CN117524405A - Cloud computing-based gynecological nursing method intelligent selection system - Google Patents
Cloud computing-based gynecological nursing method intelligent selection system Download PDFInfo
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Abstract
A cloud computing-based gynecological nursing method intelligent selection system relates to the field of gynecological nursing, and comprises a monitoring center, wherein the monitoring center is in communication connection with a field semantic database, a symptom diagnosis module, a nursing method module and a nursing feedback module; the domain semantic database maps symptom sets, disease sets and gynecological nursing method sets into a multi-node network based on information provided by domain experts; the symptom diagnosis module is used for acquiring the disease diagnosis confidence coefficient of the symptom set and the symptom diagnosis confidence coefficient of the disease set and acquiring the most relevant disease node corresponding to the current symptom set of the patient; the nursing method module is used for screening out the node of the preferred gynecological nursing method; the nursing feedback module is used for constructing a feedback flow sequence, judging whether to replace a node of a preferred gynecological nursing method according to real-time feedback evaluation grades generated by patients and nursing staff, optimizing each step of the gynecological nursing process of the patients, and improving the gynecological nursing quality of the patients.
Description
Technical Field
The invention relates to the field of gynecological nursing, in particular to an intelligent gynecological nursing method selection system based on cloud computing.
Background
The gynecological nursing field is a field with intensive knowledge, the quality of gynecological nursing mainly depends on the health medical knowledge and clinical experience mastered by doctors, however, the capacity of a single doctor is still very limited, so the quality of gynecological nursing is not high at present, disease diagnosis is one of important links in gynecological nursing, a solid foundation is provided for the treatment and prognosis of patients, the quality of gynecological nursing mainly depends on the medical knowledge and medical experience mastered by doctors, the medical knowledge and accumulated medical experience mastered by single doctors are still limited, and how to improve the clinical diagnosis and treatment level of doctors and reduce the workload of doctors is a problem to be solved urgently.
The comparison document CN116453669A is used for extracting a gynecological care index curve chart set according to a gynecological care disease name, extracting initial to-be-matched index data and an initial to-be-matched index curve chart set respectively in gynecological care index monitoring data and gynecological care index curve chart sets according to-be-matched gynecological care indexes, extracting the initial index curve chart set according to an index fluctuation range, calculating a current index data change value according to the current to-be-matched index data, obtaining a comparison index data change value set, calculating a current index difference value set of the current index data change value and the comparison index data change value, calculating the minimum difference degree of the gynecological care index curve chart set and gynecological care index monitoring data according to the current index difference value set, extracting a target index curve chart set according to the minimum difference degree, and predicting the disease condition gynecological care result of a current patient according to the target index curve chart set;
the comparison document CN115798141A 'a monitoring state alarming method based on big data monitoring' is used for acquiring clinical monitoring information of a plurality of clinical monitoring devices which are in communication connection with a server; and transferring the monitoring process of the clinical monitoring equipment meeting the preset safety monitoring requirement to the step monitoring process for operation, and giving an alarm when the preset safety monitoring requirement is not met. The system realizes synchronous monitoring of clinical physiology and monitoring, carries out process monitoring on clinical physiology data and clinical monitoring data of patients of a plurality of clinical monitoring devices through the same server, realizes automatic adjustment of server resource management, realizes accurate monitoring of gynecological nursing of multiple beds, ensures centralized management of monitoring conditions of multiple beds, timely response to abnormal conditions, ensures normal operation of the server, and carries out emergency early warning at the first time of occurrence of abnormality;
