CN113506625A - Diagnosis and treatment suggestion scoring system based on csco guide - Google Patents
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Abstract
The invention discloses a diagnosis and treatment suggestion scoring system based on a CSCO guideline, which belongs to the technical field of diagnosis and treatment suggestion scoring systems, wherein a user information management module is electrically connected with an illness state evaluation prediction module, the illness state evaluation prediction module is electrically connected with a physiotherapy scheme decision generation module, the physiotherapy scheme decision generation module is a rule logic algorithm model, the rule logic algorithm model is electrically connected with a score statistical ranking module, the score statistical ranking module is electrically connected with a surgical treatment optimal scheme establishing module in an output mode, the illness state evaluation prediction module is electrically connected with a knowledge database in an input mode, a data retrieval module is electrically connected with the rule logic algorithm model based on a CSCO guideline to determine different diagnosis and treatment schemes, and then the different diagnosis and treatment schemes are scored, so that the accuracy of illness state identification of clinical patients is greatly improved, the construction of the disease knowledge map is guided by clinical data through mapping, and the scheme optimization is high.
Description
Technical Field
The invention relates to the technical field of diagnosis and treatment suggestion scoring systems, in particular to a diagnosis and treatment suggestion scoring system based on a csco guide.
Background
Before a hospital performs a surgical operation on a patient, nursing staff related to the surgical operation needs to visit the patient before the surgery, the aim is to know the condition of the patient, communicate with the patient to eliminate anxiety and fear psychology of the patient caused by the unknown surgical operation so as to improve the cooperation degree of nurses and the patient in the operation, be beneficial to increasing the tolerance of the patient to the surgical operation, shortening the surgical operation time, relieving the pain of the patient and ensuring the smooth operation of the operation, an important demonstration link is to determine the content to be demonstrated to the patient according to the actual physiological condition of the patient in the process of the preoperative visit, the prior solution is that the nursing staff knows medical history, physiological parameters and examination results of the patient in advance, then sums up and understands related data, and finally verbalizes or transfers to the patient in the process of the preoperative visit, and the related data comprise some attentions and basic knowledge of the surgical operation to be performed by the patient, therefore, different related preoperative preparation and anesthesia operation schemes can be made according to different factors before an operation, when different schemes appear, the schemes need to be comprehensively and accurately assessed and evaluated through the fastest assessment and scoring method in the shortest time so as to determine the best implementation scheme, the existing scheme does not have a corresponding scoring standard to optimize a diagnosis and treatment assessment suggestion after the making is completed, and therefore, a diagnosis and treatment suggestion scoring system based on the csco guideline is provided.
Disclosure of Invention
The invention aims to provide a diagnosis and treatment suggestion scoring system based on a csco guideline, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a diagnosis and treatment suggestion scoring system based on a CSCO guideline comprises a human-computer interaction input module, wherein the human-computer interaction input module is electrically connected with a user information management module in an output mode, the user information management module is electrically connected with an illness state evaluation prediction module in an output mode, the illness state evaluation prediction module is electrically connected with a physiotherapy scheme decision generation module in an output mode, the physiotherapy scheme decision generation module is a rule logic algorithm model, the rule logic algorithm model is electrically connected with a score statistical sorting module in an output mode, the score statistical sorting module is electrically connected with a surgical treatment optimal scheme establishing module in an output mode, the illness state evaluation prediction module is electrically connected with a knowledge database in an input mode, the knowledge database is electrically connected with a data retrieval module in an output mode, and the data retrieval module is electrically connected with the rule logic algorithm model in an output mode based on a CSCO guideline.
Preferably, the user information management module is configured to store patient information and diagnosis and treatment stage information, where the diagnosis and treatment stage information represents a disease diagnosis and treatment operation that a patient has performed.
Preferably, the disease condition evaluation and prediction module generates disease condition information evaluation and prediction according to the physiological parameters monitored by the user information management module to establish the grade degree of the health condition, so that the health condition of the patient is classified into A, B, C, D types according to the health condition of the patient, then the disease risk evaluation is performed according to the risk evaluation table, the evaluation is divided into four risk degree grades of 1-grade low risk, 2-grade medium risk, 3-grade high risk and 4-grade high risk, and meanwhile, each grade is subjected to grading processing.
Preferably, the physical therapy scheme decision generation module can receive the patient information and the diagnosis and treatment stage information of the disease condition evaluation prediction module, can use the patient information and the diagnosis and treatment stage information as a to-be-inferred fact, process the to-be-inferred fact by using a natural language processing rule, and map the to-be-inferred fact into the knowledge graph, so that the knowledge graph can be automatically mapped onto the disease body, and the physical therapy scheme decision generation module is used for generating a corresponding disease diagnosis and treatment scheme according to the patient information and the diagnosis and treatment stage information, and recommending the scheme according to different side points such as physical therapy basic cost, operation risk, conservative treatment, emergency treatment, operation healing cycle and wound area size, so as to generate the diagnosis and treatment scheme according to the disease body and the knowledge graph.
