of difference, i representing the patient's serial number in the historic care big data,/->、/> and />Respectively representing the difference degree of the current patient and the 1 st, 2 nd and n th patients in the historical nursing big data, j represents the serial number of the preset monitoring period, and +.>Represents the j-th monitoring period, m represents the serial number of the nursing index to be matched, and +.>Representing the current index difference value of the mth care index to be matched between the current patient and the first patient in the jth monitoring period;
s9: and extracting a target index curve chart set from the nursing index curve chart set according to the minimum difference degree, acquiring later-period nursing data of the target index curve chart set, and predicting the illness state nursing result of the current patient according to the later-period nursing data.
In step S1, the nursing patient name refers to the name of the disease of the patient under nursing, for example: gastric ulcers, skin burns, and the like. Nursing index monitoring data refers to monitoring data of the nursing patient name, for example: when the nursing disease is called gastric ulcer, the nursing index monitoring data can be serum Epidermal Growth Factor (EGF), vascular Endothelial Growth Factor (VEGF), tumor necrosis factor-（TNF-/>) And content data of substances such as superoxide dismutase (SOD).
Further, the historical nursing big data refers to the nursing index monitoring data and the atlas for recording different nursing disease names and different patients in the past. The nursing index curve chart set refers to a curve chart set drawn according to the nursing index monitoring data in the historical nursing big data, and curves in the curve chart set can be drawn by taking time as an abscissa and the nursing index monitoring data as an ordinate.
In one embodiment, before extracting the care index curve atlas from the pre-constructed historical care big data according to the care disease name in step S1, the method further comprises:
classifying and storing pre-stored nursing data according to the nursing patient name to obtain patient name classified nursing data;
classifying the disease name classified nursing data by using the nursing index set for nursing the disease name to obtain index classified nursing data;
and drawing a nursing index graph according to the index classification nursing data to obtain the nursing index graph set.
It should be appreciated that the care index profile set should include a set of profiles of care index monitoring data for different patients under the same care patient name and the same care index.
In step S2, the nursing index set refers to a nursing index set corresponding to each nursing patient name, for example: when the nursing disease is named as gastric ulcer, the nursing index set may be a serum Epidermal Growth Factor (EGF) content index, a Vascular Endothelial Growth Factor (VEGF) content index, a tumor necrosis factor-alpha (TNF-alpha) content index, and a superoxide dismutase (SOD) content index.
In step S3, the initial to-be-matched index data may be content data of to-be-matched nursing index of the patient in the period when the operation starts to be performed after the completion of the operation. The to-be-matched index curve chart set refers to a curve chart set of to-be-matched nursing indexes in a nursing index curve chart set.
In step S4, it can be understood that the index fluctuation range refers to the allowable error between the nursing index monitoring data at the start detection time of the graph in the index graph set to be matched and the initial index data to be matched, and generally the nursing index monitoring data of patients with the same nursing disease name and different disease conditions are different, and the referenceable value between the patients with larger difference is smaller, so that the index fluctuation range can be used for first screening the index graph set to be matched to obtain the patient data with larger reference value. When the care index to be matched is EGF, the index fluctuation range can be 10.00ng/L, and when the initial index data to be matched is 120.00 ng/L, the EGF initial value of the initial index curve chart set is between 110.00 ng/L and 130.00 ng/L.
In step S5, the monitoring period is an interval period for monitoring the care index monitoring data of the current patient, for example: index monitoring is carried out on the current patient at 1 month, 1 day and 1 point, and monitoring is carried out at 1 month, 2 days and 1 point, so that the monitoring period is 24 hours. The current index data change value refers to the area of an area surrounded by a curve corresponding to the current index data to be matched of the current patient and two straight lines parallel to the coordinate axis.
In step S6, the comparison index data change value set is the same as the calculation method of the current index data change value, and will not be described here again, since the initial index graph set includes index data graphs of multiple persons, and therefore should be a set.
In step S7, the current index difference refers to the difference between the current index data change value and the reference index data change, and the smaller the difference, the more similar the recovery condition of two patients is, and the greater the reference value is.
