CN108305675A - Intelligent hospital guide's method and system of diversity enhancing - Google Patents
Intelligent hospital guide's method and system of diversity enhancing Download PDFInfo
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Abstract
The present invention provides intelligent hospital guide's method and system of species diversity enhancing, is related to medical field.The embodiment of the present invention is according to the evaluating of each doctor, the ranking of hospital belonging to each doctor, and the hospital belonging to each doctor is at a distance from patient current location, determine the ability weight, First Hospital weight and range of driving weight of each doctor, and intelligent hospital guide's model is established accordingly, the recommendation index of each doctor is finally determined using intelligent hospital guide's model, and is chosen the corresponding doctor's behaviours of the maximum recommendation index and recommended doctor.Above-mentioned technical proposal solves the problems, such as that hospital resources distribution is uneven, while the factors such as the range of driving of patient and hospital and the ranking of hospital are combined when recommending doctor, and optimal seeing can be provided for patient and examines doctor's suggestion.
Description
Technical Field
The invention relates to the field of medical treatment, in particular to an intelligent diagnosis guiding method and system with enhanced diversity.
Background
The medical guide is to guide the patient to the relevant department or doctor for medical treatment. Generally, a patient does not know the diagnosis and treatment characteristics of the hospital, the professional characteristics of a doctor and the like, so that most hospitals provide a diagnosis guide service for the patient in a manner of arranging a diagnosis guide in order to provide the patient with the diagnosis guide service in a timely and high-quality manner. However, the greatest problem of this manual diagnosis guidance method is that it can only provide the patient with the relevant information of the doctor in the hospital where the doctor is located, and cannot provide the patient with the relevant information of the doctors in other hospitals according to the diseases of the patient. Meanwhile, the manual diagnosis guide mode also has the problems of overlarge workload of a diagnosis guide, personnel shortage and low efficiency.
Patent No. 201710206541.3 discloses a robot intelligent diagnosis guide system, which can actively identify the existence of a patient, and make a preliminary inquiry for the patient, and provide the advice of a relevant doctor for seeing a doctor, and the proposal solves the problems of excessive workload of the doctor, shortage of staff, low diagnosis guide efficiency and the like to a certain extent, but fails to solve the problem of uneven hospital resource distribution. Meanwhile, the above patent or the prior art fails to provide the optimal doctor suggestion for the patient based on the vehicle distance between the patient and the hospital, the rank of the hospital, and other factors.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides an intelligent diagnosis guiding method and system with enhanced diversity, and solves the problems that optimal doctors cannot be provided for patients and hospital resources are not distributed uniformly in the prior art.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
in a first aspect, a method for intelligent diagnosis guidance with enhanced diversity is provided, the method comprising the following steps:
selecting a corresponding first doctor list aiming at the disease of the patient; the first doctor list comprises a plurality of doctors and hospitals to which each doctor belongs;
obtaining the evaluation of each doctor in the first doctor list, and determining the ability weight of each doctor according to the evaluation of each doctor;
obtaining the rank of the hospital to which each doctor belongs in the first doctor list, and determining the first hospital weight of each doctor according to the rank of the hospital to which each doctor belongs;
acquiring the distance between the hospital to which each doctor belongs in the first doctor list and the current position of the patient, and determining the journey weight of each doctor according to the distance;
establishing an intelligent diagnosis guiding model according to the ability weight, the first hospital weight and the journey weight of each doctor in the first doctor list, determining the recommendation index of each doctor by using the intelligent diagnosis guiding model, and selecting the doctor corresponding to the maximum recommendation index as the recommended doctor.
With reference to the first aspect, in a first possible implementation manner, the method further includes the following steps:
acquiring the distance between the hospital to which each doctor belongs and the current position of the patient;
and judging whether the distance is greater than a preset distance, and if the distance is greater than the preset distance, deleting the doctor corresponding to the distance from the first doctor list.