the prior gynecological nursing method intelligent selection technology formalizes the expertise of an expert, and aims to replace the expert to diagnose, the extraction of the experiential knowledge from the expert is labor intensive, and because the expert diagnosis often has intuitiveness, the expert cannot provide the experiential knowledge with direct causality in many times, and most of the prior art aims at a single disease or a special disease, the obtained disease auxiliary diagnosis model cannot provide auxiliary diagnosis nursing service for a large number of basic-level general doctors for common gynecological diseases, and cannot provide self-diagnosis nursing service for a large number of common diseases for patients, how to utilize cloud computing to conduct large-scale data mining, reveal the correlation between the gynecological nursing method and the treatment effect, provide online disease diagnosis, recommend the gynecological nursing method, nursing feedback score and the like, and serve as decision support tools to help doctors and patients to better understand disease states and treatment options, and provide the gynecological nursing method auxiliary service which better meets the needs of the patients and doctors is a problem that we need to solve.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a gynecological nursing method intelligent selection system based on cloud computing, which comprises a monitoring center, wherein the monitoring center is in communication connection with a field semantic database, a symptom diagnosis module, a nursing method module and a nursing feedback module;
the field semantic database comprises a symptom set, a disease set and a gynecological nursing method set, and is used for mapping the symptom set, the disease set and the gynecological nursing method set into a multi-node network based on information provided by field experts;
the symptom diagnosis module is used for acquiring the disease diagnosis confidence coefficient of the symptom set and the symptom diagnosis confidence coefficient of the disease set according to the connection relation between the symptom set and the disease set in the multi-node network, and acquiring the most relevant disease node corresponding to the current symptom set of the patient according to the disease diagnosis confidence coefficient of the symptom set and the symptom diagnosis confidence coefficient of the disease set;
the nursing method module is used for screening out optimal gynecological nursing method nodes from a plurality of gynecological nursing method nodes with field semantic connection relation with most relevant disease nodes according to historical gynecological nursing records of patients;
the nursing feedback module is used for extracting standardized treatment steps in the gynecological nursing method, constructing a feedback flow sequence according to the standardized treatment steps, and judging whether to replace the optimal gynecological nursing method node according to real-time feedback evaluation grades generated by patients and nursing staff.
Further, the process of mapping the symptom set, the disease set, and the gynecological care method set into the multi-node network based on the information provided by the domain expert by the domain semantic database includes:
and taking each symptom in the symptom set, each disease in the disease set and each gynecological nursing method in the gynecological nursing method set as nodes of the multi-node network, acquiring domain semantic connection relations among each symptom, each disease and each gynecological nursing method based on information provided by domain experts, and constructing connection strips among nodes with the domain semantic connection relations in the multi-node network according to the domain semantic connection relations.
Further, the process of obtaining the disease diagnosis confidence of the symptom set and the symptom diagnosis confidence of the disease set by the symptom diagnosis module according to the connection relation between the symptom set and the disease set in the multi-node network comprises the following steps:
acquiring the number of connecting lines of nodes belonging to a disease set, which are connected with each node belonging to the symptom set, in a multi-node network, and acquiring the disease diagnosis confidence coefficient of each node belonging to the symptom set according to the number of the connecting lines;
and then acquiring the number of connecting lines of the nodes belonging to the symptom set connected with each node belonging to the symptom set in the multi-node network and the disease diagnosis confidence coefficient of each connected node belonging to the symptom set, and acquiring the symptom diagnosis confidence coefficient of each node belonging to the disease set according to the number of connecting lines of the connected nodes belonging to the symptom set and the disease diagnosis confidence coefficient of each connected node belonging to the symptom set.
Further, the process of obtaining the most relevant disease node corresponding to the current symptom set of the patient by the symptom diagnosis module according to the disease diagnosis confidence of the symptom set and the symptom diagnosis confidence of the disease set includes:
acquiring a current symptom set of a patient, screening a plurality of disease nodes with field semantic connection relation with the symptom set of the patient from a field semantic database, acquiring disease diagnosis confidence degrees of the plurality of symptom nodes in the symptom set of the patient connected with the disease nodes, and acquiring disease association degrees of the disease nodes according to the disease diagnosis confidence degrees of the plurality of symptom nodes in the symptom set of the patient connected with the disease nodes;
setting the disease association number n, screening out the first n disease nodes with the largest disease association degree, acquiring symptom diagnosis confidence degrees of the first n disease nodes, sequencing the n disease nodes according to the sequence from high to low of the symptom diagnosis confidence degrees, and marking the disease node with the highest sequencing as the most relevant disease node.
