CN111354449B - Long-term care strategy distribution method, device, computer equipment and storage medium - Google Patents
Long-term care strategy distribution method, device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a long-term care strategy distribution method, a long-term care strategy distribution device, computer equipment and a storage medium. The method comprises the following steps: inputting the information of a plurality of nursed parties into a long-term care strategy distribution simulation model; the long-term care strategy distribution simulation model comprises a group division node, a care strategy distribution node and a care strategy redistribution node; the method comprises the following steps that a plurality of nursed parties are subjected to group division based on a group division node to obtain a plurality of nursing groups; calculating the nursing strategy distribution rate of different nursed parties in each nursing group based on the nursing strategy distribution nodes, and determining an initial nursing strategy corresponding to each nursed party according to the nursing strategy distribution rate; and calculating the disability transfer rate and the nursing strategy change rate of different nursed parties in each nursing group based on the nursing strategy redistribution node, and adjusting the initial nursing strategy corresponding to each nursed party according to the disability transfer rate and the nursing strategy change rate. By adopting the method, the accuracy of the prediction result can be improved.
Description
Technical Field
The present application relates to the field of internet technologies, and in particular, to a method and an apparatus for allocating a long-term care policy, a computer device, and a storage medium.
Background
With the continuous progress of data acquisition technology, data analysis and other technologies, the big data technology is applied to the analysis of phenomena or behaviors and is applied to various industries.
Nowadays, with the implementation of a long-term care safeguard system, people need to know different health states in time and predict the need for long-term care in a future period so as to make corresponding policy arrangement and adjustment in advance. However, the current long-term care strategy is formulated based on the care needs provided by the care-receiving party, and the care needs change in the long-term care process, so that the needs of the care-receiving party cannot be met in time and scientific allocation of care resources cannot be realized only according to the care needs provided by the care-receiving party.
Disclosure of Invention
In view of the above, it is necessary to provide a long-term care policy assignment method, apparatus, computer device and storage medium capable of improving the accuracy of care policy assignment.
A long-term care strategy assignment method, the method comprising: acquiring information of a plurality of nursed parties, and inputting the information of the plurality of nursed parties into a long-term care strategy distribution simulation model; the long-term care strategy distribution simulation model comprises a group division node, a care strategy distribution node and a care strategy redistribution node; the group division node divides a plurality of nursed parties into groups to obtain a plurality of nursing groups; acquiring a pre-configured care strategy distribution model corresponding to each care group through the care strategy distribution node, calculating the care strategy distribution rate of different care recipients in the corresponding care group based on the care strategy distribution model, and determining an initial care strategy corresponding to each care recipient according to the care strategy distribution rate; the method comprises the steps of obtaining a pre-configured disability transfer model and a nursing strategy adjusting model corresponding to each nursing group through a nursing strategy redistribution node, calculating disability transfer rates of different nursed parties in the corresponding nursing group based on the disability transfer model, calculating nursing strategy change rates of the different nursed parties in the corresponding nursing group based on the nursing strategy adjusting model, and adjusting an initial nursing strategy corresponding to each nursed party according to the disability transfer rate and the nursing strategy change rates.
In one embodiment, the method further comprises: acquiring a long-term care strategy distribution schematic diagram; the long-term care strategy distribution schematic diagram comprises a group division node, a care strategy distribution node and a care strategy redistribution node; determining simulation parameters for instructing the group partitioning node to perform group partitioning on the plurality of nursed parties; determining simulation parameters for instructing a care strategy distribution node to distribute initial care strategies for caregivers in each group; and determining simulation parameters for indicating the nursing strategy redistribution node to adjust the initial nursing strategy corresponding to the nursed party in each group according to the disability transfer rate and the nursing strategy change rate of the nursed party, so as to obtain a long-term nursing strategy distribution simulation model.
In one embodiment, the care strategy distribution rate is calculated based on a care strategy distribution model; the training step of the care strategy distribution model comprises the following steps: acquiring sample data; the sample data comprises health tracks of a plurality of sample caregivers; the health trajectory comprises a care strategy; acquiring an initial model corresponding to each care strategy to be trained; and training the initial model of the corresponding care strategy based on the sample data to obtain a care strategy distribution model corresponding to each care strategy.
In one embodiment, the obtaining an initial model corresponding to each care strategy to be trained includes: converting the care strategy in the health track information into a target care strategy according to the expected calculation precision; and acquiring an initial model to be trained corresponding to the care strategy of each target.