With reference to the first possible implementation manner of the first aspect, in a second possible implementation manner, the method further includes the following steps:
sequencing each doctor according to the sequence of the recommendation indexes from top to bottom to obtain a second doctor list;
aiming at each doctor in the second doctor list, acquiring the times of occurrence of the hospital to which the current doctor belongs in the corresponding hospital set, and calculating the quotient of the times and the total number of the hospitals in the hospital set to obtain the weight of the second hospital; wherein the hospital set is a set of hospitals of which the recommendation index is equal to or less than that of the current doctor;
and establishing a target intelligent diagnosis guiding model according to the ability weight, the first hospital weight, the second hospital weight and the trip weight of each doctor in the second doctor list, determining a target recommendation index of each doctor by using the target intelligent diagnosis guiding model, and selecting the doctor corresponding to the maximum target recommendation index to obtain the target recommended doctor.
With reference to the second possible implementation manner of the first aspect, in a third possible implementation manner, the method determines the ability weight of each doctor by using the following formula:
in the formula uiRepresents the ith doctor in the first doctor list u, wuiRepresenting the ability weight of the ith doctor in the first doctor list u, cijThe number of evaluations of the disease j of the ith doctor is represented, and I represents the number of doctors in the first doctor list.
With reference to the third possible implementation manner of the first aspect, in a fourth possible implementation manner, the method determines the trip weight of each doctor by using the following formula:
in the formula (d)pzRepresents the distance, maxd, between the hospital z to which a doctor in the first doctor list belongs and the patient ppzRepresents the maximum value, w, of the distances between the patient and the hospital to which each doctor in the first doctor list belongsdpzRepresenting a trip weight for each doctor in the first list of doctors.
With reference to the fourth possible implementation manner of the first aspect, in a fifth possible implementation manner, the method determines the first hospital weight of each doctor by using the following formula:
in the formula, rzRepresenting the rank of the hospital z to which a doctor belongs in the first doctor list, and r _ lowest representing the rank of the hospital which is ranked last in the hospitals to which each doctor belongs in the first doctor list; w is ahz1A first hospital weight representing each doctor in the first doctor list;
the method determines the second hospital weight for each doctor using the following formula:
in the formula, numhzRepresents the number of times that the hospital Z to which a doctor belongs appears in the corresponding hospital set in the second doctor list, wherein Z represents the number of corresponding doctors in the hospital set corresponding to the current doctor,the total number of hospitals in the hospital, whz2A second hospital weight representing each doctor in the second doctor list.
With reference to the fifth possible implementation manner of the first aspect, in a sixth possible implementation manner, the target intelligent diagnosis guiding model is:
wherein,
wherein Y represents the target recommendation index and P represents the number of patients.
In a second aspect, there is provided an intelligent referral system with enhanced diversity, the system comprising:
the first doctor list acquisition module is used for selecting a corresponding first doctor list aiming at the disease of the patient; the first doctor list comprises a plurality of doctors and hospitals to which each doctor belongs;
the data acquisition module is used for acquiring the evaluation of each doctor in the first doctor list, the ranking of the hospital to which each doctor belongs in the first doctor list and the distance between the hospital to which each doctor belongs and the current position of the patient in the first doctor list;
the ability weight determining module is used for determining the ability weight of each doctor according to the evaluation of each doctor;
the first hospital weight determining module is used for determining the first hospital weight of each doctor according to the rank of the hospital to which each doctor belongs;
the journey weight determining module is used for determining the journey weight of each doctor according to the distance;
and the doctor recommendation module is used for establishing an intelligent diagnosis guide model according to the ability weight, the first hospital weight and the journey weight of each doctor in the first doctor list, determining the recommendation index of each doctor by using the intelligent diagnosis guide model, and selecting the doctor corresponding to the maximum recommendation index as the recommended doctor.
With reference to the second aspect, in a first possible implementation manner, the system further includes:
and the doctor screening module is used for acquiring the distance between the hospital to which each doctor belongs and the current position of the patient, judging whether the distance is greater than a preset distance, and deleting the doctor corresponding to the distance from the first doctor list if the distance is greater than the preset distance.