Further, the process of selecting the optimal gynecological care method node from the plurality of gynecological care method nodes having the field semantic connection relation with the most relevant disease node according to the historical gynecological care record of the patient by the care method module comprises the following steps:
screening a plurality of gynecological nursing method nodes with field semantic connection relation with most relevant disease nodes from a field semantic database, acquiring historical gynecological nursing records of a patient, acquiring a gynecological nursing method of the same historical symptom set as the current symptom set of the patient according to the historical gynecological nursing records, and marking the gynecological nursing method node with the highest ranking as the optimal gynecological nursing method node according to the feedback evaluation level, the selection frequency and the number of connecting wires of the nodes belonging to the disease set connected by the node belonging to the gynecological nursing method.
Further, the nursing feedback module extracts a standardized treatment step in the gynecological nursing method, and the process of constructing a feedback flow sequence according to the standardized treatment step includes:
acquiring a gynecological care method in an optimal gynecological care method node, extracting a standardized treatment step in the gynecological care method, and segmenting the gynecological care method according to the standardized treatment step in the gynecological care method;
the standardized treatment step is used as a sectioning node of a gynecological nursing method, the gynecological nursing method is divided into a plurality of gynecological nursing flow subsequences, and the ending time stamp of each gynecological nursing flow subsequence is used as a feedback time point corresponding to the gynecological nursing flow subsequence;
and acquiring the treatment content in the gynecological nursing flow subsequence, matching the corresponding feedback monitoring content according to the treatment content, and constructing a feedback flow sequence according to the feedback time point and the feedback monitoring content.
Further, the process of the nursing feedback module judging whether to replace the optimal gynecological nursing method node according to the real-time feedback evaluation level generated by the patient and the nursing staff comprises the following steps:
acquiring a real-time feedback evaluation level generated by a patient and a nursing staff according to a feedback flow sequence, acquiring a feedback evaluation level threshold time sequence of the feedback flow sequence, and comparing the real-time feedback evaluation level with the feedback evaluation level threshold time sequence in a segmentation way;
and if the real-time feedback evaluation level is smaller than the feedback evaluation level threshold corresponding to the feedback time point, replacing the current optimal gynecological nursing method node.
Further, the process of the nursing feedback module for replacing the optimal gynecological nursing method node comprises the following steps:
and removing the current optimal gynecological nursing method node, acquiring a plurality of other gynecological nursing method nodes with field semantic connection relation with the most relevant disease node, screening out the gynecological nursing method with the highest priority level according to the priority levels of the other gynecological nursing method nodes, and marking the gynecological nursing method as the optimal gynecological nursing method node.
Compared with the prior art, the invention has the beneficial effects that:
1. the system can timely adjust the treatment scheme according to the feedback of the patient and the actual treatment response, extract the standardized treatment steps in the gynecological treatment method through the nursing feedback module, construct a feedback flow sequence according to the standardized treatment steps, and judge whether to replace the node of the optimal gynecological treatment method according to the real-time feedback evaluation level generated by the patient and nursing staff; when the gynecological nursing treatment of the patient no longer follows the established feedback evaluation level, measures can be quickly found and taken to ensure that the gynecological nursing treatment of the patient is correctly readjusted, avoid the condition that the hospitalization time is prolonged or the resources are wasted, optimize each step of the gynecological nursing process of the patient, ensure the continuity and coordination of gynecological nursing and improve the gynecological nursing quality of the patient.
2. The symptom diagnosis module obtains the most relevant disease nodes corresponding to the current symptom set of the patient according to the disease diagnosis confidence coefficient of the symptom set and the symptom diagnosis confidence coefficient of the disease set; in general, a certain symptom shows common clinical manifestation symptoms for most diseases, and common symptoms include fever, headache, fatigue, abdominal pain, skin change and the like, when the disease types associated with one symptom are more, the probability of diagnosing the disease through the symptom is lower, the contribution rate of the symptom to disease diagnosis is determined by setting disease diagnosis confidence coefficient to the symptom set, and the disease diagnosis accuracy is improved.
Drawings
Fig. 1 is a schematic diagram of a gynecological care method intelligent selection system based on cloud computing according to an embodiment of the application.