In one embodiment, the method further comprises: when the calculation precision is a first precision, the care strategies of the target comprise no need of care, no care, home care and institution care; when the calculation precision is a second precision, the care strategies of the target comprise no care, informal care, institution care, private care, volunteer care and community care.
In one embodiment, said training an initial model of a respective care strategy based on said sample data comprises: determining a sample label of corresponding sample data according to whether the care strategy in the sample data is consistent with the care strategy corresponding to the initial model; the sample data further comprises identity attributes of the party to which the sample is being cared for; extracting the identity characteristics of the identity attributes and the health characteristics of the health tracks; inputting the identity characteristics and the health characteristics into the initial model of the corresponding nursing strategy to obtain a predicted nursing strategy; and adjusting parameters of the initial model and continuing training according to the difference between the predicted care strategy and the sample label until the training ending condition is met, and ending the training to obtain a care strategy distribution model.
A long-term care strategy dispensing apparatus, the apparatus comprising: the model calling module is used for acquiring the information of a plurality of nursed parties and inputting the information of the plurality of nursed parties into a long-term care strategy distribution simulation model; the long-term care strategy distribution simulation model comprises a group division node, a care strategy distribution node and a care strategy redistribution node; the group division module is used for carrying out group division on a plurality of nursed parties through the group division node to obtain a plurality of nursing groups; the nursing strategy distribution module is used for acquiring a nursing strategy distribution model corresponding to each pre-configured nursing group through the nursing strategy distribution node, calculating the nursing strategy distribution rate of different nursed parties in the corresponding nursing group based on the nursing strategy distribution model, and determining an initial nursing strategy corresponding to each nursed party according to the nursing strategy distribution rate; and the nursing strategy adjusting module is used for acquiring a pre-configured disability transfer model and a nursing strategy adjusting model corresponding to each nursing group through the nursing strategy redistribution node, calculating disability transfer rates of different nursed parties in the corresponding nursing group based on the disability transfer model, calculating nursing strategy change rates of different nursed parties in the corresponding nursing group based on the nursing strategy adjusting model, and adjusting an initial nursing strategy corresponding to each nursed party according to the disability transfer rate and the nursing strategy change rates.
In one embodiment, the apparatus further comprises a model building module for obtaining a long-term care strategy allocation schematic diagram; the long-term care strategy distribution schematic diagram comprises a group division node, a care strategy distribution node and a care strategy redistribution node; determining simulation parameters for instructing the group partitioning node to perform group partitioning on the plurality of nursed parties; determining simulation parameters for instructing a care strategy distribution node to distribute an initial care strategy for a care-receiver in each group; and determining simulation parameters for indicating the nursing strategy redistribution node to adjust the initial nursing strategy corresponding to the nursed party in each group according to the disability transfer rate and the nursing strategy change rate of the nursed party, so as to obtain a long-term nursing strategy distribution simulation model.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the long-term care strategy allocation method provided in any one of the embodiments of the present application when executing the computer program.
A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the long-term care strategy allocation method provided in any one of the embodiments of the present application.
According to the long-term care strategy allocation method, the device, the computer equipment and the storage medium, when long-term care demand prediction is carried out based on a pre-constructed simulation model, the initial care strategy of a care-receiver is determined by combining the disability distribution rate of the care-receiver, and the initial care strategy can be dynamically adjusted by combining the disability transfer rate and the care strategy change rate of the care-receiver, so that the demand change tracks of a plurality of continuous time nodes can be dynamically predicted, the long-term care demand prediction accuracy is improved, and a reliable basis is provided for implementation and adjustment of the care policy.
Drawings
FIG. 1 is a diagram illustrating an exemplary long-term care policy distribution method;
FIG. 2 is a flow diagram of a long-term care policy assignment methodology in one embodiment;
FIG. 3 is a schematic diagram of a long-term care policy assignment schematic in one embodiment;
FIG. 4 is a block diagram of a long-term care policy distribution apparatus according to an embodiment;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The long-term care strategy allocation method provided by the application can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented by an independent server or a server cluster formed by multiple servers.
In one embodiment, as shown in fig. 2, a long-term care policy assignment method is provided, which is described by taking the method as an example applied to the terminal 110 or the server 120 in fig. 1, and includes the following steps:
step 202, obtaining information of a plurality of nursed parties, and inputting the information of the plurality of nursed parties into a long-term care strategy distribution simulation model; the long-term care strategy distribution simulation model comprises a group division node, a care strategy distribution node and a care strategy redistribution node.