With reference to the first possible implementation manner of the first aspect, in a second possible implementation manner, the system further includes:
the second doctor list acquisition module is used for sequencing each doctor according to the sequence of the recommendation indexes from top to bottom to obtain a second doctor list;
the second hospital weight determining module is used for acquiring the times of occurrence of the hospital to which the current doctor belongs in the corresponding hospital set aiming at each doctor in the second doctor list, and calculating the quotient of the times and the total number of hospitals in the hospital set to obtain the second hospital weight; wherein the hospital set is a set of hospitals of which the recommendation index is equal to or less than that of the current doctor;
the doctor recommending module is further configured to establish a target intelligent diagnosis guiding model according to the ability weight, the first hospital weight, the second hospital weight and the trip weight of each doctor in the second doctor list, determine a target recommendation index of each doctor by using the target intelligent diagnosis guiding model, and select a doctor corresponding to the maximum target recommendation index to obtain a target recommended doctor.
(III) advantageous effects
The embodiment of the invention provides an intelligent diagnosis guiding method and system with enhanced diversity. The method has the following beneficial effects:
according to the embodiment of the invention, a first doctor list is firstly obtained aiming at the disease of a patient, then the evaluation of each doctor in the first doctor list, the ranking of the hospital to which each doctor belongs and the distance between the hospital to which each doctor belongs and the current position of the patient are obtained, the capability weight, the first hospital weight and the journey weight of each doctor are determined, an intelligent diagnosis guide model is established according to the capability weight, the first hospital weight and the journey weight, finally the recommendation index of each doctor is determined by using the intelligent diagnosis guide model, and the doctor corresponding to the maximum recommendation index is selected as the recommended doctor. According to the technical scheme, the problem of uneven hospital resource distribution is solved, and meanwhile, the factors such as the journey between the patient and the hospital and the ranking of the hospital are combined when the doctor is recommended, so that the optimal doctor suggestion for seeing a doctor can be provided for the patient. The doctors with the advantages of capability of diagnosing the diseases of the patients, excellent hospital ranking and moderate distance are provided for the patients from the perspective of global resource allocation according to the diseases of the patients and the positions of the patients, the problem that the number of patients of the small hospitals with the same disease diagnosis capability is few when the large hospitals are in the same market at present is effectively solved, the current situation of uneven medical resource allocation is favorably alleviated, and the purpose of individual medical selection of the patients can be realized according to the vehicles of the patients and the hospitals.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 schematically illustrates a flow chart of a diversity enhanced intelligent approach to referral in accordance with an embodiment of the invention;
FIG. 2 schematically illustrates a flow chart of a diversity enhanced intelligent approach to referral of a further embodiment of the present invention;
FIG. 3 schematically illustrates a block diagram of a diversity enhanced intelligent referral system of an embodiment of the invention;
FIG. 4 schematically illustrates a block diagram of an enhanced-diversity intelligent referral system of a further embodiment of the invention;
fig. 5 schematically illustrates a block diagram of a diversity enhanced intelligent referral system of a further embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
An intelligent diagnosis guide method with enhanced diversity, as shown in fig. 1, comprises the following steps:
110. selecting a corresponding first doctor list aiming at the disease of the patient; the first doctor list comprises a plurality of doctors and hospitals to which each doctor belongs;
this step requires determining the patient's disease before execution, after which a first list of physicians can be obtained based on the patient's disease;
120. obtaining the evaluation of each doctor in the first doctor list, and determining the ability weight of each doctor according to the evaluation of each doctor;
in this step, the ability weight of each physician is determined using the following formula:
in the formula uiRepresents the ith doctor in the first doctor list u, wuiRepresenting the ability weight of the ith doctor in the first doctor list u, cijRepresenting the number of evaluations of the disease j of the ith doctor, and I representing the number of doctors in the first doctor list;
