A method, system and medium for assisting selection of nursing plans for orthopedic patients

Abstract

The present invention relates to the technical field of nursing plan auxiliary selection, and specifically to a nursing plan auxiliary selection method, system and medium for orthopedic patients. The method first obtains the similarity of the comprehensive medical conditions of the orthopedic patient to be analyzed and the historical orthopedic patients; based on the similarity of the basic indicator data sets of the orthopedic patient to be analyzed and the historical orthopedic patients, obtains the similarity of the basic information of the orthopedic patient to be analyzed and the historical orthopedic patients; based on the comprehensive medical condition similarity and the basic information similarity, obtains the nursing adaptability of the orthopedic patient to be analyzed and the historical orthopedic patients; based on the nursing adaptability and the nursing plan of the historical orthopedic patient, selects the nursing plan for the orthopedic patient to be analyzed. The present invention uses nursing adaptability to more accurately reflect the degree of adaptation of the nursing plans of the orthopedic patient to be analyzed and the historical orthopedic patients, thereby improving the accuracy of selecting the nursing plan for the orthopedic patient to be analyzed.

Description

Technical Field

[0001] The present invention relates to the technical field of nursing plan auxiliary selection, and in particular to a nursing plan auxiliary selection method, system and medium for orthopedic patients. Background Art

[0002] Orthopedic patients often experience discomfort symptoms such as pain and swelling. Professional care can help patients alleviate these symptoms. The care plan for orthopedic patients is of great significance in the medical process. The care plan is directly related to the patient's recovery speed, quality of life, and prognosis.

[0003] Since patient groups with similar disease types and basic information often adopt similar nursing plans, the existing technology assists the orthopedic patients to be analyzed in selecting nursing plans by analyzing the similarities in the disease and basic information of the orthopedic patients to be analyzed and the historical orthopedic patients. However, when assisting the orthopedic patients to be analyzed in selecting nursing plans, the existing technology has the problem that the nursing adaptability is difficult to accurately reflect the degree of adaptability of the nursing plans for the orthopedic patients to be analyzed and the historical orthopedic patients due to the complexity and diversity of orthopedic disease information, resulting in inaccurate auxiliary selection of nursing plans. Summary of the invention

[0004] In order to solve the problem that the orthopedic condition information in the prior art is complex and diverse, which leads to the problem that the nursing adaptability is difficult to accurately reflect the degree of adaptability of the nursing plan for the orthopedic patient to be analyzed and the historical orthopedic patients, resulting in inaccurate auxiliary selection of nursing plans, the purpose of the present invention is to provide a method, system and medium for auxiliary selection of nursing plans for orthopedic patients. The technical solutions adopted are as follows:

[0005] A method for assisting in selecting a nursing plan for orthopedic patients, the method comprising:

[0006] Obtain the basic indicator data set and disease condition indicator data set of the orthopedic patients to be analyzed; at the same time, obtain the basic indicator data set of historical orthopedic patients, the disease condition indicator data set at each historical diagnosis, and the nursing plan;

[0007] According to the similarity between the condition indicator data set of the orthopedic patient to be analyzed and the condition indicator data sets of each historical diagnosis of the historical orthopedic patient, the initial similarity of the condition between the condition indicator data set of the orthopedic patient to be analyzed and the condition indicator data sets of each historical diagnosis of the historical orthopedic patient is obtained; according to the change of the condition indicator data sets of the historical orthopedic patient in the continuous historical diagnosis, the confidence of the condition of the historical patient is obtained; according to the initial similarity of the condition between the condition indicator data set of the orthopedic patient to be analyzed and the condition indicator data sets of each historical diagnosis of the historical orthopedic patient, and the confidence of the condition of the historical patient, the comprehensive similarity of the condition of the orthopedic patient to be analyzed and the historical orthopedic patient is obtained;

[0008] According to the similarity of the basic indicator data sets of the orthopedic patients to be analyzed and the historical orthopedic patients, the similarity of the basic information of the orthopedic patients to be analyzed and the historical orthopedic patients is obtained;

[0009] According to the comprehensive condition similarity and the basic information similarity, the nursing suitability of the orthopedic patient to be analyzed and the historical orthopedic patients is obtained; according to the nursing suitability and the nursing plans of the historical orthopedic patients, the nursing plan of the orthopedic patient to be analyzed is selected.

[0010] Furthermore, the method for obtaining the initial similarity of the disease condition includes:

[0011] In the disease condition indicator data set of the orthopedic patient to be analyzed and the disease condition indicator data set of the historical diagnosis of the historical orthopedic patient, according to the similarity of the two disease condition indicator data sets at the overall level, obtain the overall disease condition similarity parameters of the two disease condition indicator data sets;

[0012] According to the similarity of the two disease condition indicator data sets at the local level, obtaining the global similarity parameter of the two disease condition indicator data sets;

[0013] The overall condition similarity parameter and the global similarity parameter are integrated to obtain the initial condition similarity between the condition indicator data set of the orthopedic patient to be analyzed and the condition indicator data set of each historical diagnosis of the historical orthopedic patient; the overall condition similarity parameter and the global similarity parameter are both positively correlated with the initial condition similarity.

[0014] Furthermore, the method for obtaining the overall disease condition similarity parameter includes:

[0015] The Jaccard correlation coefficient of the two disease condition indicator data sets is calculated to obtain the overall disease condition similarity parameter of the two disease condition indicator data sets.

[0016] Furthermore, the method for obtaining the global similarity parameter includes:

[0017] In the disease condition indicator data set of the orthopedic patients to be analyzed and the disease condition indicator data set of the historical diagnosis of the historical orthopedic patients, according to the similarity between the two disease condition indicator data under each disease condition indicator, obtain the local disease condition similarity factor of each disease condition indicator;

[0018] The local condition similarity factors of all condition indicators in the two condition indicator data sets are integrated to obtain the global similarity parameters of the two condition indicator data sets.

[0019] Furthermore, the method for obtaining the condition confidence level includes:

[0020] Obtaining a quantity confidence factor according to a quantity difference of the disease condition indicator data in the disease condition indicator data set corresponding to two consecutive historical diagnoses;

[0021] Obtaining a disease condition similarity factor according to similarities between two consecutive historical diagnoses corresponding to the disease condition indicator data sets;

[0022] The confidence level of the condition of the historical patient is obtained according to the quantity confidence factor and the condition similarity factor; the quantity confidence factor and the condition similarity factor are both positively correlated with the condition confidence level.