In step S8, the current index difference refers to the difference between the current index data change value and the control index data change, the smaller the difference is, the more similar the recovery condition of two patients is, the larger the reference value is, so when the nursing indexes are comprehensively considered, the corresponding difference degrees of all the nursing indexes are added to obtain the comprehensive disease recovery speed evaluation value of two patients, and when the sum of the difference degrees is smaller, the more similar the disease recovery condition of the current patient and the historical patient is, the larger the reference value is.
In one embodiment, the extracting an initial index curve graph set from the index graph set to be matched according to the initial index data to be matched and the pre-constructed index fluctuation range (step S4) includes:
acquiring a nursing monitoring period of the initial index data to be matched;
extracting an index data set of each index curve in the index curve graph to be matched in the nursing monitoring period;
calculating the difference value of the initial index data to be matched and each index data in the index data set to obtain a difference value set;
extracting a target difference set smaller than a pre-constructed index fluctuation range from the difference set;
and extracting an initial index curve graph set corresponding to the target difference set.
Specifically, the care period refers to the care period of the current patient.
In one embodiment, the calculating the current index data change value in the preset monitoring period according to the current index data to be matched (step S5) includes:
drawing a current index curve to be matched according to the current index data to be matched;
extracting the monitoring initial time and the monitoring ending time of the preset monitoring period;
determining a transverse straight line according to the monitoring initial moment;
determining a longitudinal straight line according to the monitoring termination time;
and calculating the area surrounded by the transverse straight line, the longitudinal straight line and the current index curve to be matched, and taking the surrounded area as the current index data change value.
Wherein, the determining a transverse straight line according to the monitoring initial time includes:
extracting initial index data points of the current index curve to be matched at the monitoring initial moment;
and making a straight line passing through the initial index data point according to the direction of the transverse coordinate axis of the coordinate system where the current index curve to be matched is located, and obtaining the transverse straight line.
It should be understood that the monitoring initiation time and the monitoring termination time may be 1 month, 1 day, 1 point, and 1 month, 2 days, 1 point, respectively.
The method for determining the longitudinal straight line according to the monitoring termination time comprises the following steps:
extracting the termination index number of the current index curve to be matched at the monitoring termination moment
The determining a longitudinal straight line according to the monitoring termination time comprises the following steps:
extracting termination index data points of the current index curve to be matched at the monitoring termination moment;
and making a straight line passing through the termination index data point according to the direction of the longitudinal coordinate axis of the coordinate system where the current index curve to be matched is located, so as to obtain the longitudinal straight line.
Specifically, calculating the area enclosed by the transverse straight line, the longitudinal straight line and the current index curve to be matched comprises the following steps:
calculating a monitoring integration interval according to the monitoring initial time and the monitoring termination time;
and calculating a micro-integral value of the current index curve to be matched in the monitoring integral interval according to a pre-constructed integral formula, and taking the micro-integral value as the area surrounded by the transverse straight line, the longitudinal straight line and the current index curve to be matched.
In the specific implementation process, the pre-constructed integral formula is as follows:
wherein ,micro-integration value representing j-th monitoring period,/->Indicating the monitoring initiation time,/->Indicating the moment of termination of monitoring, +.>Representing the value of the current index curve to be matched at the initial index data point,/for>And (t) representing the value of the current index curve to be matched at the t-th moment in the monitoring integration interval.
It can be understood that the replacement of the current index data change value by the area can represent the recovery condition of the current index to be matched of the current patient, and the larger the area is, the better the recovery effect is, the smaller the area is, and the slower the recovery speed is.
In one embodiment, the acquiring post-care data of the target metric curve set includes:
sequentially extracting target index curves in the target index curve graph set according to the nursing index;
and extracting the curve data after the monitoring termination time from the target index curve, and taking the curve data after the monitoring termination time as the later-period nursing data.
Wherein predicting the current patient's condition care outcome based on the post-care data comprises:
judging whether abnormal curve data exist in the later-period nursing data or not;
if the abnormal curve data does not exist in the later-period nursing data, judging that the illness state nursing result of the current patient is normal;
if abnormal curve data exist in the later-stage nursing data, judging the symptom representation according to the abnormal curve data, and obtaining the illness nursing result.