The chronic care strategy allocation simulation model may be constructed in advance based on a long-term care strategy allocation schematic. The long-term care policy assignment schematic may be drawn based on software tools such as STATA. Referring to fig. 3, fig. 3 is a schematic diagram of a long-term care strategy allocation schematic in one embodiment. As shown in fig. 3, the long-term care policy distribution schematic diagram includes a plurality of prediction nodes, which may be divided into a group division node 302, a care policy distribution node 304, a care policy node 306, and a care policy redistribution 308 according to different execution logics of the corresponding nodes. Each prediction node has corresponding simulation parameters.
In one embodiment, before invoking the long-term care policy assignment simulation model, the long-term care policy assignment method further includes: acquiring a long-term care strategy distribution schematic diagram; the long-term care strategy distribution schematic diagram comprises a group division node, a care strategy distribution node and a care strategy redistribution node; determining node logic information for instructing a group partitioning node to perform group partitioning on a plurality of nursed parties; determining node logic information for instructing a care strategy distribution node to distribute an initial care strategy for a care-receiver in each group; and determining node logic information for indicating the nursing strategy redistribution node to adjust the initial nursing strategy corresponding to the nursed party in each group according to the disability transfer rate and the nursing strategy change rate of the nursed party to obtain a long-term nursing strategy distribution simulation model.
In one embodiment, the method further comprises: when the calculation precision is the first precision, the target care strategies comprise no need of care, no care, home care and institution care; when the calculation accuracy is a second accuracy, the care strategies of the target include no care, informal care, institutional care, private care, volunteer care, and community care.
In this embodiment, the long-term care policy distribution schematic diagram includes six group partitioning nodes 302, one care policy distribution node 304, and one care policy redistribution node 308. According to different nursing strategy division granularities, the precision of the long-term nursing demand prediction result can be divided into a first precision and a second precision, and the number of the corresponding nursing strategy nodes 306 is also different. For example, in the present embodiment, there may be 5 or 8 care policy nodes 306 according to different care policy allocation accuracies. When the nursing strategy allocation precision is the first precision, the corresponding 5 nursing strategy nodes are respectively nursing-free, home nursing, institution nursing and death. When the care strategy is of the second precision, the corresponding 8 care strategy nodes are respectively unnecessary care, no care, home care, institution care, private care, volunteer care, community care and death.
By not requiring care is meant that no long term care is required at all. Failure to receive care means that care is needed, but none is received. Home care means that a care recipient who is resident at home receives care, and is mainly provided by family, friends or professional caregivers, or community service organizations. Institutional care means that the care recipient resides in a care institution to receive care. Private care means that the care-giver resides in the home and is primarily provided with care by a professional care provider. Volunteer care means that a care-target party living at home provides services such as sending meals, regular visits and the like for free by volunteers, friends, neighbors and the like. Community care means that a care-receiver resides in a home and care is provided by the community.
And 204, carrying out group division on the multiple nursed parties through the group division node to obtain multiple nursing groups.
And after the distribution schematic diagram of the long-term care strategy is drawn, configuring the implementation logic of each prediction node. Specifically, when the computer device needs to divide the to-be-allocated care-givers into 6 care groups according to the age, the gender and the disability distribution rate based on 6 group division nodes, the simulation parameters of the 6 group division nodes may be sequentially configured as combinations of different age intervals, genders and disability distribution rate intervals. For example, one combination may be "male, age [65, 70], disability distribution rate [70, 80]", and another combination may be "female, age [70, 75], disability distribution rate [80, 90]", etc.
Wherein, the disability distribution rate can be calculated based on the disability distribution rate model. The disability distribution rate model may be a pre-trained machine learning model, such as a logistic regression model. Training samples are obtained through population census, questionnaire and other modes. The training samples can be information such as identity attributes and health tracks of a plurality of cared parties in historical periods (such as 2014-2018). The identity attributes include, among other things, the age, gender, marital status, household type (city/town), etc. of the party being cared for. The health track includes information of health state transitions that occur at multiple points in time by the care-giver. Health states include health and disability. The computer equipment preprocesses the training samples to obtain a plurality of groups of sample data and incapability prediction labels corresponding to each group of sample data (X1, X2). Wherein X1 is age and X2 is gender. The disability prediction label may be a characteristic value corresponding to whether the elderly are disabled, such as Y disability =1 for disability and Y disability =0 for health.
The initial model to be trained may be a logistic regression model:
z＝β 0 +β 1 X 1 +β 2 X 2 。
wherein, pr (Y) Disability to use = 1) means the probability of occurrence of a "disabling" event, beta 0 、β 1 And beta 2 Are regression parameters.