[0023] Furthermore, the method for obtaining the comprehensive disease condition similarity includes:

[0024] The initial similarity of the condition between the orthopedic patient to be analyzed and the historical orthopedic patient in the first historical diagnosis of the condition indicator data set is used as the basic initial similarity; the initial similarity of the condition between the orthopedic patient to be analyzed and the historical orthopedic patient in the second historical diagnosis of the condition indicator data set is used as the reference initial similarity;

[0025] According to the difference between the basic initial similarity and the reference initial similarity, the confidence of the condition of the historical orthopedic patients is adjusted to obtain the basic adjustment weight;

[0026] According to the basic adjustment weight and the basic initial similarity, the comprehensive condition similarity between the orthopedic patient to be analyzed and the historical orthopedic patients is obtained; the basic adjustment weight and the basic initial similarity are both positively correlated with the comprehensive condition similarity.

[0027] Furthermore, the method for obtaining the basic adjustment weight includes:

[0028] When the reference initial similarity is greater than the basic initial similarity, a positive correlation mapping is performed on the condition confidence to obtain a basic adjustment weight;

[0029] When the reference initial similarity is less than the basic initial similarity, negative correlation mapping is performed on the condition confidence to obtain a basic adjustment weight;

[0030] When the reference initial similarity is equal to the basic initial similarity, a fixed value of 1 is used as the basic adjustment weight.

[0031] Furthermore, the method for obtaining the basic information similarity includes:

[0032] In the basic indicator data set of the orthopedic patients to be analyzed and the historical orthopedic patients, the similarity between two basic indicator data is calculated under each basic indicator to obtain the basic local similarity of each basic indicator;

[0033] The basic local similarities of all basic indicators are combined to obtain the basic information similarity between the orthopedic patient to be analyzed and the historical orthopedic patients.

[0034] A nursing plan auxiliary selection system for orthopedic patients, the system comprising:

[0035] The data acquisition module is used to acquire the basic indicator data set and the disease indicator data set of the orthopedic patients to be analyzed; at the same time, the basic indicator data set of historical orthopedic patients, the disease indicator data set of each historical diagnosis, and the nursing plan;

[0036] The disease condition similarity analysis module is used to obtain the initial disease condition similarity between the disease condition indicator data set of the orthopedic patient to be analyzed and the disease condition indicator data sets of each historical diagnosis of the historical orthopedic patients according to the similarity between the disease condition indicator data set of the orthopedic patient to be analyzed and the disease condition indicator data sets of each historical diagnosis of the historical orthopedic patients; obtain the disease condition confidence of the historical patients according to the change of the disease condition indicator data sets of the historical orthopedic patients in the continuous historical diagnosis; obtain the comprehensive disease condition similarity between the orthopedic patient to be analyzed and the historical orthopedic patients according to the initial disease condition similarity between the disease condition indicator data set of the orthopedic patient to be analyzed and the disease condition indicator data sets of each historical diagnosis of the historical orthopedic patients, and the disease condition confidence of the historical patients;

[0037] A basic information similarity analysis module is used to obtain the similarity of basic information of the orthopedic patient to be analyzed and the historical orthopedic patients based on the similarity of the basic indicator data sets of the orthopedic patient to be analyzed and the historical orthopedic patients;

[0038] A plan selection module is used to obtain the nursing suitability of the orthopedic patient to be analyzed and the historical orthopedic patients according to the comprehensive condition similarity and the basic information similarity; and select the nursing plan for the orthopedic patient to be analyzed according to the nursing suitability and the nursing plans of the historical orthopedic patients.

[0039] A medium for assisting selection of nursing plans for orthopedic patients comprises a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and when the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor, any one of the steps of a method for assisting selection of nursing plans for orthopedic patients is implemented.

[0040] The present invention has the following beneficial effects:

[0041] In order to analyze the degree of adaptation of the actual nursing plan between historical orthopedic patients and orthopedic patients to be analyzed, it is necessary to analyze the similarity of the medical conditions first. Considering that the medical condition indicator data set reflects the orthopedic medical condition information such as fractures and bone cracks, joint diseases, and spinal diseases of orthopedic patients. The initial medical condition similarity is used to preliminarily reflect the similarity of the orthopedic conditions represented by the two medical condition indicator data sets; considering the complexity of orthopedic medical condition information, that is, under normal circumstances, the medical condition indicator data set of historical orthopedic patients can accurately reflect the actual orthopedic medical condition, and under abnormal circumstances, there are potential complications in historical orthopedic patients that have not been detected. At this time, the medical condition indicator data set is difficult to accurately reflect the actual orthopedic medical condition, and the medical condition confidence of the historical patients is obtained; the greater the medical condition confidence, the more accurately the medical condition indicator data of the historical patients can reflect the actual orthopedic medical condition, and the more reliable the nursing plan adopted. Finally, the comprehensive medical condition similarity of the orthopedic patients to be analyzed and the historical orthopedic patients is obtained, and the comprehensive medical condition similarity can more accurately reflect the similarity of the medical conditions between the historical orthopedic patients and the orthopedic patients to be analyzed.

[0042] Considering the nursing plans adopted by historical orthopedic patients with similar physical functions to the orthopedic patients to be analyzed, it is of reference to the orthopedic patients to be analyzed. For example, the elderly have similar physical functions, osteoporosis often occurs in the elderly, and the nursing needs are similar, so the nursing plans adopted by the elderly are similar. Obtain the similarity of basic information of the orthopedic patients to be analyzed and the historical orthopedic patients. The higher the similarity of basic information, the more likely the orthopedic patients to be analyzed and the historical orthopedic patients have similar physical functions. The higher the value, the more similar the basic information of the two is, and the higher the corresponding nursing adaptability is. Taking into account the diversity of orthopedic disease information, the hospital medical system may have a relatively low similarity between the disease conditions of the orthopedic patients to be analyzed and all historical orthopedic patients. At this time, it is difficult to reflect the actual degree of adaptation of the nursing plan based only on the similarity of the disease conditions. Therefore, it is necessary to reflect the similarity of physical functions between the orthopedic patients to be analyzed and the historical orthopedic patients through the similarity of basic information, and finally obtain the nursing adaptability, so that the nursing adaptability can more accurately reflect the degree of adaptation of the nursing plans for the orthopedic patients to be analyzed and the historical orthopedic patients, and improve the accuracy of selecting nursing plans for orthopedic patients to be analyzed. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A flowchart of a method for assisting in selecting a nursing plan for orthopedic patients provided by one embodiment of the present invention;

[0045] Figure 2 A structural diagram of a nursing plan auxiliary selection system for orthopedic patients provided by one embodiment of the present invention;

[0046] Figure 3 A flow chart of a method for obtaining initial similarity of disease conditions provided by an embodiment of the present invention;

[0047] Figure 4 A flow chart of a method for obtaining a disease condition confidence level provided by an embodiment of the present invention;

[0048] Figure 5 A flow chart of a method for obtaining comprehensive disease condition similarity provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a method, system and medium for assisting in selecting a nursing plan for orthopedic patients proposed by the present invention, in combination with the accompanying drawings and preferred embodiments, as well as its specific implementation methods, structures, features and effects. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0050] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0051] The following is a detailed description of a method, system and medium for assisting in selecting a nursing plan for orthopedic patients provided by the present invention in conjunction with the accompanying drawings.