Specifically, after the result of nursing the illness state is obtained, nursing is carried out according to the predicted condition, if the predicted result is worsening, treatment is carried out in time, if the predicted result is good, nutrition is supplemented according to the condition, exercise is enhanced, and recovery is carried out as soon as possible.
According to the nursing prediction method based on big data, firstly, the nursing disease name and the nursing index monitoring data of a current patient are obtained, then the nursing disease name is utilized to extract a nursing index curve chart set corresponding to the nursing disease name in the pre-constructed historical nursing big data, and the nursing indexes to be matched are extracted one by one, so that the aims of respectively extracting initial index data to be matched and the initial index curve chart set to be matched in the nursing index monitoring data and the nursing index curve chart set are achieved; when comparing initial to-be-matched index data and to-be-matched index curve atlas, firstly, extracting an initial index curve atlas in the to-be-matched index curve atlas according to the initial to-be-matched index data and a pre-constructed index fluctuation range, calculating a current index data change value in a preset monitoring period according to the current to-be-matched index data in the process of comparing the initial to-be-matched index data and the to-be-matched index curve atlas, acquiring a comparison index data change value set of each index curve in the initial index curve in the monitoring period, and then calculating a current index difference value of each comparison index data change value in the current index data change value set and the comparison index data change value set to obtain a current index difference value set; and finally, calculating the minimum difference degree of all nursing index graphs and nursing index monitoring data in the nursing index graph according to the current index difference value sets of all nursing indexes to be matched and a pre-constructed pathological data difference degree formula, extracting a target index curve graph set in the nursing index graph according to the minimum difference degree, and taking later-stage nursing data of the target index curve graph set as a prediction result of the current patient. Thereby can fully utilize nursing big data, improve patient nursing.
Example two
Based on the same inventive concept, the application discloses a nursing prediction device based on big data, comprising:
the monitoring data acquisition module is used for acquiring the nursing disease name and nursing index monitoring data of the current patient, and extracting a nursing index curve chart set from the pre-constructed historical nursing big data according to the nursing disease name;
the nursing index extraction module to be matched is used for obtaining a pre-constructed nursing index set, and sequentially extracting the nursing indexes to be matched in the pre-constructed nursing index set;
the initial to-be-matched index data extraction module is used for respectively extracting initial to-be-matched index data and a to-be-matched index curve chart set in the nursing index monitoring data and the nursing index chart set according to the to-be-matched nursing index;
the initial index curve graph set extraction module is used for extracting an initial index curve graph set in the index curve graph set to be matched according to the initial index data to be matched and the pre-constructed index fluctuation range;
the current index data change value calculation module is used for extracting current index data to be matched from the nursing index monitoring data and calculating a current index data change value in a preset monitoring period according to the current index data to be matched;
the control index data change value set obtaining module is used for obtaining control index data change values of each index curve in the initial index curve graph in the preset monitoring period to obtain a control index data change value set;
the current index difference value set obtaining module is used for calculating the current index difference value of each comparison index data change value in the current index data change value and the comparison index data change value set to obtain a current index difference value set;
the minimum difference calculation module is used for calculating the minimum difference between all nursing index graphs in the nursing index graph and the nursing index monitoring data according to the current index difference set of all nursing indexes to be matched and a pre-constructed pathological data difference formula, wherein the pathological data difference formula is as follows:
wherein ,representing the minimum degree of difference, i representing the patient's serial number in the historic care big data,/->、/> and />Respectively representing the difference degree of the current patient and the 1 st, 2 nd and n th patients in the historical nursing big data, j represents the serial number of the preset monitoring period, and +.>Represents the j-th monitoring period, m represents the serial number of the nursing index to be matched, and +.>Representing the current index difference value of the mth care index to be matched between the current patient and the first patient in the jth monitoring period;
and the nursing prediction module is used for extracting a target index curve chart set from the nursing index curve chart set according to the minimum difference degree, acquiring later-period nursing data of the target index curve chart set and predicting the illness state nursing result of the current patient according to the later-period nursing data.
