CN116612870A - General surgery patient data management method - Google Patents
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- 206010046996 Varicose vein Diseases 0.000 description 2
- 206010000050 Abdominal adhesions Diseases 0.000 description 1
- 206010003011 Appendicitis Diseases 0.000 description 1
- 239000010754 BS 2869 Class F Substances 0.000 description 1
- 239000010755 BS 2869 Class G Substances 0.000 description 1
- 238000001266 bandaging Methods 0.000 description 1
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Abstract
The invention relates to the technical field of patient data processing, and discloses a general surgery patient data management method, which comprises the following steps: step 1, collecting current patient data and historical patient data; step 2, generating a nursing capability library of a nurse based on the collected historical patient data; step 3, constructing a management strategy matrix; step 4, generating ID vectors for patients and nurses; generating a text vector for the patient's disease type and nurse's care entry; inputting a neural network model; step 5, initializing a population, wherein individuals in the initialized population represent a management strategy matrix; iterating the initialized population through a genetic algorithm, obtaining a target population after iterating K times, and selecting a management strategy matrix represented by an individual from the target population as a nursing scheduling result; the invention comprehensively considers the matching degree of nursing ability of nurses and diseases of patients, and automatically generates an optimized nursing management strategy by estimating the satisfaction degree of the patients.
Description
Technical Field
The invention relates to the technical field of patient data processing, in particular to a general surgery patient data management method.
Background
Most surgical patients are patients with inconvenient actions, the nursing work management arrangement in the prior art generally only considers the fatigue degree of nursing staff and the economic benefit of hospitals, the satisfaction degree of patients on nursing cannot be considered, in addition, because different patients have different illness states, nursing services are needed in a targeted manner, different nurses have different skill levels, the nursing work of different illness states cannot be met, and only the nursing work of nurses and patients can be reasonably arranged, the quality and the accuracy of the nursing services can be ensured; the disease type corresponds to the care effort.
Disclosure of Invention
The invention provides a general surgery patient data management method, which solves the technical problem that the management arrangement of nursing work of a surgical patient only considers the fatigue degree of nursing staff and the economic benefit of a hospital in the related technology.
The invention provides a general surgery patient data management method, which comprises the following steps: step 1, collecting current patient data and historical patient data, wherein the current patient data and the historical patient data comprise a patient ID and a disease type of a patient, and the historical patient data further comprise nursing contents of the patient, corresponding nursing nurses and nursing satisfaction levels.
Step 2, generating a nursing capability library of nurses based on the collected historical patient data, wherein the nursing capability library comprises a plurality of nursing items, and one nursing item corresponds to one disease type.
Step 3, constructing a management strategy matrix; the elements in the management policy matrix are real numbers, the integer bits of the real numbers are one bit, corresponding to shifts, the decimal digits of the real numbers are six bits, corresponding to the IDs of three patients.
Step 4, generating a single-heat code for patients and nurses in the generated management strategy matrix, and then generating an ID vector through a skip model; a text vector is generated for the patient's disease type and the nurse's care entry.
Inputting the ID vector and the text vector into a neural network model, wherein the neural network model comprises a linear layer, a first hiding layer and a full-connection layer, the linear layer is used for linearly transforming the ID vector into the text vector with the same dimension, the first hiding layer inputs the text vector and the transformed text vector, the output of the first hiding layer inputs the full-connection layer, and the full-connection layer outputs a label corresponding to the nursing satisfaction level; the neural network model is trained based on the management policy matrix generated from the historical patient data.
And 5, initializing a population, wherein individuals in the initialized population represent a management strategy matrix, and the elements of the management strategy matrix of the individuals in the initialized population are randomly generated.
And iterating the initialized population through a genetic algorithm, obtaining a target population after iterating K times, and selecting a management strategy matrix represented by an individual from the target population as a nursing scheduling result.
After each iteration, the generated management strategy matrix is processed and then input into a neural network model, and only individuals with the set nursing satisfaction level or more are reserved to enter the next iteration.
Further, the disease type of the patient corresponds to different disease care content.