[0052] The embodiment of the present invention provides a method, system and medium for assisting in selecting a nursing plan for orthopedic patients. Figure 1 , which shows a flow chart of a method for assisting in selecting a nursing plan for orthopedic patients provided by an embodiment of the present invention, the method comprising the following steps:

[0053] Step S1: Obtain the basic indicator data set and disease condition indicator data set of the orthopedic patients to be analyzed; at the same time, obtain the basic indicator data set of historical orthopedic patients, the disease condition indicator data set of each historical diagnosis, and the nursing plan;

[0054] The main purpose of this program is to provide appropriate nursing plans for orthopedic patients to be analyzed. Therefore, it is necessary to find historical orthopedic patients who are highly compatible with the actual nursing plans of the orthopedic patients to be analyzed. The nursing plans adopted by historical orthopedic patients can be used as a reference to assist in the selection of nursing plans for orthopedic patients to be analyzed. First of all, data collection is required.

[0055] The electronic medical record information of each orthopedic patient can be obtained from the hospital medical system. The electronic medical record information includes multiple condition indicators under the same diagnosis. Each condition indicator can reflect the orthopedic condition information of each dimension. Specifically, the condition indicators include: fractures, joint diseases, spinal diseases and nerve loss diseases, etc. The existing technology JieBa word segmentation method is used to segment the word, process the electronic medical record text, and obtain the word sequence; then use prior knowledge to determine the word set corresponding to the condition indicator; in order to facilitate computer processing, it is necessary to convert the text data into a vector, count all the corresponding vectors of the condition indicators, and obtain the condition indicator data set of orthopedic patients. Among them, since each vector actually reflects the orthopedic condition information, each vector in the condition indicator data set is used as the condition indicator data. It should be noted that the number of condition indicator data in the condition indicator data set reflects the number of orthopedic conditions, and the number of condition indicator data in different condition indicator data sets may be different. In any condition indicator data set, one condition indicator corresponds to at most one condition indicator data.

[0056] The basic indicators of each orthopedic patient at the time of the first diagnosis can also be obtained from the hospital medical system. Each basic indicator can reflect the basic information of the orthopedic patient in each dimension. Specifically, the basic indicators include: height, weight, age and past medical history, etc. The text data is converted into vectors, and the vectors corresponding to all basic indicators are counted to obtain the basic indicator data set. Among them, since each vector in the basic indicator data set actually reflects the basic information of the orthopedic patient, each vector in the basic indicator data set is used as the basic indicator data. For text data, such as fractures, the specific conversion method for converting text data into vectors can be to convert text data into vectors using a bag of words model, etc.; for numerical data, such as height, it can be converted into a corresponding vector using unique hot encoding, which is not limited here. It should be noted that past medical history is different from orthopedic conditions. Orthopedic conditions refer to the orthopedic diseases suffered by orthopedic patients, and past medical history refers to other diseases that orthopedic patients may have, such as heart disease and hypertension. It should be noted that the basic indicator data set counts the basic indicator data corresponding to the patient's first diagnosis. All types of basic indicators are fixed. In each basic indicator data set, each basic indicator corresponds to one basic indicator data, and the number of basic indicator data in all basic indicator data sets is the same.

[0057] In order to assist doctors in determining the nursing plans adopted for newly admitted orthopedic patients, newly admitted orthopedic patients are regarded as orthopedic patients to be analyzed, and orthopedic patients who have undergone at least two historical diagnoses are regarded as historical orthopedic patients; first, the basic information and orthopedic conditions of the patients to be analyzed need to be determined, that is, the basic indicator data set and the disease condition indicator data set of the orthopedic patients to be analyzed are obtained; in order to find historical orthopedic patients with a high degree of adaptability to the actual nursing plans of the orthopedic patients to be analyzed, the basic information and orthopedic conditions of the historical orthopedic patients need to be determined, that is, the basic indicator data set and the disease condition indicator data set of the historical patients are obtained; in order to determine the nursing plans for the orthopedic patients to be analyzed, all nursing plans adopted by historical orthopedic patients need to be used as a reference, that is, the nursing plans for historical orthopedic patients are obtained from the hospital medical system.

[0058] It should be noted that the bag-of-words model, unique hot encoding and JieBa word segmentation are prior arts well known to those skilled in the art and will not be described in detail here. It should be noted that, in order to facilitate calculations, all indicator data involved in the calculations in the embodiments of the present invention are preprocessed to eliminate the dimension effect. The specific means of removing the dimension effect are technical means well known to those skilled in the art and are not limited here.

[0059] Step S2: Based on the similarity between the condition indicator data set of the orthopedic patient to be analyzed and the condition indicator data sets of each historical diagnosis of historical orthopedic patients, obtain the initial similarity of the condition between the condition indicator data set of the orthopedic patient to be analyzed and the condition indicator data sets of each historical diagnosis of historical orthopedic patients; based on the changes in the condition indicator data sets of historical orthopedic patients in continuous historical diagnoses, obtain the confidence of the condition of the historical patients; based on the initial similarity of the condition between the condition indicator data set of the orthopedic patient to be analyzed and the condition indicator data sets of each historical diagnosis of historical orthopedic patients, as well as the confidence of the condition of the historical patients, obtain the comprehensive similarity of the condition of the orthopedic patient to be analyzed and the historical orthopedic patients.

[0060] In order to analyze the degree of adaptation of the actual nursing plan between historical orthopedic patients and orthopedic patients to be analyzed, it is necessary to analyze the similarity of the medical conditions first. Considering that the medical condition indicator data set reflects the orthopedic medical condition information such as fractures and bone cracks, joint diseases, and spinal diseases of orthopedic patients. The initial medical condition similarity is used to preliminarily reflect the similarity of the orthopedic conditions represented by the two medical condition indicator data sets; considering the complexity of orthopedic medical condition information, that is, under normal circumstances, the medical condition indicator data set of historical orthopedic patients can accurately reflect the actual orthopedic medical condition, and under abnormal circumstances, there are potential complications in historical orthopedic patients that have not been detected. At this time, the medical condition indicator data set is difficult to accurately reflect the actual orthopedic medical condition, and the medical condition confidence of the historical patients is obtained; the greater the medical condition confidence, the more accurately the medical condition indicator data of the historical patients can reflect the actual orthopedic medical condition, and the more reliable the nursing plan adopted. Finally, the comprehensive medical condition similarity of the orthopedic patients to be analyzed and the historical orthopedic patients is obtained, and the comprehensive medical condition similarity can more accurately reflect the similarity of the medical conditions between the historical orthopedic patients and the orthopedic patients to be analyzed.