Since the device described in the second embodiment of the present application is a device for implementing the nursing prediction method based on big data in the first embodiment of the present application, based on the method described in the first embodiment of the present application, a person skilled in the art can understand the specific structure and the deformation of the device, and therefore, the detailed description thereof is omitted herein. All devices used in the method of the first embodiment of the present application are within the scope of the present application.
Example III
Based on the same inventive concept, the present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed, implements the method as described in embodiment one.
Since the computer readable storage medium described in the third embodiment of the present application is a computer readable storage medium used for implementing the nursing prediction method based on big data in the first embodiment of the present application, based on the method described in the first embodiment of the present application, a person skilled in the art can understand the specific structure and the modification of the computer readable storage medium, and therefore, the detailed description thereof is omitted herein. All computer readable storage media used in the method according to the first embodiment of the present application are included in the scope of protection.
Example IV
Based on the same inventive concept, the application also provides a computer device, comprising a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor executes the program to implement the method in the first embodiment.
Because the computer device described in the fourth embodiment of the present application is a computer device used for implementing the nursing prediction method based on big data in the first embodiment of the present application, based on the method described in the first embodiment of the present application, a person skilled in the art can understand the specific structure and the deformation of the computer device, so that the detailed description thereof is omitted herein. All computer devices used in the method of the first embodiment of the present application are within the scope of the present application.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the embodiments of the present application without departing from the spirit or scope of the embodiments of the application. Thus, if such modifications and variations of the embodiments of the present application fall within the scope of the claims and the equivalents thereof, the present application is also intended to include such modifications and variations.
Claims (10)
1. A big data based care prediction method, comprising:
acquiring nursing disease names and nursing index monitoring data of a current patient, and extracting a nursing index curve chart set from pre-constructed historical nursing big data according to the nursing disease names;
acquiring a pre-constructed nursing index set, and sequentially extracting nursing indexes to be matched from the pre-constructed nursing index set;
respectively extracting initial to-be-matched index data and a to-be-matched index curve chart set from the nursing index monitoring data and the nursing index curve chart set according to the to-be-matched nursing index;
extracting an initial index curve chart set from the index curve chart set to be matched according to the initial index data to be matched and the pre-constructed index fluctuation range;
extracting current index data to be matched from the nursing index monitoring data, and calculating a current index data change value in a preset monitoring period according to the current index data to be matched;
obtaining a comparison index data change value of each index curve in the initial index curve graph in the preset monitoring period to obtain a comparison index data change value set;
calculating the current index difference value of each comparison index data change value in the current index data change value and the comparison index data change value set to obtain a current index difference value set;
calculating the minimum difference between all nursing index graphs in the nursing index graph and the nursing index monitoring data according to the current index difference sets of all nursing indexes to be matched and a pre-constructed pathological data difference formula, wherein the pathological data difference formula is as follows:
wherein ,representing the minimum degree of difference, i representing the patient's serial number in the historic care big data,/->、/> and />Respectively representing the difference degree of the current patient and the 1 st, 2 nd and n th patients in the historical nursing big data, j represents the serial number of the preset monitoring period, and +.>Represents the j-th monitoring period, m represents the serial number of the nursing index to be matched, and +.>Representing the current index difference value of the mth care index to be matched between the current patient and the first patient in the jth monitoring period;
and extracting a target index curve chart set from the nursing index curve chart set according to the minimum difference degree, acquiring later-period nursing data of the target index curve chart set, and predicting the illness state nursing result of the current patient according to the later-period nursing data.
2. The big data based care prediction method as claimed in claim 1, wherein the extracting an initial index curve atlas in the index curve set to be matched according to the initial index data to be matched and a pre-constructed index fluctuation range comprises:
acquiring a nursing monitoring period of the initial index data to be matched;
extracting an index data set of each index curve in the index curve graph to be matched in the nursing monitoring period;
calculating the difference value of the initial index data to be matched and each index data in the index data set to obtain a difference value set;
extracting a target difference set smaller than a pre-constructed index fluctuation range from the difference set;
and extracting an initial index curve graph set corresponding to the target difference set.