Further, if a nurse's care-ability library includes a care entry corresponding to a disease type, that nurse is indicated to have the care-ability of the disease type.
Further, the element of the ith row and the jth column in the management policy matrix represents the care content of the ith data on the ith day; the care content includes the shift and the patient's ID.
Further, the patient's disease type and nurse care items are text data that are processed by conventional text vectorized representations to generate text vectors.
The first hidden layer is provided with a plurality of layers, and the calculation formula of the first hidden layer of the first layer is as follows:
。
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>And->Respectively representIntermediate vector of ith patient and ith nurse of layer i, +.>Represents the set of layer 1 +.>Is the j-th vector of>Represents the set of layer 1 +.>Is the j-th vector of>And->Vector sets related to the ith patient and the ith nurse of layer 1-1, respectively,/->And->Intermediate vectors of the ith patient and the ith nurse are respectively contained; />Andeach comprising a text vector and an intermediate vector, N and M each representing +.>And->Vector quantity in>And->Weight parameters and bias, respectively, representing the first hidden layer of the first layer, +.>Representing a sigmoid activation function.
When l=1, the number of the cells,，/>，/>ID vector representing the jth patient, < >>An ID vector indicating the jth nurse.
Further, the historical patient data includes scheduling data, and the generated management policy matrix is unique.
Further, the random generation of the elements of the management policy matrix satisfies the following condition: the nurse's care item must correspond to the type of illness of the patient it is nursing.
The same nurse can only take one shift a day.
The patient corresponds to more than one nurse per shift.
At least one patient is nursed by one nurse in one shift.
Further, the individuals in the target population are ranked according to the nursing satisfaction level from high to low, and the management strategy matrix represented by the first U individuals is selected as a recommendation result to be selected by a manager.
Further, the labels corresponding to the nursing satisfaction levels comprise three nursing satisfaction levels corresponding to the first, second and third levels respectively, and the higher the level is, the higher the nursing satisfaction is.
The invention has the beneficial effects that: the invention comprehensively considers the matching degree of nursing ability of nurses and diseases of patients, and automatically generates an optimized nursing management strategy by estimating the satisfaction degree of the patients.
Drawings
Fig. 1 is a flow chart of a general surgery patient data management method of the present invention.
Detailed Description
The subject matter described herein will now be discussed with reference to example embodiments. It is to be understood that these embodiments are merely discussed so that those skilled in the art may better understand and implement the subject matter described herein and that changes may be made in the function and arrangement of the elements discussed without departing from the scope of the disclosure herein. Various examples may omit, replace, or add various procedures or components as desired. In addition, features described with respect to some examples may be combined in other examples as well.
As shown in fig. 1, a general surgery patient data management method includes the steps of: step 1, collecting current patient data and historical patient data, wherein the current patient data and the historical patient data comprise a patient ID and a disease type of a patient, and the historical patient data further comprise nursing contents of the patient, corresponding nursing nurses and nursing satisfaction levels.
The disease type of the patient corresponds to different disease nursing contents, for example, the disease type of the patient is acute appendicitis, the patient adopts a semi-lying position after the blood pressure of the patient is stable except for conventional surgical nursing after operation, and a nurse is required to take proper food after exhausting, and the nurse is required to urge the patient to take appropriate action to get out of the bed the next day after operation of the patient, so that intestinal adhesion of the patient is prevented.
For example, the type of disease of the patient is varicose vein operation of the lower limb, the varicose vein of the patient is required to be bound by elastic bandaging, the affected limb is lifted by 15-20 cm, and a nurse encourages the patient to take appropriate movement in the next day after the operation of the patient.
Step 2, generating a nursing capability library of nurses based on the collected historical patient data, wherein the nursing capability library comprises a plurality of nursing items, and one nursing item corresponds to one disease type.
I.e. if a care item corresponding to a disease type is included in the care capability repository, this indicates that the nurse has care capabilities for that disease type.
And 3, constructing a management strategy matrix.
The elements in the management policy matrix are real numbers, the integer bits of the real numbers are one bit, the decimal digits of the real numbers are six bits corresponding to the shift, and the IDs corresponding to three patients (the IDs of the patients are represented by two digits, and are represented by a default value of "00" if there is no corresponding patient).