[0061] See also Figure 3 , which shows a flow chart of a method for obtaining initial similarity of disease conditions provided by an embodiment of the present invention. Preferably, since the disease condition indicator data set reflects the orthopedic disease condition information of orthopedic patients, the similarity between each two disease condition indicator data sets is analyzed to analyze the similarity of orthopedic diseases. In an embodiment of the present invention, the method for obtaining initial similarity of disease conditions includes:

[0062] Step S201: In the disease condition indicator data set of the orthopedic patient to be analyzed and the disease condition indicator data set of the historical diagnosis of the historical orthopedic patients, according to the similarity of the two disease condition indicator data sets at the overall level, obtain the overall disease condition similarity parameters of the two disease condition indicator data sets.

[0063] First, the similarity between the two disease condition indicator data sets is determined from an overall perspective, and the overall disease condition similarity parameter of each two disease condition indicator data sets is obtained. The larger the value, the more similar the orthopedic disease conditions are.

[0064] In one embodiment of the present invention, the Jaccard correlation coefficient of each two condition indicator data sets is calculated to obtain the overall condition similarity parameter of the two condition indicator data sets. Since the number of condition indicator data in the condition indicator data set reflects the number of orthopedic conditions, the intersection and union of orthopedic conditions are analyzed using the Jaccard correlation coefficient to reflect the similarity of orthopedic conditions. It should be noted that the Jaccard correlation coefficient method is a prior art well known to those skilled in the art and will not be described in detail here.

[0065] Step S202: obtaining global similarity parameters of the two disease condition indicator data sets according to the similarity of the two disease condition indicator data sets at the local level.

[0066] The local similarity between two disease condition indicator data sets is also determined from a local perspective, and the global similarity parameter of each two disease condition indicator data sets is obtained. The larger the value of the global similarity parameter, the more similar the orthopedic conditions are.

[0067] In one embodiment of the present invention, a method for obtaining global similarity parameters includes:

[0068] In the disease condition indicator data set of the orthopedic patients to be analyzed and the disease condition indicator data set of the historical diagnosis of the historical orthopedic patients, the local disease condition similarity factor of each disease condition indicator is obtained according to the similarity between the two disease condition indicator data under each disease condition indicator.

[0069] The local condition similarity factors of all condition indicators in the two condition indicator data sets are integrated to obtain the global similarity parameters of the two condition indicator data sets.

[0070] For the above steps, specifically, the condition indicator to which the condition indicator data belongs represents the actual type of orthopedic condition; in the condition indicator data set of the orthopedic patients to be analyzed and the condition indicator data set of the historical diagnosis of the historical orthopedic patients, the two condition indicator data corresponding to the condition indicator are used as the condition combination to be analyzed; in the condition combination to be analyzed, the cosine similarity of the two condition indicator data is calculated to obtain the local condition similarity factor of the condition combination to be analyzed; the mean of the local condition similarity factors of all condition combinations to be analyzed in the two condition indicator data sets is calculated to obtain the global similarity parameters of the two condition indicator data sets. It should be noted that when there is no condition combination to be analyzed in the two condition indicator data sets, it means that there is no similar orthopedic condition, and the global similarity parameter is set to 0. It should be noted that cosine similarity is a prior art well known to those skilled in the art and will not be elaborated here.

[0071] It should be understood that, in the case where it is unclear whether the disease condition indicator data set of the orthopedic patient to be analyzed and the disease condition indicator data set of the historical diagnosis of the historical orthopedic patient have two disease condition indicator data under each disease condition indicator, in other embodiments of the present invention, the method for obtaining the global similarity parameter includes:

[0072] In order to compare the similarity of the same orthopedic conditions, it is necessary to construct a comparison combination of condition indicator data; considering that the similarity between the corresponding condition indicator data of the same orthopedic conditions reflects the local similarity, the higher the similarity, the more similar the orthopedic conditions are. Specifically, in the condition indicator data set of the orthopedic patients to be analyzed and the condition indicator data set of the historical diagnosis of historical orthopedic patients, the condition indicator data set with the largest number of condition indicator data is used as the initial target analysis set, and the condition indicator data set with the least number of condition indicator data is used as the initial reference analysis set; the dynamic adjustment process of the target analysis set and the reference analysis set is performed:

[0073] Select any disease condition indicator data from the target analysis set as the data to be analyzed, calculate the cosine similarity between the data to be analyzed and all disease condition indicator data in the reference analysis set, and use the disease condition indicator data corresponding to the largest cosine similarity as the reference data; use the data to be analyzed and the reference data as a disease condition indicator data comparison combination; delete the data to be analyzed and the reference data in the target analysis set and the reference analysis set, and construct a new target analysis set and a new reference analysis set.

[0074] The above dynamic adjustment process of the target analysis set and the reference analysis set is repeated until the adjustment that there is no disease indicator data in the reference analysis set is satisfied.

[0075] Statistically obtain the comparison combination of all disease condition indicator data corresponding to the end of iteration; at the end of iteration, a target analysis set will also be output. If there are remaining disease condition indicator data in the target analysis set, each remaining disease condition indicator data will be used as data to be analyzed. In the initial reference analysis set, the cosine similarity between each data to be analyzed and all disease condition indicator data will be calculated respectively, and the disease condition indicator data corresponding to the largest cosine similarity will be used as reference data; thereby obtaining the comparison combination of all disease condition indicator data corresponding to the remaining disease condition indicator data in the target analysis set; the comparison combination of all disease condition indicator data corresponding to the end of iteration and the comparison combination of all disease condition indicator data corresponding to the remaining disease condition indicator data in the target analysis set will be used as the comparison combination of all disease condition indicator data in the two disease condition indicator data sets, and the disease condition indicator data comparison combination can reflect two disease condition indicator data under the same disease condition indicator.

[0076] In the comparison combination of condition indicator data, the cosine similarity of the two condition indicator data is calculated to obtain the local condition similarity factor of the condition indicator data comparison combination; the mean of the local condition similarity factors of all condition indicator data comparison combinations in the two condition indicator data sets is calculated to obtain the global similarity parameters of the two condition indicator data sets. It should be noted that when the number of two condition indicator data sets is equal, one of the condition indicator data sets is used as the target analysis set and the other is used as the reference analysis set. When the largest cosine similarity corresponds to multiple condition indicator data, one of the condition indicator data is selected as the reference data.

[0077] Step S203: The overall condition similarity parameter and the global similarity parameter are integrated to obtain the initial condition similarity between the condition indicator data set of the orthopedic patient to be analyzed and the condition indicator data set of each historical diagnosis of the historical orthopedic patient; the overall condition similarity parameter and the global similarity parameter are positively correlated with the initial condition similarity.