The computer equipment inputs preprocessed sample data into an initial model to be trained to obtain an intermediate prediction result, calculates a difference value between the intermediate prediction result and the incapability prediction label, adjusts regression parameters in the initial model according to the difference value until a trained model with the accuracy reaching a threshold is obtained, and takes the trained model as an incapability distribution rate model.
And step 206, acquiring a pre-configured care strategy distribution model corresponding to each care group through the care strategy distribution node, calculating the care strategy distribution rate of different care recipients in the corresponding care group based on the care strategy distribution model, and determining an initial care strategy corresponding to each care recipient according to the care strategy distribution rate.
When the computer device needs to determine the initial care policies required for each care group based on the care policy assignment node, the simulation parameters of the care policy assignment node may be configured as a pre-trained care policy distribution model. Subsequently, the computer device may respectively calculate probabilities that the care-receiving party in each care group is suitable for different care strategies according to the care strategy model, and use the care strategy with the highest probability as the initial care strategy of the care-receiving party. The node configuration information also includes an initial care strategy determination time, such as 5 days each month, and beginnings. In other words, the caregiver long-term care policy assignment is made once 5 days of the first month of each month.
When the computer device needs to determine the number of the nursed parties corresponding to each care strategy based on the care strategy nodes, the simulation parameters of the care strategy nodes can be configured to count the number of the nursed parties allocated to the corresponding initial care strategy. For example, the care policy node "home care" is used to count the number of cared parties for which the initial care policy is "home care". In one embodiment, the counted number of attended parties is displayed above the corresponding care strategy node. As shown in fig. 3, the number of care recipients corresponding to the care strategy node "home care" is n3.
It should be noted that the care policy node 306a is a node whose care policy is "dead". Once a care-receiver is "dead" with respect to the care policy, long-term care need prediction for the care-receiver is terminated.
In one embodiment, based on the pre-statistically derived conclusion (which may be third party data) of "the duration of time of the cared party under each care strategy for different ages and sexes health status" with universality, the corresponding duration of time is recorded above each care strategy node 306.
And 208, acquiring a pre-configured disability transfer model and a nursing strategy adjustment model corresponding to each nursing group through a nursing strategy redistribution node, calculating disability transfer rates of different nursed parties in the corresponding nursing group based on the disability transfer model, calculating nursing strategy change rates of different nursed parties in the corresponding nursing group based on the nursing strategy adjustment model, and adjusting an initial nursing strategy corresponding to each nursed party according to the disability transfer rate and the nursing strategy change rates.
When the computer device needs to determine the disability transfer rate of the caregivers in each care group based on the care policy redistribution node, the simulation parameters of the care policy redistribution node may be configured as a pre-trained disability transfer model. Subsequently, the computer device can respectively calculate the disability transfer rate of each cared party according to the disability transfer model. The disabling transfer model may also be a pre-trained machine learning model, such as a logistic regression model. And preprocessing the training sample to obtain sample data (X1, X2 and X3) and a corresponding disability transfer prediction label. Wherein X1 is age, X2 is gender, and X3 is health status (healthy/incapacitated). The predictive label may be a value indicative of the transition of an elderly person from one state of health to another, such as Y Health → disability =1 indicates a shift from healthy to incapacitated.
z＝β 0 +β 1 X 1 +β 2 X 2 +β 3 X 3 。
Wherein, pr (Y) Health → disability = 1) means the probability of the care-receiver transitioning from health to disability, β 0 、β 1 、β 2 And beta 3 Are regression parameters. The computer equipment inputs preprocessed sample data into an initial model to be trained to obtain an intermediate prediction result, calculates a difference value between the intermediate prediction result and the incapability transfer prediction label, adjusts regression parameters in the initial model according to the difference value until a trained model with the accuracy reaching a threshold value is obtained, and takes the trained model as an incapability transfer model.
When the computer device needs to determine the care strategy change rate of the caregivers in each care group based on the care strategy reallocation nodes, the simulation parameters of the care strategy reallocation nodes can be configured as a pre-trained care strategy adjustment model. Subsequently, the computer device can respectively calculate the care strategy change rate of each care-receiver according to the care strategy adjustment model. The care strategy adjustment model may also be a pre-trained machine learning model, such as a logistic regression model.