Detailed Description
The following description of the embodiments of the present application, taken in conjunction with the accompanying drawings, clearly and completely describes the technical solutions of the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
As shown in fig. 1, the gynecological nursing method intelligent selection system based on cloud computing comprises a monitoring center, wherein the monitoring center is in communication connection with a field semantic database, a symptom diagnosis module, a nursing method module and a nursing feedback module;
the field semantic database comprises a symptom set, a disease set and a gynecological nursing method set, and is used for mapping the symptom set, the disease set and the gynecological nursing method set into a multi-node network based on information provided by field experts;
the symptom diagnosis module is used for acquiring the disease diagnosis confidence coefficient of the symptom set and the symptom diagnosis confidence coefficient of the disease set according to the connection relation between the symptom set and the disease set in the multi-node network, and acquiring the most relevant disease node corresponding to the current symptom set of the patient according to the disease diagnosis confidence coefficient of the symptom set and the symptom diagnosis confidence coefficient of the disease set;
the nursing method module is used for screening out optimal gynecological nursing method nodes from a plurality of gynecological nursing method nodes with field semantic connection relation with most relevant disease nodes according to historical gynecological nursing records of patients;
the nursing feedback module is used for extracting standardized treatment steps in the gynecological nursing method, constructing a feedback flow sequence according to the standardized treatment steps, and judging whether to replace the optimal gynecological nursing method node according to real-time feedback evaluation grades generated by patients and nursing staff.
It should be further noted that, in the implementation process, the process of mapping the symptom set, the disease set and the gynecological nursing method set into the multi-node network based on the information provided by the domain expert by the domain semantic database includes:
and taking each symptom in the symptom set, each disease in the disease set and each gynecological nursing method in the gynecological nursing method set as nodes of the multi-node network, acquiring domain semantic connection relations among each symptom, each disease and each gynecological nursing method based on information provided by domain experts, and constructing connection strips among nodes with the domain semantic connection relations in the multi-node network according to the domain semantic connection relations.
It should be further noted that, based on the information provided by the field specialist, related concepts and attributes such as diseases, symptoms, and gynecological nursing techniques are described, and based on the ontology definition, some general knowledge in the field of health care is described, that is, example data marked by related concepts and semantic relationships thereof, for example, if gynecological nursing is performed for patients with hypertension, not only blood pressure needs to be monitored and controlled, but also gynecological nursing such as headache, eye flowers, hypodynamia, etc. may need to be performed according to symptoms of the patients, whereas for diabetics, manifestations describing "diabetes" include "polydipsia", "polyphagia", "polyuria", "weight loss" and the like, besides controlling blood sugar level, personalized gynecological nursing may also need to be performed for their foot health care, diet plan and exercise plan.
It should be further noted that, in the implementation process, the process of obtaining the disease diagnosis confidence of the symptom set and the symptom diagnosis confidence of the disease set by the symptom diagnosis module according to the connection relationship between the symptom set and the disease set in the multi-node network includes:
acquiring the number of connecting lines of nodes belonging to a disease set, which are connected with each node belonging to the symptom set, in a multi-node network, acquiring the disease diagnosis confidence coefficient of each node belonging to the symptom set according to the number of the connecting lines, wherein the formula for acquiring the disease diagnosis confidence coefficient of each node is as follows:
；
wherein Di is disease diagnosis confidence coefficient of the ith symptom, and Ni is the number of connecting wires of the ith symptom;
then, the number of connecting wires of the nodes belonging to the symptom set connected with each node belonging to the symptom set in the multi-node network and the disease diagnosis confidence coefficient of each connected node belonging to the symptom set are obtained, and the symptom diagnosis confidence coefficient of each node belonging to the disease set is obtained according to the number of connecting wires of the connected nodes belonging to the symptom set and the disease diagnosis confidence coefficient of each connected node belonging to the symptom set, wherein the calculation formula for obtaining the symptom diagnosis confidence coefficient of each node is as follows:
；
wherein Sj is symptom diagnosis confidence of the j-th disease, djn is disease diagnosis confidence of the n-th symptom node connected with the j-th disease, nj is the number of connecting lines of the nodes connected with the j-th disease and belonging to the symptom set, n is the counting identification of the nodes connected with the j-th disease and belonging to the symptom set, and a1 and a2 are weight factors.