The element of the ith row and the jth column in the management policy matrix represents the nursing content of the ith data on the ith day; the care content includes the shift and the patient's ID.
The shift of nurses in one day is: class A (7:00-15:00), class B (8:00-16:00), class C (9:30-19:00), class D (14:00-22:00), class E (15:00-23:00), class F (16:00-24:00), class G (18:00-1:00) and class H (24:00-8:00).
And 4, generating a single-heat code for patients and nurses in the generated management strategy matrix, and then generating an ID vector through a skip model.
A text vector is generated for the patient's disease type and the nurse's care entry.
In one embodiment of the invention, the patient's disease type and nurse care items are text data that are processed by conventional text vectorized representations to generate text vectors.
The ID vector and the text vector are input into a neural network model, the neural network model comprises a linear layer, a first hiding layer and a full connection layer, wherein the linear layer is used for linearly transforming the ID vector to the text vector with the same dimension, the first hiding layer inputs the text vector and the transformed text vector, the output of the first hiding layer inputs the full connection layer, and the full connection layer outputs a label corresponding to the nursing satisfaction level.
The first hidden layer is provided with a plurality of layers, and the calculation formula of the first hidden layer of the first layer is as follows:
。
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>And->Intermediate vectors of the ith patient and the ith nurse of the first layer, respectively,/->Represents the set of layer 1 +.>Is the j-th vector of>Represents the set of layer 1 +.>Is the j-th vector of>And->Vector sets related to the ith patient and the ith nurse of layer 1-1, respectively,/->And->Intermediate vectors of the ith patient and the ith nurse are respectively contained; />And->Each comprising a text vector and an intermediate vector, N and M each representing +.>And->The number of vectors in (1) is such that a nurse corresponding to a vector indicating that the vector is related to a patient nurses the patient or that the disease type corresponding to the vector belongs to the patient, and a nurse corresponding to a vector indicating that the patient corresponding to the vector is nursed by the nurse or that the nursing item corresponding to the vector belongs to the nurse. />And->Weight parameters and bias, respectively, representing the first hidden layer of the first layer, +.>Representing a sigmoid activation function.
When l=1, the number of the cells,，/>，/>ID vector representing the jth patient, < >>An ID vector indicating the jth nurse.
The management policy matrix generated based on the historical patient data trains the neural network model, and is unique because the historical patient data contains the data of the shifts, and the management policy matrix generated based on the current patient data is random and is not unique.
In one embodiment of the invention, the labels corresponding to the nursing satisfaction levels comprise three nursing satisfaction levels respectively corresponding to a first nursing satisfaction level, a second nursing satisfaction level and a third nursing satisfaction level, the higher the nursing satisfaction level is, and the nursing satisfaction label during training can be obtained by interviewing the patient before discharge.
And 5, initializing a population, wherein individuals in the initialized population represent a management strategy matrix, and the elements of the management strategy matrix of the individuals in the initialized population are randomly generated.
The random generation satisfies the following conditions: the nurse's care item must correspond to the type of illness of the patient it is nursing.
The same nurse can only take one shift a day.
The patient corresponds to more than one nurse per shift.
At least one patient is nursed by one nurse in one shift.
And iterating the initialized population through a genetic algorithm, obtaining a target population after iterating K times, and selecting a management strategy matrix represented by an individual from the target population as a nursing scheduling result.
After each iteration, the generated management strategy matrix is processed and then input into a neural network model, and only individuals with the set nursing satisfaction level or more are reserved to enter the next iteration.
In one embodiment of the invention, individuals in the target population are ranked according to the care satisfaction level from high to low, and the management policy matrix represented by the first U individuals is selected as the recommendation result to be selected by the manager.
The embodiment has been described above with reference to the embodiment, but the embodiment is not limited to the above-described specific implementation, which is only illustrative and not restrictive, and many forms can be made by those of ordinary skill in the art, given the benefit of this disclosure, are within the scope of this embodiment.