[0078] The initial similarity of the disease condition preliminarily reflects the similarity of the orthopedic conditions represented by the two disease condition indicator data sets by comprehensively analyzing the similarities between each two disease condition indicator data sets. The larger the value, the more similar the orthopedic conditions are.

[0079] In one embodiment of the present invention, the product of the overall condition similarity parameter and the global similarity parameter is calculated to obtain the initial condition similarity of each two condition indicator data sets. In other embodiments of the present invention, the sum of the overall condition similarity parameter and the global similarity parameter is calculated to obtain the initial condition similarity of each two condition indicator data sets.

[0080] See also Figure 4 , which shows a flow chart of a method for obtaining the confidence of a condition provided by an embodiment of the present invention. Preferably, taking into account the complexity of orthopedic condition information, that is, if the orthopedic disease of a historical orthopedic patient may not be discovered in the first historical diagnosis, but with the passage of time and the progression of the disease, it often leads to potential complications that are obvious and are discovered in the second historical diagnosis; for example, severe fractures or spinal diseases that were not discovered in the first historical diagnosis may lead to the development of the disease and complications such as nerve or vascular damage, which are discovered in the second historical diagnosis. The credibility of the historical patient's condition is analyzed by analyzing the possibility of the existence of complications. In one embodiment of the present invention, the method for obtaining the confidence of the condition includes:

[0081] Step S211: Obtain a quantity confidence factor according to the quantity difference of the disease condition indicator data in the disease condition indicator data set corresponding to two consecutive historical diagnoses.

[0082] Considering that the lower the possibility of complications, the more the disease indicator data set representing the historical patients can represent the actual disease condition, and the more referenceable the nursing plan representing the historical patients is. Often, the more disease indicator data the later historical diagnosis has than the previous historical diagnosis, the more disease problems the later historical diagnosis exposed, and the more likely complications are to occur, and the quantitative confidence factor is obtained. The larger the quantitative confidence factor, the more it represents that the later historical diagnosis did not expose more disease problems, and the less likely complications are to occur, indicating that the disease indicator data set of the historical patients can represent the actual disease condition.

[0083] In one embodiment of the present invention, the difference between the number of all condition indicator data in the condition indicator data set corresponding to the first historical diagnosis and the second historical diagnosis is calculated to obtain the quantity confidence factor. In other embodiments of the present invention, the ratio of the number of all condition indicator data in the condition indicator data set corresponding to the first historical diagnosis and the second historical diagnosis is calculated to obtain the quantity confidence factor.

[0084] Step S212: Obtain a disease condition similarity factor based on the similarity between disease condition indicator data sets corresponding to two consecutive historical diagnoses.

[0085] Considering that the presence of complications will significantly change the condition between the first two historical diagnoses, the smaller the condition similarity factor, the more medical problems exposed in the later historical diagnosis, the more likely complications will occur, and the less likely the historical patient's medical condition indicator data set is to represent the actual condition.

[0086] In one embodiment of the present invention, the Jaccard correlation coefficient of the two disease condition indicator data sets corresponding to the first historical diagnosis and the second historical diagnosis is used as the disease condition similarity factor. In other embodiments of the present invention, the global similarity parameter of the two disease condition indicator data sets corresponding to the first historical diagnosis and the second historical diagnosis is used as the disease condition similarity factor.

[0087] Step S213: Obtain the confidence level of the historical patient's condition based on the quantity confidence factor and the condition similarity factor; the quantity confidence factor and the condition similarity factor are both positively correlated with the condition confidence level.

[0088] The confidence level of the historical patients’ conditions is obtained by combining the quantitative confidence factor and the condition similarity factor. The greater the condition confidence level, the more accurately the historical patients’ condition indicator data can reflect the actual orthopedic condition, and the more reliable the nursing plan adopted is.

[0089] In one embodiment of the present invention, the sum of the quantity confidence factor and the condition similarity factor is calculated, and the sum is normalized to obtain the condition confidence of the historical patient. In other embodiments of the present invention, the product of the quantity confidence factor and the condition similarity factor is calculated, and the product is normalized to obtain the condition confidence of the historical patient. In one embodiment of the present invention, the normalization method is: normalization is performed using the norm normalization function to limit the value range to between 0 and 1.

[0090] In other embodiments of the present invention, considering that some complications manifest relatively slowly, it is also necessary to analyze the second historical diagnosis and the third historical diagnosis to obtain the condition confidence, that is, the method for obtaining the condition confidence includes: obtaining the second and third quantitative confidence factors according to the difference in the quantity of condition indicator data in the condition indicator data sets corresponding to the second historical diagnosis and the third historical diagnosis; obtaining the second and third condition similarity factors of the two condition indicator data sets corresponding to the second historical diagnosis and the third historical diagnosis according to the similarity between the condition indicator data sets corresponding to the second historical diagnosis and the third historical diagnosis; obtaining the second and third condition confidence of the historical patient according to the second and third quantitative confidence factors and the second and third condition similarity factors; the second and third quantitative confidence factors and the second and third condition similarity factors are both positively correlated with the second and third condition confidence; calculating the average of the second and third condition confidence and the condition confidence as the new condition confidence.

[0091] See also Figure 5 , which shows a flow chart of a method for obtaining comprehensive condition similarity in one embodiment of the present invention. Preferably, in order to more accurately reflect the similarity of the condition between the historical orthopedic patients and the orthopedic patients to be analyzed, in one embodiment of the present invention, the method for obtaining the comprehensive condition similarity includes:

[0092] Step S221: The initial similarity of the condition between the orthopedic patient to be analyzed and the condition indicator data set of the historical orthopedic patients in the first historical diagnosis is used as the basic initial similarity; the initial similarity of the condition between the orthopedic patient to be analyzed and the condition indicator data set of the historical orthopedic patients in the second historical diagnosis is used as the reference initial similarity.

[0093] Since doctors will develop appropriate nursing plans for historical orthopedic patients at the time of the first historical diagnosis, in order to eliminate the influence of nursing plans and obtain more nursing plan references, the initial similarity of the condition between the orthopedic patients to be analyzed and the historical orthopedic patients at the first historical diagnosis is used as the basic initial similarity. The basic initial similarity preliminarily reflects the similarity between the initial conditions of the orthopedic patients to be analyzed and the historical orthopedic patients. However, in order to analyze the historical orthopedic patients after a period of recovery, the reference initial similarity is obtained to reflect the similarity between the conditions of the orthopedic patients to be analyzed and the historical orthopedic patients after a period of recovery.

[0094] Step S222: According to the difference between the basic initial similarity and the reference initial similarity, the confidence level of the condition of the historical orthopedic patients is adjusted to obtain a basic adjustment weight.