Preferably, the rule editing algorithm model performs weighting processing on the health condition grades, the risk degree grades and the corresponding diagnosis and treatment schemes, then multiplies the weighting coefficient of each grade by the score under the grade to serve as a diagnosis and decision result under the diagnosis and treatment scheme, and meanwhile recommends the diagnosis and decision result based on the NCCN evidence and the CSCO guideline to perform overall scheme scoring on the diagnosis and treatment scheme.
Preferably, the classification statistical ranking module ranks the diagnosis and treatment technical schemes in the rule editing algorithm model based on the sum of the scores of the diagnosis and treatment technical schemes and the scheme so as to obtain the optimized diagnosis and treatment technical schemes under different emphasis points.
The using method of the scoring system comprises the following steps:
s1: when in use, the basic information input of the patient is realized through the human-computer interaction input module, the user information management module is used for storing the information of the patient and the information of the diagnosis and treatment stage, wherein the diagnosis and treatment stage information represents the disease diagnosis and treatment operations of the patient, the disease condition evaluation and prediction module generates disease condition information according to the physiological parameters monitored by the user information management module, meanwhile, the disease condition evaluation and prediction module can extract medical records and professional clinical medical data from the knowledge database to evaluate and predict the disease condition, to establish a degree of grade of health status, which is based on the patient's health status, classified into A, B, C, D categories, then, performing morbidity risk assessment according to a risk assessment table, dividing the assessment into four risk degree grades of 1-grade low risk, 2-grade medium risk, 3-grade high risk and 4-grade high risk, and simultaneously performing scoring processing on each grade;
S2: the physical therapy scheme decision generation module can receive the patient information and the diagnosis and treatment stage information of the disease evaluation prediction module 3, can be used as a to-be-inferred fact, processes the to-be-inferred fact by utilizing a natural language processing rule, and maps the to-be-inferred fact into a knowledge graph, so that the knowledge graph can be automatically mapped onto a disease body, the physical therapy scheme decision generation module is used for generating a corresponding disease diagnosis and treatment scheme according to the patient information and the diagnosis and treatment stage information, and meanwhile, scheme recommendation is carried out on different side points such as basic physical therapy cost, operation risk, conservative treatment, emergency treatment, operation healing period, wound area size and the like, so that the diagnosis and treatment scheme is generated according to the disease body and the knowledge graph, and the data retrieval module extracts a related knowledge graph network from a knowledge database and sends the related knowledge graph network to a rule editing algorithm model for scheme evaluation and scoring optimization;
s3: the rule editing algorithm model carries out weighted reprocessing on the health condition grades, the risk degree grades and the corresponding diagnosis and treatment schemes, then the weight coefficient of each grade is multiplied by the grade to serve as a diagnosis and decision result under the diagnosis and treatment scheme, meanwhile, the diagnosis and decision result is recommended to carry out integral scheme grading on the diagnosis and treatment scheme based on NCCN evidence and CSCO guidelines, the classification statistical sorting module sorts the diagnosis and treatment technical scheme and the scheme grading sum in the rule editing algorithm model, the optimal surgical treatment scheme establishing module obtains the optimized diagnosis and treatment technical schemes under different side points, the patient condition information is combined with the actual diagnosis and treatment clinical data to carry out deep analysis and identification on specific case conditions based on the existing knowledge graph library to determine different diagnosis and treatment schemes, then the grading processing is carried out on the different diagnosis and treatment schemes, and the diagnosis and treatment accuracy of clinical patients is greatly improved, the construction of the disease knowledge graph is guided by clinical data through mapping, and the diagnosis and treatment rule has practical value for disease diagnosis and treatment.