130. obtaining the rank of the hospital to which each doctor belongs in the first doctor list, and determining the first hospital weight of each doctor according to the rank of the hospital to which each doctor belongs;
in this step, the first hospital weight for each doctor is determined using the following formula:
in the formula, rzRepresenting the rank of the hospital z to which a doctor belongs in the first doctor list, and r _ lowest representing the rank of the hospital which is ranked last in the hospitals to which each doctor belongs in the first doctor list; w is ahz1A first hospital weight representing each doctor in the first doctor list;
140. acquiring the distance between the hospital to which each doctor belongs in the first doctor list and the current position of the patient, and determining the journey weight of each doctor according to the distance;
in this step, the trip weight for each doctor is determined using the following formula:
in the formula (d)pzRepresents the distance, maxd, between the hospital z to which a doctor in the first doctor list belongs and the patient ppzRepresents the maximum value, w, of the distances between the patient and the hospital to which each doctor in the first doctor list belongsdpzRepresenting a trip weight for each doctor in the first list of doctors;
150. establishing an intelligent diagnosis guiding model according to the ability weight, the first hospital weight and the journey weight of each doctor in the first doctor list, determining the recommendation index of each doctor by using the intelligent diagnosis guiding model, and selecting the doctor corresponding to the maximum recommendation index as a recommended doctor; specifically, the intelligent diagnosis guide model is obtained by multiplying the capacity weight, the first hospital weight and the journey weight. In addition, corresponding doctors are sorted according to the recommendation index from large to small to obtain a doctor-seeing recommendation list.
The embodiment provides a doctor list which has the ability to diagnose the disease of the patient and has excellent hospital ranking and moderate distance according to the disease state of the patient and the position of the patient from the perspective of global resource allocation. Ensuring that patients can easily obtain a list of doctors with the ability to diagnose their disease using the method, which fully considers the remaining objective indicators of the patient, such as the patient's range to the hospital, the official ranking of the hospital, the hospital's comprehensive ability.
In one embodiment, the intelligent diagnosis guiding method with enhanced diversity further comprises the following steps:
210. acquiring the distance between the hospital to which each doctor belongs and the current position of the patient;
220. and judging whether the distance is greater than a preset distance, and if the distance is greater than the preset distance, deleting the doctor corresponding to the distance from the first doctor list.
This embodiment eliminates doctors who are too far from the patient.
In one embodiment, as shown in fig. 2, the intelligent diagnosis guiding method with enhanced diversity further comprises the following steps:
310. sequencing each doctor according to the sequence of the recommendation indexes from top to bottom to obtain a second doctor list;
320. aiming at each doctor in the second doctor list, acquiring the times of occurrence of the hospital to which the current doctor belongs in the corresponding hospital set, and calculating the quotient of the times and the total number of the hospitals in the hospital set to obtain the weight of the second hospital; wherein the hospital set is a set of hospitals of which the recommendation index is equal to or less than that of the current doctor;
in this step, the second hospital weight for each doctor is determined using the following formula:
in the formula, numhzRepresents the number of times that the hospital Z to which a doctor belongs appears in the corresponding hospital set in the second doctor list, wherein Z represents the number of corresponding doctors in the hospital set corresponding to the current doctor,the total number of hospitals in the hospital, whz2A second hospital weight representing each doctor in the second doctor list;
330. establishing a target intelligent diagnosis guiding model according to the ability weight, the first hospital weight, the second hospital weight and the journey weight of each doctor in the second doctor list, determining a target recommendation index of each doctor by using the target intelligent diagnosis guiding model, and selecting the doctor corresponding to the maximum target recommendation index to obtain a target recommended doctor; in addition, sorting corresponding doctors according to the target recommendation index from large to small to obtain a doctor-seeing recommendation list;
in this step, the target intelligent diagnosis guide model is:
wherein,
wherein Y represents the target recommendation index and P represents the number of patients.