[0095] When the reference initial similarity is greater than the basic initial similarity, it means that the influence of minor conditions is excluded. The more similar the main conditions of the orthopedic patients to be analyzed are to the historical orthopedic patients, the difference between the reference initial similarity and the basic initial similarity is used as the adjustment direction to adjust the condition confidence and obtain the basic adjustment weight.

[0096] In one embodiment of the present invention, a method for obtaining a basic adjustment weight includes:

[0097] When the reference initial similarity is greater than the basic initial similarity, a positive correlation mapping is performed on the condition confidence to obtain the basic adjustment weight. When the reference initial similarity is less than the basic initial similarity, a negative correlation mapping is performed on the condition confidence to obtain the basic adjustment weight. When the reference initial similarity is equal to the basic initial similarity, a fixed value of 1 is used as the basic adjustment weight.

[0098] Specifically, when the reference initial similarity is greater than the basic initial similarity, the condition confidence is positively mapped by calculating the sum of 1 and the condition confidence to obtain the basic adjustment weight. When the reference initial similarity is less than the basic initial similarity, the condition confidence is negatively mapped by calculating the difference between 1 and the condition confidence to obtain the basic adjustment weight. When the reference initial similarity is equal to the basic initial similarity, a fixed value of 1 is used as the basic adjustment weight.

[0099] Step S223: According to the basic adjustment weight and the basic initial similarity, the comprehensive condition similarity of the orthopedic patient to be analyzed and the historical orthopedic patients is obtained; the basic adjustment weight and the basic initial similarity are both positively correlated with the comprehensive condition similarity.

[0100] The comprehensive condition similarity can more accurately reflect the similarity of the condition between historical orthopedic patients and orthopedic patients to be analyzed.

[0101] In one embodiment of the present invention, the product of the basic adjustment weight and the basic initial similarity is calculated to obtain the comprehensive condition similarity between the orthopedic patient to be analyzed and the historical orthopedic patients.

[0102] Step S3: According to the similarity of the basic indicator data sets of the orthopedic patient to be analyzed and the historical orthopedic patients, the similarity of the basic information of the orthopedic patient to be analyzed and the historical orthopedic patients is obtained.

[0103] Considering the nursing plans adopted by historical orthopedic patients with similar physical functions to the orthopedic patients to be analyzed, it is of reference to the orthopedic patients to be analyzed. For example, the elderly have similar physical functions, osteoporosis often occurs in the elderly, and the nursing needs are similar, so the nursing plans adopted by the elderly are similar. Obtain the similarity of basic information of the orthopedic patients to be analyzed and the historical orthopedic patients. The higher the similarity of basic information, the more likely the orthopedic patients to be analyzed and the historical orthopedic patients have similar physical functions. The higher the value, the more similar the basic information of the two is, and the higher the corresponding nursing adaptability is.

[0104] Preferably, in one embodiment of the present invention, in order to analyze the similarity of basic information between historical orthopedic patients and orthopedic patients to be analyzed, the method for obtaining the similarity of basic information includes:

[0105] In the basic indicator data set of the orthopedic patients to be analyzed and the historical orthopedic patients, the similarity between two basic indicator data is calculated under each basic indicator to obtain the basic local similarity of each basic indicator;

[0106] The basic local similarities of all basic indicators are combined to obtain the basic information similarity between the orthopedic patients to be analyzed and the historical orthopedic patients.

[0107] Considering that basic indicators reflect the type of basic information, different orthopedic patients have fixed basic indicators for all types, that is, each basic indicator data set has the same basic indicators for all types. Basic indicators include height, weight, age, and medical history. The similarity of all basic indicator data under various basic indicators reflects the similarity of basic information in each dimension, and obtains the basic information similarity between the orthopedic patient to be analyzed and the historical orthopedic patients. The higher the similarity of basic information, the more likely it is that the orthopedic patient to be analyzed and the historical orthopedic patient have similar physical functions.

[0108] For the above steps, specifically, in the basic indicator data set of the orthopedic patients to be analyzed and the historical orthopedic patients, the cosine similarity between the two basic indicator data is calculated under each basic indicator to obtain the basic local similarity of each basic indicator, and the similarity is reflected by the cosine similarity; the cumulative value of the basic local similarities of all basic indicators is calculated to obtain the basic information similarity of the orthopedic patients to be analyzed and the historical orthopedic patients.

[0109] Step S4: According to the similarity of comprehensive medical conditions and basic information, the nursing suitability of the orthopedic patient to be analyzed and the historical orthopedic patients is obtained; according to the nursing suitability and the nursing plans of the historical orthopedic patients, the nursing plan of the orthopedic patient to be analyzed is selected.

[0110] Considering that the comprehensive condition similarity can more accurately reflect the similarity of the condition between the historical orthopedic patients and the orthopedic patients to be analyzed, the higher the value, the more similar the conditions of the two are, and the corresponding nursing adaptability is high. Considering the diversity of orthopedic condition information, the hospital medical system may have a relatively low similarity between the orthopedic patients to be analyzed and all historical orthopedic patients. At this time, it is difficult to reflect the actual degree of adaptation of the nursing plan based only on the similarity of the condition. Therefore, it is necessary to reflect the similarity of the physical functions of the orthopedic patients to be analyzed and the historical orthopedic patients through the similarity of basic information. The higher the value, the more similar the basic information of the two is, and the corresponding nursing adaptability is high. Finally, the nursing adaptability is obtained, so that the nursing adaptability can more accurately reflect the degree of adaptation of the nursing plan for the orthopedic patients to be analyzed and the historical orthopedic patients, and improve the accuracy of selecting the nursing plan for the orthopedic patients to be analyzed.

[0111] Preferably, when the comprehensive condition similarity is higher, more emphasis is placed on adapting the nursing plan from the perspective of the condition; and when the comprehensive condition similarity is lower, more emphasis is placed on adapting the nursing plan from the perspective of basic information. In one embodiment of the present invention, a method for obtaining nursing suitability includes:

[0112] The similarity of the comprehensive conditions of the orthopedic patients to be analyzed and the historical orthopedic patients is inversely normalized to obtain the basic weight of emphasis; the difference between 1 and the basic weight of emphasis is calculated to obtain the weight of emphasis on the condition; the product of the weight of emphasis on the condition and the similarity of the comprehensive condition is calculated to obtain the first product value; the product of the basic weight of emphasis and the similarity of basic information is calculated to obtain the second product value; the sum of the first product value and the second product value is calculated to obtain the nursing adaptability of the orthopedic patients to be analyzed and the historical orthopedic patients. The specific formula includes:

[0113] ;

[0114] in, For orthopedic patients to be analyzed and The nursing fitness of each orthopaedic patient; To tune hyperparameters; The natural constant The exponential function with base ; For orthopedic patients to be analyzed and The similarity of the comprehensive conditions of historical orthopedic patients; For orthopedic patients to be analyzed and Similarity of basic information of historical orthopedic patients; To attach importance to the basic weight; In order to attach importance to the weight of the disease condition, in one embodiment of the present invention, the value of the adjustment hyperparameter is 8, which is used to adjust the weight distribution of the basic information similarity and the comprehensive disease condition similarity.