Each care strategy may be transferred to any other care strategy. The care strategy adjustment model employed when the prediction accuracy is different may be different. When the prediction precision is the first precision, a corresponding care strategy adjustment model exists between any two care strategies, for example, a model transferred from the care strategy i to the care strategy j can be referred to as Prij. And taking the data of the party to be nursed transferred between every two nursing strategies as corresponding training samples of the nursing strategy adjustment model Prij to obtain the prediction labels corresponding to a plurality of groups of sample data (X1, X2, X3 and X4). Wherein X1 is age, X2 is gender, X3 is health status, and X4 is current care strategy. The care strategy adjustment prediction label may be a characteristic value corresponding to the care-recipient being shifted from one care strategy to another, such as Yij =1 representing a shift from care strategy i to care strategy j.
z＝β 0 +β 1 X 1 +β 2 X 2 +β 3 X 3 +β 4 X 4 。
Wherein, prij (Y) ij = 1) means the probability of a care-giver transitioning from care strategy i to care strategy j, β 0 、β 1 、β 2 、β 3 And beta 4 Are regression parameters. The computer equipment inputs the preprocessed sample data into the initial model to be trained to obtain an intermediate prediction result, calculates a difference value between the intermediate prediction result and the nursing strategy adjustment prediction label, adjusts regression parameters in the initial model according to the difference value until a trained model with the accuracy reaching a threshold value is obtained, and takes the trained model as a nursing strategy adjustment model.
When the prediction precision is the second precision, the initial model to be trained may be a multi-class Logistic Regression (MLR) model:
Pr(Y i ＝1)+Pr(Y i ＝2)+……+Pr(Y i ＝8)＝1
where i and j are each a numerical value representing one care strategy. i =1,2 \ 82308, representing an initial care strategy. j =1,2 \ 82308, representing post-transfer care strategies (denoted as target care strategies). For example, 1 represents no care required; 2 did not receive any care and so on, 8 was dead. In (X1, X2, X3, X4), X1 is age, X2 is gender, X3 is health status, and X4 is current care strategy.
When the simulation parameters are estimated, because the long-term care forms involved are more when the prediction precision requirement is higher, the model training efficiency can be improved based on a multi-classification model compared with a mode of solving the simulation parameters one by one for each logistic regression model. Different simulation parameter estimation methods are set according to different precision requirements, so that the simulation efficiency can be improved, and different prediction precision requirements can be met.
When a long-term care demand forecasting request for a carereceiver in the target area in a future preset period is received, a simulation model can be allocated to the forecasting request based on the long-term care strategy. For example, if the long-term care needs of the care-receiver in the next 10 years of Shanghai are to be predicted, the data of the care-receiver in the current month of Shanghai (which may be the third-party data obtained from the relevant organization) can be input into the simulation model at the beginning of each month, and the prediction result in the current month can be obtained. In one embodiment, the simulation model may preset a plurality of statistical analysis time nodes, such as annual demand population counted each year to obtain annual long-term care demand population of the target area. The target area year long term care needs population may be accumulated over 12 months of the year.
As another example, the validity of a new policy may be verified based on a simulation model. Specifically, when a new policy is issued, one or more simulation parameters in the corresponding simulation model may change, the data of the nursed party obtained from the third party may also change, and the effectiveness of the policy may be determined according to the change of the population in which "there is a nursing demand but no nursing is obtained all the time" based on the changed data of the nursed party and the prediction of the simulation model.
The traditional approach typically predicts the number of people with long-term care needs by multiplying the disability distribution rate by the total number of people. The population calculated in this way is a static value at a fixed time node.
In the long-term care strategy allocation method, when long-term care demand prediction is carried out based on a pre-constructed simulation model, the initial care strategy of a care-receiver is determined by combining the disability distribution rate of the care-receiver, and the initial care strategy can be dynamically adjusted by combining the disability transfer rate and the care strategy change rate of the care-receiver, so that the demand change tracks of a plurality of continuous time nodes can be dynamically predicted, the long-term care demand prediction accuracy is improved, and a reliable basis is provided for implementation and adjustment of a care policy.
In one embodiment, obtaining an initial model corresponding to each care strategy to be trained comprises: converting the care strategy in the health track information into a target care strategy according to the expected calculation precision; and acquiring an initial model to be trained corresponding to the nursing strategy of each target.
And respectively training the corresponding nursing strategy distribution model by the computer equipment aiming at each nursing strategy. In other words, there are a plurality of care strategy distribution models. The care strategy distribution model may also be a pre-trained machine learning model, such as a logistic regression model or the like. Training samples are obtained by means of census, questionnaire and the like. According to different nursing strategy division granularities, the precision of the long-term nursing demand prediction result can be divided into a first precision and a second precision.