In the above embodiment, first, a doctor list (i.e. a first doctor list) corresponding to a disease is obtained according to the disease of a patient; obtaining evaluation data of doctors diagnosing the disease in the patient pair list, and obtaining the optimal sequencing of the doctor ability corresponding to the disease (namely obtaining the ability weight of each doctor) by the evaluation data; and then, the intelligent diagnosis guiding model is used, and doctor watching recommendation lists are obtained by fully considering doctor ability, hospital ranking, hospital resources and the journey between patients and hospitals. The algorithm of the intelligent diagnosis guide model uses the thought of a greedy algorithm for reference, reduces the spatial complexity of the algorithm by acquiring a local optimal mode, and achieves the purpose of shunting patients to different hospitals by adopting a mode of dynamically updating weights. The invention aims to solve the problems of difficulty in selecting a doctor for a patient and uneven hospital resource distribution. The embodiment can effectively solve the phenomenon that a large-scale hospital has a small number of patients in the same disease diagnosis capability but in a courtyard at present, and is favorable for relieving the current situation of uneven medical resource distribution.
Corresponding to the above method, an embodiment of the present invention further provides an intelligent diagnosis guide system with enhanced diversity, as shown in fig. 3, where the system includes:
the first doctor list acquisition module is used for selecting a corresponding first doctor list aiming at the disease of the patient; the first doctor list comprises a plurality of doctors and hospitals to which each doctor belongs;
the data acquisition module is used for acquiring the evaluation of each doctor in the first doctor list, the ranking of the hospital to which each doctor belongs in the first doctor list and the distance between the hospital to which each doctor belongs and the current position of the patient in the first doctor list;
the ability weight determining module is used for determining the ability weight of each doctor according to the evaluation of each doctor;
the first hospital weight determining module is used for determining the first hospital weight of each doctor according to the rank of the hospital to which each doctor belongs;
the journey weight determining module is used for determining the journey weight of each doctor according to the distance;
and the doctor recommendation module is used for establishing an intelligent diagnosis guide model according to the ability weight, the first hospital weight and the journey weight of each doctor in the first doctor list, determining the recommendation index of each doctor by using the intelligent diagnosis guide model, and selecting the doctor corresponding to the maximum recommendation index as the recommended doctor.
In one embodiment, as shown in fig. 4, the intelligent diagnosis guide system with enhanced diversity further comprises:
and the doctor screening module is used for acquiring the distance between the hospital to which each doctor belongs and the current position of the patient, judging whether the distance is greater than a preset distance, and deleting the doctor corresponding to the distance from the first doctor list if the distance is greater than the preset distance.
In one embodiment, as shown in fig. 5, the intelligent diagnosis guiding system with enhanced diversity further comprises:
the second doctor list acquisition module is used for sequencing each doctor according to the sequence of the recommendation indexes from top to bottom to obtain a second doctor list;
the second hospital weight determining module is used for acquiring the times of occurrence of the hospital to which the current doctor belongs in the corresponding hospital set aiming at each doctor in the second doctor list, and calculating the quotient of the times and the total number of hospitals in the hospital set to obtain the second hospital weight; wherein the hospital set is a set of hospitals of which the recommendation index is equal to or less than that of the current doctor;
the doctor recommending module is further configured to establish a target intelligent diagnosis guiding model according to the ability weight, the first hospital weight, the second hospital weight and the trip weight of each doctor in the second doctor list, determine a target recommendation index of each doctor by using the target intelligent diagnosis guiding model, and select a doctor corresponding to the maximum target recommendation index to obtain a target recommended doctor.
Each step in the method of the embodiment of the present invention corresponds to a step in the system of the embodiment of the present invention in the intelligent diagnosis guiding process, and each step in the system of the embodiment of the present invention in the intelligent diagnosis guiding process is included in the method of the embodiment of the present invention, so that repeated parts are not described herein again.
The enhanced-diversity intelligent approach system of the present invention is described in detail below with reference to yet another specific embodiment.
The system of the embodiment comprises a data preparation module, an intelligent diagnosis guide model building module and a model solving module.