[0115] In the formula, Reflects the orthopedic patients to be analyzed and the The degree of poor condition of each orthopedic patient in history. The larger the value, the worse it is, and the focus of judgment is more inclined to the similarity of basic information; otherwise, the focus of judgment is more inclined to the similarity of comprehensive condition. To adjust the weights of basic information similarity and comprehensive condition similarity, so that the nursing adaptability can more accurately reflect the adaptability of the nursing plans for the orthopedic patients to be analyzed and the historical orthopedic patients.

[0116] Specifically, first obtain the nursing suitability of the orthopedic patient to be analyzed and all historical orthopedic patients. Then, use the historical orthopedic patients corresponding to the largest number of nursing suitability as reference patients for the orthopedic patients to be analyzed; provide the nursing plans adopted by all reference patients to the doctor to assist the doctor in selecting nursing plans for the orthopedic patients to be analyzed, so as to reduce the doctor's workload. In one embodiment of the present invention, the preset number is 10, and the more reference information the orthopedic patient to be analyzed needs, the larger the preset number can be set.

[0117] In summary, the embodiments of the present invention provide a method, system and medium for assisting in selecting a nursing plan for orthopedic patients. In the embodiments of the present invention, the comprehensive similarity of the condition of the orthopedic patient to be analyzed and the historical orthopedic patients is first obtained based on the initial similarity of the condition between the condition indicator data set of the orthopedic patient to be analyzed and the condition indicator data set of each historical diagnosis of the historical orthopedic patient, as well as the condition confidence of the historical patient; the basic information similarity of the orthopedic patient to be analyzed and the historical orthopedic patients is obtained based on the similarity of the basic indicator data set of the orthopedic patient to be analyzed and the historical orthopedic patients; the nursing adaptability of the orthopedic patient to be analyzed and the historical orthopedic patients is obtained based on the comprehensive similarity of the condition and the basic information similarity; the nursing adaptability of the orthopedic patient to be analyzed and the historical orthopedic patients is selected based on the nursing adaptability and the nursing plan of the historical orthopedic patient. The embodiments of the present invention more accurately reflect the degree of adaptability of the nursing plans of the orthopedic patient to be analyzed and the historical orthopedic patients through the nursing adaptability, thereby improving the accuracy of selecting the nursing plan for the orthopedic patient to be analyzed.

[0118] The present invention also proposes a nursing plan auxiliary selection system for orthopedic patients, please refer to Figure 2 , which shows a structural diagram of a nursing plan auxiliary selection system for orthopedic patients provided by an embodiment of the present invention. The system includes: a data acquisition module 101, a disease similarity analysis module 102, a basic information similarity analysis module 103 and a plan selection module 104.

[0119] The data acquisition module 101 is used to acquire the basic indicator data set and the disease condition indicator data set of the orthopedic patient to be analyzed; and at the same time, acquire the basic indicator data set of the historical orthopedic patients, the disease condition indicator data set at each historical diagnosis, and the nursing plan;

[0120] The condition similarity analysis module 102 is used to obtain the initial condition similarity between the condition indicator data set of the orthopedic patient to be analyzed and the condition indicator data sets of each historical diagnosis of the historical orthopedic patients based on the similarity between the condition indicator data set of the orthopedic patient to be analyzed and the condition indicator data sets of each historical diagnosis of the historical orthopedic patients; obtain the condition confidence of the historical patients based on the changes in the condition indicator data sets of the historical orthopedic patients in consecutive historical diagnoses; obtain the comprehensive condition similarity between the orthopedic patient to be analyzed and the historical orthopedic patients based on the initial condition similarity between the condition indicator data set of the orthopedic patient to be analyzed and the condition indicator data sets of each historical diagnosis of the historical orthopedic patients, as well as the condition confidence of the historical patients.

[0121] The basic information similarity analysis module 103 is used to obtain the basic information similarity of the orthopedic patient to be analyzed and the historical orthopedic patients according to the similarity of the basic indicator data sets of the orthopedic patient to be analyzed and the historical orthopedic patients.

[0122] The plan selection module 104 is used to obtain the nursing suitability of the orthopedic patient to be analyzed and the historical orthopedic patients according to the comprehensive condition similarity and the basic information similarity; and select the nursing plan for the orthopedic patient to be analyzed according to the nursing suitability and the nursing plans of the historical orthopedic patients.

[0123] A medium for assisting selection of nursing plans for orthopedic patients comprises a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and when the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor, the steps of a method for assisting selection of nursing plans for orthopedic patients are implemented.

[0124] It should be noted that the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the nursing plan auxiliary selection system for orthopedic patients and the nursing plan auxiliary selection method embodiment for orthopedic patients provided in the above embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0125] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0126] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for assisting in selecting a nursing plan for orthopedic patients, characterized in that: The method comprises: Obtain the basic indicator data set and disease condition indicator data set of the orthopedic patients to be analyzed; at the same time, obtain the basic indicator data set of historical orthopedic patients, the disease condition indicator data set at each historical diagnosis, and the nursing plan; According to the similarity between the condition indicator data set of the orthopedic patient to be analyzed and the condition indicator data sets of each historical diagnosis of the historical orthopedic patient, the initial similarity of the condition between the condition indicator data set of the orthopedic patient to be analyzed and the condition indicator data sets of each historical diagnosis of the historical orthopedic patient is obtained; according to the change of the condition indicator data sets of the historical orthopedic patient in the continuous historical diagnosis, the confidence of the condition of the historical patient is obtained; according to the initial similarity of the condition between the condition indicator data set of the orthopedic patient to be analyzed and the condition indicator data sets of each historical diagnosis of the historical orthopedic patient, and the confidence of the condition of the historical patient, the comprehensive similarity of the condition of the orthopedic patient to be analyzed and the historical orthopedic patient is obtained; According to the similarity of the basic indicator data sets of the orthopedic patients to be analyzed and the historical orthopedic patients, the similarity of the basic information of the orthopedic patients to be analyzed and the historical orthopedic patients is obtained; According to the similarity of the comprehensive condition and the similarity of the basic information, the nursing suitability of the orthopedic patient to be analyzed and the historical orthopedic patients is obtained; according to the nursing suitability and the nursing plans of the historical orthopedic patients, the nursing plan of the orthopedic patient to be analyzed is selected; The method for obtaining the condition confidence level includes: Obtaining a quantity confidence factor according to a quantity difference of the disease condition indicator data in the disease condition indicator data set corresponding to two consecutive historical diagnoses; Obtaining a disease condition similarity factor according to similarities between two consecutive historical diagnoses corresponding to the disease condition indicator data sets; The confidence level of the condition of the historical patient is obtained according to the quantity confidence factor and the condition similarity factor; the quantity confidence factor and the condition similarity factor are both positively correlated with the condition confidence level.