The significance of obtaining disease diagnosis confidence for symptom sets is: in general, a certain symptom shows common clinical manifestation symptoms for most diseases, and common symptoms include fever, headache, fatigue, abdominal pain, skin change and the like, when the disease types associated with one symptom are more, the probability of diagnosing the disease through the symptom is lower, the contribution rate of the symptom to disease diagnosis is determined by setting disease diagnosis confidence coefficient to the symptom set, and the disease diagnosis accuracy is improved.
It should be further noted that, in the implementation process, the process of obtaining the most relevant disease node corresponding to the current symptom set of the patient by the symptom diagnosis module according to the disease diagnosis confidence of the symptom set and the symptom diagnosis confidence of the disease set includes:
acquiring a current symptom set of a patient, screening a plurality of disease nodes with field semantic connection relation with the symptom set of the patient from a field semantic database, acquiring disease diagnosis confidence degrees of the plurality of symptom nodes in the symptom set of the patient connected with the disease nodes, and acquiring disease association degrees of the disease nodes according to the disease diagnosis confidence degrees of the plurality of symptom nodes in the symptom set of the patient connected with the disease nodes; the association formula for obtaining the disease association degree of the disease node is as follows:
；
wherein R represents the disease association of the disease node, dq represents the disease diagnosis confidence of the q-th symptom node in the symptom set of the patient to which the disease node is connected, and N represents the total number of symptom nodes in the symptom set of the patient to which the disease node is connected.
Setting the disease association number n, screening out the first n disease nodes with the largest disease association degree, acquiring symptom diagnosis confidence degrees of the first n disease nodes, sequencing the n disease nodes according to the sequence from high to low of the symptom diagnosis confidence degrees, and marking the disease node with the highest sequencing as the most relevant disease node.
It should be further noted that, in the specific implementation process, the nursing method module screens out the optimal gynecological nursing method node from the plurality of gynecological nursing method nodes having the field semantic connection relationship with the most relevant disease node according to the historical gynecological nursing record of the patient, and the process includes:
screening a plurality of gynecological nursing method nodes with field semantic connection relation with most relevant disease nodes from a field semantic database, acquiring historical gynecological nursing records of a patient, acquiring a gynecological nursing method of the same historical symptom set as the current symptom set of the patient and feedback evaluation grades, selection frequencies of the gynecological nursing methods according to the historical gynecological nursing records, acquiring priority grades of the gynecological nursing methods according to the feedback evaluation grades, the selection frequencies and the number of connecting wires, and marking the gynecological nursing method node with the highest ranking as the optimal gynecological nursing method node;
the calculation formula for acquiring the priority level of the gynecological nursing method is as follows:
；
fk is the priority level of the kth gynecological nursing method; yk is the feedback evaluation grade of the kth gynecological care method; uk is the frequency of selection of the kth gynecological care method; zk is the number of connecting lines of nodes belonging to the disease set connected by the kth gynecological nursing method; a3, a4 and a5 are weight factors.
It should be further noted that, in the specific implementation process, the nursing feedback module extracts a standardized treatment step in the gynecological nursing method, and the process of constructing the feedback flow sequence according to the standardized treatment step includes:
acquiring a gynecological care method in an optimal gynecological care method node, extracting a standardized treatment step in the gynecological care method, and segmenting the gynecological care method according to the standardized treatment step in the gynecological care method;
the standardized treatment step is used as a sectioning node of a gynecological nursing method, the gynecological nursing method is divided into a plurality of gynecological nursing flow subsequences, and the ending time stamp of each gynecological nursing flow subsequence is used as a feedback time point corresponding to the gynecological nursing flow subsequence;
and acquiring the treatment content in the gynecological nursing flow subsequence, matching the corresponding feedback monitoring content according to the treatment content, and constructing a feedback flow sequence according to the feedback time point and the feedback monitoring content.