In one embodiment, training the initial model of the respective care strategy based on the sample data comprises: determining a sample label of corresponding sample data according to whether the care strategy in the sample data is consistent with the care strategy corresponding to the initial model; the sample data also includes identity attributes of the party to which the sample is being cared for; extracting identity characteristics of identity attributes and health characteristics of health tracks; inputting the identity characteristics and the health characteristics into the initial model of the corresponding nursing strategy to obtain a predicted nursing strategy; and adjusting parameters of the initial model and continuing training according to the difference between the predicted care strategy and the sample label until the training ending condition is met, and ending the training to obtain a care strategy distribution model.
When the prediction precision is the first precision, the training process of each care strategy distribution model is as follows: the computer equipment preprocesses the training samples to obtain nursing prediction labels corresponding to a plurality of groups of sample data (X1, X2, X3). The sample data comprises identity characteristic age X1 and gender X2 of the cared party and health characteristic health state X3 of the cared party. The care prediction label may be a value that corresponds to a characterization of a care strategy employed by the care giver, such as Y Does not require nursing =1 indicates that the care strategy adopted is no need for care; y is Home care =1 denotes employedThe care strategy is home care and so on.
The initial model to be trained may be a logistic regression model:
z＝β 0 +β 1 X 1 +β 2 X 2 +β 3 X 3
wherein, pr (Y) i = 1) means the probability that the care-giver needs a care strategy i, i is death, no care needed, no care taken, home care, or institutional care. Beta is a beta 0 、β 1 、β 2 And beta 3 Are regression parameters. The computer equipment inputs preprocessed sample data into an initial model to be trained to obtain an intermediate prediction result, calculates a difference value between the intermediate prediction result and a nursing prediction label, adjusts regression parameters in the initial model according to the difference value until a trained model with the accuracy rate reaching a threshold is obtained, and takes the trained model as a nursing strategy distribution rate model.
When the prediction accuracy is the second accuracy, the initial model to be trained may be a logistic regression model:
z＝β 0 +β 1 X 1 +β 2 X 2 +β 3 X 3
wherein, pr (Y) j = 1) means probability that care strategy j is needed by care-recipient, i is death, no care needed, no care taken, home care, institutional care, personal care, volunteer care, or community care. Beta is a 0 、β 1 、β 2 And beta 3 Are regression parameters. Inputting the preprocessed sample data into the initial model to be trained by the computer equipment to obtain an intermediate prediction result, calculating the difference value between the intermediate prediction result and the nursing prediction label, and performing regression on regression parameters in the initial model according to the difference valueAnd adjusting the number until a trained model with the accuracy reaching a threshold is obtained, and taking the trained model as a nursing strategy distribution rate model.
In the embodiment, different nursing strategy distribution rate models are set according to different precision requirements, so that different prediction precision requirements can be met.
In one embodiment, obtaining an initial model corresponding to each care strategy to be trained comprises: converting the care strategies in the health tracks into target care strategies according to the expected calculation precision; and acquiring an initial model to be trained corresponding to the nursing strategy of each target.
The recording modes of the care strategies in the training samples obtained by the modes of census, questionnaire and the like may be various. For example, the "not cared" care strategy may correspond to a record in the training sample of "unattended", "not well-attended", etc. And the computer equipment converts the fields for the care strategies in the training samples into the same care strategy name field based on a preset semantic recognition model.
In the embodiment, the care strategies in the training samples are subjected to standardization processing, so that the follow-up care demand prediction with different precisions based on different care strategy partition granularities is facilitated.
It should be understood that, although the steps in the flowchart of fig. 2 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 2 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 4, there is provided a long-term care strategy assigning apparatus including: a model calling module 402, a group partitioning module 404, a care strategy assignment module 406, and a care strategy adjustment module 408, wherein:
the model calling module 402 is used for acquiring information of a plurality of nursed parties and inputting the information of the plurality of nursed parties into the long-term care strategy distribution simulation model; the long-term care strategy distribution simulation model comprises a group division node, a care strategy distribution node and a care strategy redistribution node.
The group dividing module 404 is configured to divide the plurality of care recipients into groups by the group dividing node, so as to obtain a plurality of care groups.
And the care strategy allocation module 406 is configured to obtain a pre-configured care strategy distribution model corresponding to each care group through the care strategy allocation node, calculate the care strategy distribution rates of different care recipients in the corresponding care groups based on the care strategy distribution model, and determine an initial care strategy corresponding to each care recipient according to the care strategy distribution rates.