The data preparation module is used for acquiring the following data: the disease of the patient, the patient's assessment of the ability of the doctor to diagnose the disease, the official ranking of the hospital, the range of the hospital and the patient. And sorting the doctor capability evaluation from high to low to obtain a doctor capability sorting list.
Many existing doctor recommendation systems recommend according to the qualification of doctors, which causes the phenomena of overload of patients in large hospitals, the competition of the patients for specialist numbers, uneven distribution of hospital resources and the like. In order to solve the problems, when the doctor is recommended for the patient, the doctor capacity is considered, and the factors of the hospital duty condition, the hospital official ranking condition and the patient-hospital trip in the doctor capacity sorting are also considered, wherein the hospital duty condition in the doctor capacity sorting can directly reflect the number condition of high-quality doctors owned by the hospital; the official ranking of the hospital can reflect the overall service level of the hospital; the convenience degree of the patient receiving treatment is directly influenced by the patient and the vehicle journey of the hospital. The invention adopts an intelligent diagnosis guiding model, and finally obtains a doctor recommendation sequence for balancing the indexes, wherein the specific modeling process is as follows:
the physician's ability is derived from the patient's assessment of the physician's diagnosis of the disease and represents the patient's satisfaction and experience with the physician diagnosing the disease. Doctor ability weightingFor doctor uiDiagnosis of disease vjEvaluation of (c)ijAll doctors diagnose diseases vjEvaluation ofThe ratio of (a) to (b). The physician ability weight is specifically expressed as:the greater the physician's ability weight, the more experienced the physician in diagnosing the disease and the greater the patient's satisfaction.
The hospital weight includes two parts of physician ability ordering of the occupation ratio of hospitals (corresponding to the two hospital weights) and the official ranking condition of hospitals (corresponding to the one hospital weight). Wherein, the ratio of doctors' ability to order traditional Chinese medicine is selected by hospital hzNumber of (2)Account for all hospitals in the listIs calculated, the ratio is a dynamically updated process because in the ranking process, when doctor u isiAfter being selected, the user selects the next doctor by using the rejection uiThe physician's ability after the information is ranked, and so on, until all physicians are ranked.
The hospital official ranking may reflect the overall healthcare level of the hospital, so the higher the ranking, the greater the corresponding weight should be. Hospitals and hospital official ranks in physician ability ordering are first corresponded, rzIndicates hospital hzThe official ranking of (1) is set as r _ lowest, the hospital ranking at the end of the official ranking of the hospital in the physician ability ranking list is set as r _ lowest, and the range of the hospital ranking in the list is [1, r _ lowest ]]Mapping Hospital rank to [0,1 ] using a linearly decreasing relationship]The weight for representing the hospital official ranking, the weight for the hospital official ranking can be represented asThe hospital weights can be expressed synthetically as:
in the medical procedure selection process of the patient, the journey between the patient and the hospital is one of important consideration factors, which directly affects the selection of the patient to the doctor, so the journey between the patient and the hospital is one of indexes for balancing the diversity. The patient and hospital journey is that the patient position is determined, the patient is used as the center, all hospital lists with the patient journey smaller than D in the area are collected, the hospital lists are sorted according to the length of the patient journey, and intersection is taken with the hospitals in the doctor ability sorting. When the distance between the patient and the hospital is shorter, the recommended strength should be greater, and the corresponding weight should be greater, so we use the time attenuation function f (x) to be (1-x) e-xTo describe the corresponding relationship between the journey and the weight, wherein, orderx∈[0,1]，dpzIndicates hospital hzThe weight of the journey with patient p, i.e. the journey between patient and hospitalCan be expressed as:
in summary, the intelligent diagnosis guidance model can be obtained as follows:
wherein the objective function of the model isY represents the product of each weight of the sequence, and the bigger the Y value is, the better the corresponding doctor ranking is, namely, the doctor ranking can comprehensively consider factors of the doctor ability, the hospital occupation ratio in the doctor ability list, the hospital ranking and the driving range of the patient and the hospital, so that the corresponding doctor ranking when the Y value is the largest is obtained by the model. The model contains 4 constraints, condition one: dpzD is used for limiting the range of the vehicle journey of the patient and the hospital; the other three conditions are respectively used for solving the weight of the doctor abilityWeights of hospitalsAnd patient to hospital drive weight
Because the number of doctors in the list is large, if the objective functions ordered by all possible doctors are solved, the space complexity of the model solving is high, and the solving efficiency is low, so that the model solving module of the embodiment takes the thought of the negotiable algorithm as a reference, and reduces the space complexity of the algorithm by solving the local optimum, specifically:
step one, utilizing dpzAnd D or less, screening doctors, and selecting all doctors with the journey less than D with the patient.