It should be further noted that, in the implementation process, the process of judging whether to replace the optimal gynecological nursing method node by the nursing feedback module according to the real-time feedback evaluation level generated by the patient and the nursing staff includes:
acquiring a real-time feedback evaluation level generated by a patient and a nursing staff according to a feedback flow sequence, wherein the real-time feedback evaluation level is determined by the treatment effect and satisfaction degree of the patient, acquiring a feedback evaluation level threshold time sequence of the feedback flow sequence, and comparing the real-time feedback evaluation level with the feedback evaluation level threshold time sequence in a segmented manner;
and if the real-time feedback evaluation level is smaller than the feedback evaluation level threshold corresponding to the feedback time point, replacing the current optimal gynecological nursing method node.
It should be further noted that, in the implementation process, the process of replacing the optimal gynecological care method node by the care feedback module includes:
and removing the current optimal gynecological nursing method node, acquiring a plurality of other gynecological nursing method nodes with field semantic connection relation with the most relevant disease node, screening out the gynecological nursing method with the highest priority level according to the priority levels of the other gynecological nursing method nodes, and marking the gynecological nursing method as the optimal gynecological nursing method node.
It should be further noted that, if all gynecological nursing methods of the current most relevant disease node do not meet the feedback evaluation level requirement, acquiring a gynecological nursing method for replacing the current most relevant disease node according to the symptom diagnosis confidence of the disease node, and then selecting the replaced gynecological nursing method of the most relevant disease node.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (7)
1. The gynecological nursing method intelligent selection system based on cloud computing comprises a monitoring center, and is characterized in that the monitoring center is in communication connection with a field semantic database, a symptom diagnosis module, a nursing method module and a nursing feedback module;
the field semantic database comprises a symptom set, a disease set and a gynecological nursing method set, and is used for mapping the symptom set, the disease set and the gynecological nursing method set into a multi-node network based on information provided by field experts;
the process of mapping the symptom set, the disease set, and the gynecological care method set into the multi-node network based on the information provided by the domain expert includes:
taking each symptom in the symptom set, each disease in the disease set and each gynecological nursing method in the gynecological nursing method set as nodes of the multi-node network, acquiring domain semantic connection relations among each symptom, each disease and each gynecological nursing method based on information provided by domain experts, and constructing connection strips among nodes with domain semantic connection relations in the multi-node network according to the domain semantic connection relations;
the symptom diagnosis module is used for acquiring the disease diagnosis confidence coefficient of the symptom set and the symptom diagnosis confidence coefficient of the disease set according to the connection relation between the symptom set and the disease set in the multi-node network, and acquiring the most relevant disease node corresponding to the current symptom set of the patient according to the disease diagnosis confidence coefficient of the symptom set and the symptom diagnosis confidence coefficient of the disease set;
the nursing method module is used for screening out optimal gynecological nursing method nodes from a plurality of gynecological nursing method nodes with field semantic connection relation with most relevant disease nodes according to historical gynecological nursing records of patients;
the nursing feedback module is used for extracting standardized treatment steps in the gynecological nursing method, constructing a feedback flow sequence according to the standardized treatment steps, and judging whether to replace the optimal gynecological nursing method node according to real-time feedback evaluation grades generated by patients and nursing staff.
2. The intelligent selection system of a gynecological nursing method based on cloud computing as set forth in claim 1, wherein the process of obtaining the disease diagnosis confidence of the symptom set and the symptom diagnosis confidence of the disease set by the symptom diagnosis module according to the connection relationship between the symptom set and the disease set in the multi-node network includes:
acquiring the number of connecting lines of nodes belonging to a disease set, which are connected with each node belonging to the symptom set, in a multi-node network, and acquiring the disease diagnosis confidence coefficient of each node belonging to the symptom set according to the number of the connecting lines;
and then acquiring the number of connecting lines of the nodes belonging to the symptom set connected with each node belonging to the symptom set in the multi-node network and the disease diagnosis confidence coefficient of each connected node belonging to the symptom set, and acquiring the symptom diagnosis confidence coefficient of each node belonging to the disease set according to the number of connecting lines of the connected nodes belonging to the symptom set and the disease diagnosis confidence coefficient of each connected node belonging to the symptom set.