3. The big data based care prediction method as claimed in claim 1, wherein the calculating a current index data change value within a preset monitoring period according to the current index data to be matched comprises:
drawing a current index curve to be matched according to the current index data to be matched;
extracting the monitoring initial time and the monitoring ending time of the preset monitoring period;
determining a transverse straight line according to the monitoring initial moment;
determining a longitudinal straight line according to the monitoring termination time;
and calculating the area surrounded by the transverse straight line, the longitudinal straight line and the current index curve to be matched, and taking the surrounded area as the current index data change value.
4. A big data based care prediction method as recited in claim 3, wherein said determining a transverse straight line from said monitoring initiation time comprises:
extracting initial index data points of the current index curve to be matched at the monitoring initial moment;
and making a straight line passing through the initial index data point according to the direction of the transverse coordinate axis of the coordinate system where the current index curve to be matched is located, and obtaining the transverse straight line.
5. A big data based care prediction method according to claim 3, wherein said determining a longitudinal straight line from the monitoring termination time comprises:
extracting termination index data points of the current index curve to be matched at the monitoring termination moment;
and making a straight line passing through the termination index data point according to the direction of the longitudinal coordinate axis of the coordinate system where the current index curve to be matched is located, so as to obtain the longitudinal straight line.
6. The big data based care prediction method as set forth in claim 3, wherein said calculating an area enclosed by the horizontal straight line, the vertical straight line and the current index curve to be matched includes:
calculating a monitoring integration interval according to the monitoring initial time and the monitoring termination time;
and calculating a micro-integral value of the current index curve to be matched in the monitoring integral interval according to a pre-constructed integral formula, and taking the micro-integral value as the area surrounded by the transverse straight line, the longitudinal straight line and the current index curve to be matched.
7. The big data based care prediction method of claim 3, wherein the acquiring post-care data of the target index profile set comprises:
sequentially extracting target index curves in the target index curve graph set according to the nursing index;
and extracting the curve data after the monitoring termination time from the target index curve, and taking the curve data after the monitoring termination time as the later-period nursing data.
8. A big data based care prediction device, comprising:
the monitoring data acquisition module is used for acquiring the nursing disease name and nursing index monitoring data of the current patient, and extracting a nursing index curve chart set from the pre-constructed historical nursing big data according to the nursing disease name;
the nursing index extraction module to be matched is used for obtaining a pre-constructed nursing index set, and sequentially extracting the nursing indexes to be matched in the pre-constructed nursing index set;
the initial to-be-matched index data extraction module is used for respectively extracting initial to-be-matched index data and a to-be-matched index curve chart set in the nursing index monitoring data and the nursing index chart set according to the to-be-matched nursing index;
the initial index curve graph set extraction module is used for extracting an initial index curve graph set in the index curve graph set to be matched according to the initial index data to be matched and the pre-constructed index fluctuation range;
the current index data change value calculation module is used for extracting current index data to be matched from the nursing index monitoring data and calculating a current index data change value in a preset monitoring period according to the current index data to be matched;
the control index data change value set obtaining module is used for obtaining control index data change values of each index curve in the initial index curve graph in the preset monitoring period to obtain a control index data change value set;
the current index difference value set obtaining module is used for calculating the current index difference value of each comparison index data change value in the current index data change value and the comparison index data change value set to obtain a current index difference value set;
the minimum difference calculation module is used for calculating the minimum difference between all nursing index graphs in the nursing index graph and the nursing index monitoring data according to the current index difference set of all nursing indexes to be matched and a pre-constructed pathological data difference formula, wherein the pathological data difference formula is as follows:
wherein ,representing the minimum degree of difference, i representing the patient's serial number in the historic care big data,/->、/> and />respectively representing the difference degree of the current patient and the 1 st, 2 nd and n th patients in the historical nursing big data, j represents the serial number of the preset monitoring period, and +.>Represents the j-th monitoring period, m represents the serial number of the nursing index to be matched, and +.>Representing the current index difference value of the mth care index to be matched between the current patient and the first patient in the jth monitoring period;
and the nursing prediction module is used for extracting a target index curve chart set from the nursing index curve chart set according to the minimum difference degree, acquiring later-period nursing data of the target index curve chart set and predicting the illness state nursing result of the current patient according to the later-period nursing data.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when executed, implements the method of any one of claims 1 to 7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 7 when the program is executed.
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