2. A method for assisting in selecting a nursing plan for orthopedic patients according to claim 1, characterized in that: The method for obtaining the initial similarity of the disease condition includes: In the disease condition indicator data set of the orthopedic patient to be analyzed and the disease condition indicator data set of the historical diagnosis of the historical orthopedic patient, according to the similarity of the two disease condition indicator data sets at the overall level, obtain the overall disease condition similarity parameters of the two disease condition indicator data sets; According to the similarity of the two disease condition indicator data sets at the local level, obtaining the global similarity parameter of the two disease condition indicator data sets; The overall condition similarity parameter and the global similarity parameter are integrated to obtain the initial condition similarity between the condition indicator data set of the orthopedic patient to be analyzed and the condition indicator data set of each historical diagnosis of the historical orthopedic patient; the overall condition similarity parameter and the global similarity parameter are both positively correlated with the initial condition similarity.

3. A method for assisting selection of nursing plans for orthopedic patients according to claim 2, characterized in that: The method for obtaining the overall disease condition similarity parameter includes: The Jaccard correlation coefficient of the two disease condition indicator data sets is calculated to obtain the overall disease condition similarity parameter of the two disease condition indicator data sets.

4. The method for assisting in selecting a nursing plan for orthopedic patients according to claim 2, characterized in that: The method for obtaining the global similarity parameter includes: In the disease condition indicator data set of the orthopedic patients to be analyzed and the disease condition indicator data set of the historical diagnosis of the historical orthopedic patients, according to the similarity between the two disease condition indicator data under each disease condition indicator, obtain the local disease condition similarity factor of each disease condition indicator; The local condition similarity factors of all condition indicators in the two condition indicator data sets are integrated to obtain the global similarity parameters of the two condition indicator data sets.

5. The method for assisting in selecting a nursing plan for orthopedic patients according to claim 1, characterized in that: The method for obtaining the comprehensive medical condition similarity includes: The initial similarity of the condition between the orthopedic patient to be analyzed and the historical orthopedic patient in the first historical diagnosis of the condition indicator data set is used as the basic initial similarity; the initial similarity of the condition between the orthopedic patient to be analyzed and the historical orthopedic patient in the second historical diagnosis of the condition indicator data set is used as the reference initial similarity; According to the difference between the basic initial similarity and the reference initial similarity, the confidence of the condition of the historical orthopedic patients is adjusted to obtain the basic adjustment weight; According to the basic adjustment weight and the basic initial similarity, the comprehensive condition similarity between the orthopedic patient to be analyzed and the historical orthopedic patients is obtained; the basic adjustment weight and the basic initial similarity are both positively correlated with the comprehensive condition similarity.

6. A method for assisting selection of nursing plans for orthopedic patients according to claim 5, characterized in that: The method for obtaining the basic adjustment weight includes: When the reference initial similarity is greater than the basic initial similarity, a positive correlation mapping is performed on the condition confidence to obtain a basic adjustment weight; When the reference initial similarity is less than the basic initial similarity, negative correlation mapping is performed on the condition confidence to obtain a basic adjustment weight; When the reference initial similarity is equal to the basic initial similarity, a fixed value of 1 is used as the basic adjustment weight.

7. A method for assisting selection of nursing plans for orthopedic patients according to claim 1, characterized in that: The method for obtaining the basic information similarity includes: In the basic indicator data set of the orthopedic patients to be analyzed and the historical orthopedic patients, the similarity between two basic indicator data is calculated under each basic indicator to obtain the basic local similarity of each basic indicator; The basic local similarities of all basic indicators are combined to obtain the basic information similarity between the orthopedic patient to be analyzed and the historical orthopedic patients.

8. A nursing plan auxiliary selection system for orthopedic patients, characterized in that: The system comprises: The data acquisition module is used to acquire the basic indicator data set and the disease indicator data set of the orthopedic patients to be analyzed; at the same time, the basic indicator data set of historical orthopedic patients, the disease indicator data set of each historical diagnosis, and the nursing plan; The disease condition similarity analysis module is used to obtain the initial similarity of the disease condition between the disease condition indicator data set of the orthopedic patient to be analyzed and the disease condition indicator data set of each historical diagnosis of the historical orthopedic patient according to the similarity between the disease condition indicator data set of the orthopedic patient to be analyzed and the disease condition indicator data set of each historical diagnosis of the historical orthopedic patient; obtain the confidence of the disease condition of the historical patient according to the change of the disease condition indicator data set of the historical orthopedic patient in the continuous historical diagnosis; and obtain the confidence of the disease condition of the historical patient according to the change of the disease condition indicator data set of the orthopedic patient to be analyzed and the disease condition indicator data set of each historical diagnosis of the historical orthopedic patient. The initial similarity and the condition confidence of the historical patient are used to obtain the comprehensive condition similarity between the orthopedic patient to be analyzed and the historical orthopedic patient; the method for obtaining the condition confidence includes: obtaining a quantity confidence factor according to the quantity difference of the condition indicator data in the condition indicator data set corresponding to two consecutive historical diagnoses; obtaining a condition similarity factor according to the similarity between the condition indicator data sets corresponding to two consecutive historical diagnoses; obtaining the condition confidence of the historical patient according to the quantity confidence factor and the condition similarity factor; the quantity confidence factor and the condition similarity factor are both positively correlated with the condition confidence; A basic information similarity analysis module is used to obtain the similarity of basic information of the orthopedic patient to be analyzed and the historical orthopedic patients based on the similarity of the basic indicator data sets of the orthopedic patient to be analyzed and the historical orthopedic patients; A plan selection module is used to obtain the nursing suitability of the orthopedic patient to be analyzed and the historical orthopedic patients according to the comprehensive condition similarity and the basic information similarity; and select the nursing plan for the orthopedic patient to be analyzed according to the nursing suitability and the nursing plans of the historical orthopedic patients.

9. A nursing plan auxiliary selection medium for orthopedic patients, characterized in that: The method comprises a processor and a memory, wherein at least one instruction, at least one program, a code set or an instruction set is stored in the memory, and when the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor, the steps of a method for assisting in selecting a nursing plan for orthopedic patients as described in any one of claims 1 to 7 are implemented.

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