3. The cloud computing-based gynecological care method intelligent selection system according to claim 2, wherein the process of the symptom diagnosis module obtaining the most relevant disease node corresponding to the current symptom set of the patient according to the disease diagnosis confidence of the symptom set and the symptom diagnosis confidence of the disease set comprises:
acquiring a current symptom set of a patient, screening a plurality of disease nodes with field semantic connection relation with the symptom set of the patient from a field semantic database, acquiring disease diagnosis confidence degrees of the plurality of symptom nodes in the symptom set of the patient connected with the disease nodes, and acquiring disease association degrees of the disease nodes according to the disease diagnosis confidence degrees of the plurality of symptom nodes in the symptom set of the patient connected with the disease nodes;
setting the disease association number n, screening out the first n disease nodes with the largest disease association degree, acquiring symptom diagnosis confidence degrees of the first n disease nodes, sequencing the n disease nodes according to the sequence from high to low of the symptom diagnosis confidence degrees, and marking the disease node with the highest sequencing as the most relevant disease node.
4. A cloud computing based gynecological care method intelligent selection system according to claim 3, wherein the process of the care method module selecting an optimal gynecological care method node from a plurality of gynecological care method nodes having field semantic connection relation with the most relevant disease node according to the patient's historical gynecological care records comprises:
screening a plurality of gynecological nursing method nodes with field semantic connection relation with most relevant disease nodes from a field semantic database, acquiring historical gynecological nursing records of a patient, acquiring a gynecological nursing method of the same historical symptom set as the current symptom set of the patient according to the historical gynecological nursing records, and marking the gynecological nursing method node with the highest ranking as the optimal gynecological nursing method node according to the feedback evaluation level, the selection frequency and the number of connecting wires of the nodes belonging to the disease set connected by the node belonging to the gynecological nursing method.
5. The intelligent selection system of gynecological nursing method based on cloud computing as set forth in claim 4, wherein the nursing feedback module extracts a standardized treatment step in the gynecological nursing method, and the process of constructing a feedback flow sequence according to the standardized treatment step includes:
acquiring a gynecological care method in an optimal gynecological care method node, extracting a standardized treatment step in the gynecological care method, and segmenting the gynecological care method according to the standardized treatment step in the gynecological care method;
the standardized treatment step is used as a sectioning node of a gynecological nursing method, the gynecological nursing method is divided into a plurality of gynecological nursing flow subsequences, and the ending time stamp of each gynecological nursing flow subsequence is used as a feedback time point corresponding to the gynecological nursing flow subsequence;
and acquiring the treatment content in the gynecological nursing flow subsequence, matching the corresponding feedback monitoring content according to the treatment content, and constructing a feedback flow sequence according to the feedback time point and the feedback monitoring content.
6. The intelligent gynecological care method selection system based on cloud computing as claimed in claim 5, wherein the process of judging whether to replace the optimal gynecological care method node by the care feedback module according to the real-time feedback evaluation level generated by the patient and the nursing staff comprises the following steps:
acquiring a real-time feedback evaluation level generated by a patient and a nursing staff according to a feedback flow sequence, acquiring a feedback evaluation level threshold time sequence of the feedback flow sequence, and comparing the real-time feedback evaluation level with the feedback evaluation level threshold time sequence in a segmentation way;
and if the real-time feedback evaluation level is smaller than the feedback evaluation level threshold corresponding to the feedback time point, replacing the current optimal gynecological nursing method node.
7. The intelligent gynecological care method selecting system based on cloud computing as claimed in claim 6, wherein the process of replacing the optimal gynecological care method node by the care feedback module comprises the following steps:
and removing the current optimal gynecological nursing method node, acquiring a plurality of other gynecological nursing method nodes with field semantic connection relation with the most relevant disease node, screening out the gynecological nursing method with the highest priority level according to the priority levels of the other gynecological nursing method nodes, and marking the gynecological nursing method as the optimal gynecological nursing method node.
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