And step two, calculating the value of the static weight. In the process of calculating the hospital weight, the ratio of the hospitals in the physician ability sequencing is a dynamic updating process, so the weight of the part is not considered for the second time, and the weights of the rest static parts are calculated and used for the first timeInstead of hospital weight, thenThe static weight for each doctor was calculated as shown in table 1 below:
TABLE 1
Ranking according to the static weights of the physicians, resulting in table 2:
TABLE 2
And step three, extracting the doctors from top to bottom according to the static weight sequence of the doctors, calculating the dynamic weight of the doctors, multiplying the static weight and the dynamic weight to obtain the final weight, and sequencing the final weight of the doctors from top to bottom.
In the recommending process of the doctor, the factors of the doctor ability, the hospital ranking, the hospital resource allocation and the journey between the hospital and the patient are fully considered, and the problems of personalized doctor selection of the patient and uneven hospital resource allocation are solved. In the process of calculating the recommended list of the doctor for consultation, the thought of the negotiation algorithm is used for reference, the space complexity of the algorithm is reduced, the diversity of the recommended doctor list is improved by using a dynamic weight updating mode, and the purpose of shunting patients to different hospitals is achieved.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An intelligent diagnosis guide method with enhanced diversity is characterized by comprising the following steps:
selecting a corresponding first doctor list aiming at the disease of the patient; the first doctor list comprises a plurality of doctors and hospitals to which each doctor belongs;
obtaining the evaluation of each doctor in the first doctor list, and determining the ability weight of each doctor according to the evaluation of each doctor;
obtaining the rank of the hospital to which each doctor belongs in the first doctor list, and determining the first hospital weight of each doctor according to the rank of the hospital to which each doctor belongs;
acquiring the distance between the hospital to which each doctor belongs in the first doctor list and the current position of the patient, and determining the journey weight of each doctor according to the distance;
establishing an intelligent diagnosis guiding model according to the ability weight, the first hospital weight and the journey weight of each doctor in the first doctor list, determining the recommendation index of each doctor by using the intelligent diagnosis guiding model, and selecting the doctor corresponding to the maximum recommendation index as the recommended doctor.
2. The method according to claim 1, characterized in that the method further comprises the steps of:
acquiring the distance between the hospital to which each doctor belongs and the current position of the patient;
and judging whether the distance is greater than a preset distance, and if the distance is greater than the preset distance, deleting the doctor corresponding to the distance from the first doctor list.
3. The method according to claim 2, characterized in that the method further comprises the steps of:
sequencing each doctor according to the sequence of the recommendation indexes from top to bottom to obtain a second doctor list;
aiming at each doctor in the second doctor list, acquiring the times of occurrence of the hospital to which the current doctor belongs in the corresponding hospital set, and calculating the quotient of the times and the total number of the hospitals in the hospital set to obtain the weight of the second hospital; wherein the hospital set is a set of hospitals of which the recommendation index is equal to or less than that of the current doctor;
and establishing a target intelligent diagnosis guiding model according to the ability weight, the first hospital weight, the second hospital weight and the trip weight of each doctor in the second doctor list, determining a target recommendation index of each doctor by using the target intelligent diagnosis guiding model, and selecting the doctor corresponding to the maximum target recommendation index to obtain the target recommended doctor.