The nursing policy adjusting module 408 is configured to obtain a pre-configured disability transfer model and a nursing policy adjusting model corresponding to each nursing group through the nursing policy redistribution node, calculate disability transfer rates of different care recipients in the corresponding nursing group based on the disability transfer model, calculate nursing policy change rates of different care recipients in the corresponding nursing group based on the nursing policy adjusting model, and adjust an initial nursing policy corresponding to each care recipient according to the disability transfer rates and the nursing policy change rates.
In one embodiment, the apparatus further comprises a model building module 410 for obtaining a long-term care strategy allocation schematic diagram; the long-term care strategy distribution schematic diagram comprises a group division node, a care strategy distribution node and a care strategy redistribution node; determining simulation parameters for instructing the group partitioning node to perform group partitioning on the plurality of nursed parties; determining simulation parameters for instructing a care strategy distribution node to distribute an initial care strategy for a care-receiver in each group; and determining simulation parameters for indicating the nursing strategy redistribution nodes to adjust the initial nursing strategies corresponding to the nursed parties in each group according to the disability transfer rate and the nursing strategy change rate of the nursed parties, so as to obtain a long-term nursing strategy distribution simulation model.
In one embodiment, the care policy assignment module 406 is further configured to obtain sample data; the sample data comprises health tracks of a plurality of sample cared parties; the health track includes a care strategy; acquiring an initial model corresponding to each care strategy to be trained; training the initial model of the corresponding nursing strategy based on the sample data to obtain a nursing strategy distribution model corresponding to each nursing strategy.
In one embodiment, the care strategy allocation module 406 is further configured to convert the care strategies in the health track information into targeted care strategies according to a desired calculation accuracy; and acquiring an initial model to be trained corresponding to the care strategy of each target.
In one embodiment, when the calculation accuracy is a first accuracy, the care strategy of the goal includes no need for care, no care received, home care, and institutional care; when the calculation accuracy is a second accuracy, the care strategies of the target include no care, informal care, institutional care, private care, volunteer care, and community care.
In one embodiment, the nursing policy allocating module 406 is further configured to determine a sample tag of corresponding sample data according to whether the nursing policy in the sample data is consistent with the nursing policy corresponding to the initial model; the sample data also includes identity attributes of the party to which the sample is being cared for; extracting identity characteristics of identity attributes and health characteristics of health tracks; inputting the identity characteristics and the health characteristics into the initial model of the corresponding care strategy to obtain a predicted care strategy; and adjusting parameters of the initial model and continuing training according to the difference between the predicted care strategy and the sample label until the training ending condition is met, and ending the training to obtain a care strategy distribution model.
For specific definition of the long-term care strategy allocation means, reference may be made to the above definition of the long-term care strategy allocation method, which is not described herein again. The various modules in the long-term care strategy dispensing apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and the computer program to run on the non-volatile storage medium. The database of the computer device is used for storing the identity attribute and the health track data of the nursed party needing nursing demand prediction. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a long-term care strategy allocation method.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for assigning a long-term care policy as provided in any one of the embodiments of the present application.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct Rambus Dynamic RAM (DRDRAM), and Rambus Dynamic RAM (RDRAM), among others.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A long-term care policy assignment method, the method comprising:
acquiring information of a plurality of nursed parties, and inputting the information of the plurality of nursed parties into a long-term care strategy distribution simulation model; the long-term care strategy distribution simulation model comprises a group division node, a care strategy distribution node and a care strategy redistribution node;
the group division node divides a plurality of nursed parties into groups to obtain a plurality of nursing groups;
acquiring a pre-configured care strategy distribution model corresponding to each care group through the care strategy distribution node, calculating the care strategy distribution rate of each care-receiving party in the care group based on the care strategy distribution model, and determining an initial care strategy corresponding to each care-receiving party according to the care strategy distribution rate; the nursing strategy distribution rate is the probability of the nursing strategy adopted by the nursed party;
acquiring a pre-configured disability transfer model and a nursing strategy adjustment model corresponding to each nursing group through the nursing strategy redistribution node, calculating a disability transfer rate of each nursed party in the nursing group based on the disability transfer model, calculating a nursing strategy change rate of each nursed party in the nursing group based on the nursing strategy adjustment model, and adjusting an initial nursing strategy of each nursed party according to the disability transfer rate and the nursing strategy change rate; the disability transfer rate is the probability of a cared party transferring from health to disability.