Claims (10)
1. A method for managing patient data in general surgery, comprising the steps of: step 1, collecting current patient data and historical patient data, wherein the current patient data and the historical patient data comprise a patient ID and a disease type of a patient, and the historical patient data further comprise nursing contents of the patient, corresponding nursing nurses and nursing satisfaction levels;
step 2, generating a nursing capability library of nurses based on the collected historical patient data, wherein the nursing capability library comprises a plurality of nursing items, and one nursing item corresponds to one disease type;
step 3, constructing a management strategy matrix; the elements in the management strategy matrix are real numbers, the integer bits of the real numbers are one bit, corresponding to shifts, the decimal digits of the real numbers are six bits, corresponding to IDs of three patients;
step 4, generating a single-heat code for patients and nurses in the generated management strategy matrix, and then generating an ID vector through a skip model; generating a text vector for the patient's disease type and nurse's care entry;
inputting the ID vector and the text vector into a neural network model, wherein the neural network model comprises a linear layer, a first hiding layer and a full-connection layer, the linear layer is used for linearly transforming the ID vector into the text vector with the same dimension, the first hiding layer inputs the text vector and the transformed text vector, the output of the first hiding layer inputs the full-connection layer, and the full-connection layer outputs a label corresponding to the nursing satisfaction level; training a neural network model based on a management policy matrix generated from historical patient data;
step 5, initializing a population, wherein individuals in the initialized population represent a management strategy matrix, and elements of the management strategy matrix of the individuals in the initialized population are randomly generated;
iterating the initialized population through a genetic algorithm, obtaining a target population after iterating K times, and selecting a management strategy matrix represented by an individual from the target population as a nursing scheduling result;
after each iteration, the generated management strategy matrix is processed and then input into a neural network model, and only individuals with the set nursing satisfaction level or more are reserved to enter the next iteration.
2. The method of claim 1, wherein the patient's disease type corresponds to different disease care content.
3. A general surgery patient data management method according to claim 1, wherein if a nurse's care-ability library includes a care item corresponding to a disease type, it indicates that the nurse has the care ability of the disease type.
4. The general surgery patient data management method according to claim 1, wherein the element of the ith row and the jth column in the management policy matrix represents care content of the ith data on the ith day; the care content includes the shift and the patient's ID.
5. The general surgery patient data management method according to claim 1, wherein the patient's disease type and nurse care items are text data, and the text vector is generated by processing the text data by a conventional text vectorized representation method.
6. The general surgery patient data management method according to claim 1, wherein the first hidden layer is provided with a plurality of layers, and the first hidden layer of the first layer has a calculation formula as follows:；
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>And->Intermediate vectors of the ith patient and the ith nurse of the first layer, respectively,/->Represents the set of layer 1 +.>Is the j-th vector of>Represents the set of layer 1 +.>Is the j-th vector of>And->Vector sets related to the ith patient and the ith nurse of layer 1-1, respectively,/->And->Intermediate vectors of the ith patient and the ith nurse are respectively contained; n and M each represent->And->Vector quantity in>And->Weight parameters and bias, respectively, representing the first hidden layer of the first layer, +.>Representing a sigmoid activation function;
when l=1, the number of the cells,，/>，/>ID vector representing the jth patient, < >>An ID vector indicating the jth nurse.
7. The general surgery patient data management method according to claim 1, wherein the historical patient data includes shift data, and the generated management policy matrix is unique.
8. The general surgery patient data management method according to claim 1, wherein the random generation of the elements of the management policy matrix satisfies the following condition:
the nurse's care item must correspond to the type of illness of the patient it is nursing;
the same nurse can only arrange one shift in one day;
the patient corresponds to more than one nurse in each shift;
at least one patient is nursed by one nurse in one shift.
9. The general surgery patient data management method according to claim 1, wherein the individuals in the target population are ranked from high to low according to the nursing satisfaction level, and the management policy matrix represented by the first U individuals is selected as the recommendation result by the manager.
10. The general surgery patient data management method according to claim 1, wherein the labels corresponding to the nursing satisfaction levels include three nursing satisfaction levels corresponding to one, two, and three levels, respectively, and the higher the level, the higher the nursing satisfaction.
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