4. The method of claim 3, wherein the method determines the ability weight for each physician using the following formula:
in the formula uiRepresents the ith doctor in the first doctor list u, wuiRepresenting the ability weight of the ith doctor in the first doctor list u, cijThe number of evaluations of the disease j of the ith doctor is represented, and I represents the number of doctors in the first doctor list.
5. The method of claim 4, wherein the method determines the trip weight for each physician using the following equation:
in the formula (d)pzRepresents the distance, maxd, between the hospital z to which a doctor in the first doctor list belongs and the patient ppzRepresents the maximum value, w, of the distances between the patient and the hospital to which each doctor in the first doctor list belongsdpzRepresenting a trip weight for each doctor in the first list of doctors.
6. The method of claim 5, wherein the method determines the first hospital weight for each doctor using the formula:
in the formula, rzRepresenting a doctor in the first doctor listRank of the affiliated hospital z, r _ lowest represents the rank of the last ranked hospital among the hospitals to which each doctor in the first doctor list belongs; w is ahz1A first hospital weight representing each doctor in the first doctor list;
the method determines the second hospital weight for each doctor using the following formula:
in the formula, numhzRepresents the number of times that the hospital Z to which a doctor belongs appears in the corresponding hospital set in the second doctor list, wherein Z represents the number of corresponding doctors in the hospital set corresponding to the current doctor,the total number of hospitals in the hospital, whz2A second hospital weight representing each doctor in the second doctor list.
7. The method of claim 6, wherein the target intelligent referral model is:
wherein,
wherein Y represents the target recommendation index and P represents the number of patients.
8. An intelligent referral system with enhanced diversity, the system comprising:
the first doctor list acquisition module is used for selecting a corresponding first doctor list aiming at the disease of the patient; the first doctor list comprises a plurality of doctors and hospitals to which each doctor belongs;
the data acquisition module is used for acquiring the evaluation of each doctor in the first doctor list, the ranking of the hospital to which each doctor belongs in the first doctor list and the distance between the hospital to which each doctor belongs and the current position of the patient in the first doctor list;
the ability weight determining module is used for determining the ability weight of each doctor according to the evaluation of each doctor;
the first hospital weight determining module is used for determining the first hospital weight of each doctor according to the rank of the hospital to which each doctor belongs;
the journey weight determining module is used for determining the journey weight of each doctor according to the distance;
and the doctor recommendation module is used for establishing an intelligent diagnosis guide model according to the ability weight, the first hospital weight and the journey weight of each doctor in the first doctor list, determining the recommendation index of each doctor by using the intelligent diagnosis guide model, and selecting the doctor corresponding to the maximum recommendation index as the recommended doctor.
9. The system of claim 8, further comprising:
and the doctor screening module is used for acquiring the distance between the hospital to which each doctor belongs and the current position of the patient, judging whether the distance is greater than a preset distance, and deleting the doctor corresponding to the distance from the first doctor list if the distance is greater than the preset distance.
10. The system of claim 8, further comprising:
the second doctor list acquisition module is used for sequencing each doctor according to the sequence of the recommendation indexes from top to bottom to obtain a second doctor list;
the second hospital weight determining module is used for acquiring the times of occurrence of the hospital to which the current doctor belongs in the corresponding hospital set aiming at each doctor in the second doctor list, and calculating the quotient of the times and the total number of hospitals in the hospital set to obtain the second hospital weight; wherein the hospital set is a set of hospitals of which the recommendation index is equal to or less than that of the current doctor;
the doctor recommending module is further configured to establish a target intelligent diagnosis guiding model according to the ability weight, the first hospital weight, the second hospital weight and the trip weight of each doctor in the second doctor list, determine a target recommendation index of each doctor by using the target intelligent diagnosis guiding model, and select a doctor corresponding to the maximum target recommendation index to obtain a target recommended doctor.
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