2. The method of claim 1, further comprising:
acquiring a long-term care strategy distribution schematic diagram; the long-term care strategy distribution schematic diagram comprises a group division node, a care strategy distribution node and a care strategy redistribution node;
determining simulation parameters for instructing the group partitioning node to perform group partitioning on the plurality of nursed parties;
determining simulation parameters for instructing a care strategy distribution node to distribute an initial care strategy for a care-receiver in each group;
and determining simulation parameters for indicating the nursing strategy redistribution nodes to adjust the initial nursing strategies corresponding to the nursed parties in each group according to the disability transfer rate and the nursing strategy change rate of the nursed parties, so as to obtain a long-term nursing strategy distribution simulation model.
3. The method of claim 1, wherein the step of training the care strategy distribution model comprises:
acquiring sample data; the sample data comprises health tracks of a plurality of sample caregivers; the health trajectory comprises a care strategy;
acquiring an initial model corresponding to each care strategy to be trained;
and training the initial model of the corresponding care strategy based on the sample data to obtain a care strategy distribution model corresponding to each care strategy.
4. The method of claim 3, wherein the obtaining an initial model corresponding to each care strategy to be trained comprises:
converting the care strategy in the health track information into a target care strategy according to the expected calculation precision;
and acquiring an initial model to be trained corresponding to the care strategy of each target.
5. The method of claim 4, further comprising:
when the calculation precision is a first precision, the care strategies of the target comprise no need of care, no care, home care and institution care;
when the calculation precision is a second precision, the care strategies of the target comprise no care, informal care, institution care, private care, volunteer care and community care.
6. The method of claim 3, wherein training an initial model of a respective care strategy based on the sample data comprises:
determining a sample label of corresponding sample data according to whether the care strategy in the sample data is consistent with the care strategy corresponding to the initial model; the sample data further comprises identity attributes of the party to which the sample is being cared for;
extracting the identity characteristics of the identity attributes and the health characteristics of the health tracks;
inputting the identity characteristics and the health characteristics into the initial model of the corresponding care strategy to obtain a predicted care strategy;
and adjusting parameters of the initial model and continuing training according to the difference between the predicted nursing strategy and the sample label until the training ending condition is met, and ending the training to obtain a nursing strategy distribution model.
7. A long-term care strategy allocation apparatus, the apparatus comprising:
the model calling module is used for acquiring the information of a plurality of nursed parties and inputting the information of the plurality of nursed parties into a long-term care strategy distribution simulation model; the long-term care strategy distribution simulation model comprises a group division node, a care strategy distribution node and a care strategy redistribution node;
the group division module is used for carrying out group division on a plurality of nursed parties through the group division node to obtain a plurality of nursing groups;
the nursing strategy distribution module is used for acquiring a nursing strategy distribution model corresponding to each pre-configured nursing group through the nursing strategy distribution node, calculating the nursing strategy distribution rate of different nursed parties in the corresponding nursing group based on the nursing strategy distribution model, and determining an initial nursing strategy corresponding to each nursed party according to the nursing strategy distribution rate; the nursing strategy distribution rate is the probability of the nursing strategy adopted by the nursed party;
the nursing strategy adjusting module is used for acquiring a pre-configured disability transfer model and a nursing strategy adjusting model corresponding to each nursing group through the nursing strategy redistribution node, calculating disability transfer rates of different nursed parties in the corresponding nursing group based on the disability transfer model, calculating nursing strategy change rates of different nursed parties in the corresponding nursing group based on the nursing strategy adjusting model, and adjusting an initial nursing strategy corresponding to each nursed party according to the disability transfer rates and the nursing strategy change rates; the disability transfer rate is the probability of a cared party transferring from health to disability.
8. The apparatus of claim 7, further comprising a model building module for obtaining a long-term care strategy assignment schematic; the long-term care strategy distribution schematic diagram comprises a group division node, a care strategy distribution node and a care strategy redistribution node; determining simulation parameters for instructing the group partitioning node to perform group partitioning on the plurality of nursed parties; determining simulation parameters for instructing a care strategy distribution node to distribute initial care strategies for caregivers in each group; and determining simulation parameters for indicating the nursing strategy redistribution node to adjust the initial nursing strategy corresponding to the nursed party in each group according to the disability transfer rate and the nursing strategy change rate of the nursed party, so as to obtain a long-term nursing strategy distribution simulation model.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor when executing the computer program performs the steps of the method according to any of claims 1 to 6.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
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