Compared with the prior art, the invention has the beneficial effects that:
the invention has reasonable structural design, the illness state evaluation and prediction module generates illness state information evaluation and prediction according to physiological parameters monitored by the user information management module to determine the grade degree of the health condition, the physical therapy scheme decision generation module can receive patient information and diagnosis and treatment stage information of the illness state evaluation and prediction module, the physical therapy scheme decision generation module is used for generating a corresponding illness state diagnosis and treatment scheme according to the patient information and the diagnosis and treatment stage information, the data retrieval module extracts a related knowledge map network from a knowledge database and sends the related knowledge map network to the rule editing algorithm model for carrying out scheme evaluation and grade optimization, the rule editing algorithm model carries out weighting and weight processing on the health state grade, the danger degree grade and the corresponding diagnosis and treatment scheme, then the weight coefficient of each grade is multiplied by the grade under the grade to be used as a diagnosis decision result under the diagnosis and treatment scheme, and the diagnosis decision result is recommended to carry out integral scheme grading on the diagnosis and treatment scheme based on NCCN evidence and CSCO guidelines, the classification statistics and sequencing module sequences the diagnosis and treatment technical scheme and the scheme score sum in the rule editing algorithm model, the optimal surgical treatment scheme establishing module obtains the optimal diagnosis and treatment technical scheme under different emphasis points, patient condition information is combined with actual diagnosis and treatment clinical data to carry out deep analysis and identification on specific case conditions based on the existing knowledge map library to determine different diagnosis and treatment schemes, then the different diagnosis and treatment schemes are scored, the accuracy of clinical patient condition identification is greatly improved, and the optimal diagnosis and treatment suggested treatment scheme can be determined in time.
Drawings
Fig. 1 is a working principle diagram of the present invention.
In the figure: 1. a human-computer interaction input module; 2. a user information management module; 3. a disease condition evaluation prediction module; 4. a physical therapy scheme decision generation module; 5. editing an algorithm model according to the rules; 6. a score statistics ordering module; 7. a surgical treatment optimal scheme establishing module; 8. a knowledge database; 9. and a data calling module.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Referring to fig. 1, the present invention provides a technical solution: a diagnosis and treatment suggestion scoring system based on a CSCO guide comprises a man-machine interaction input module 1, wherein the man-machine interaction input module 1 is electrically connected with a user information management module 2 in an output mode, the user information management module 2 is electrically connected with an illness state evaluation and prediction module 3 in an output mode, the illness state evaluation and prediction module 3 is electrically connected with a physiotherapy scheme decision generation module 4 in an output mode, the physiotherapy scheme decision generation module 4 is provided with a rule logic algorithm model 5, the rule logic algorithm model 5 is electrically connected with a score statistical sorting module 6 in an output mode, the score statistical sorting module 6 is electrically connected with a surgery treatment optimal scheme establishing module 7 in an output mode, the illness state evaluation and prediction module 3 is electrically connected with a knowledge database 8 in an input mode, the knowledge database 8 is electrically connected with a data retrieval module 9 in an output mode, and the data retrieval module 9 is electrically connected with the rule logic algorithm model 5 in an output mode based on a CSCO guide.
The user information management module 2 is used for storing patient information and diagnosis and treatment stage information, wherein the diagnosis and treatment stage information represents disease diagnosis and treatment operations which are already performed by a patient;
the disease condition evaluation and prediction module 3 generates disease condition information evaluation and prediction according to the physiological parameters monitored by the user information management module 2 to determine the grade degree of the health condition, so that the health condition of the patient is divided into A, B, C, D types according to the health condition of the patient, then the disease risk evaluation is carried out according to a risk evaluation table, the evaluation is divided into four risk degree grades of 1-grade low risk, 2-grade medium risk, 3-grade high risk and 4-grade high risk, and meanwhile, each grade is subjected to grading processing;
the physical therapy scheme decision generation module 4 can receive the patient information and the diagnosis and treatment stage information of the disease condition evaluation prediction module 3, can be used as a to-be-inferred fact, processes the to-be-inferred fact by using a natural language processing rule, and maps the to-be-inferred fact into a knowledge graph, so that the knowledge graph can be automatically mapped onto a disease body, the physical therapy scheme decision generation module 4 is used for generating a corresponding disease diagnosis and treatment scheme according to the patient information and the diagnosis and treatment stage information, and meanwhile, carrying out scheme recommendation from different side points such as physical therapy basic cost, operation risk, conservative treatment, emergency treatment, operation healing cycle and wound area size, and accordingly generating the diagnosis and treatment scheme according to the disease body and the knowledge graph;
The rule editing algorithm model 5 carries out weighted weight processing on the health condition grades, the risk degree grades and the corresponding diagnosis and treatment schemes, then the weight coefficient of each grade is multiplied by the score under the grade to serve as a diagnosis and decision result under the diagnosis and treatment scheme, and meanwhile, the diagnosis and decision result carries out integral scheme scoring on the diagnosis and treatment scheme based on NCCN evidence and CSCO guideline recommendation, wherein the qualification of meeting the NCCN 1 evidence or CSCO BC guideline I-grade recommendation is completely met, and the score is 3; the qualitative rating of meeting the NCCN 2A evidence or CSCO BC guideline level II recommendation, but not meeting the condition of the 'completely meeting' layer is a high-level meeting, and the rating is 2 points; the qualitative rating of compliance with NCCN 2B evidence or CSCO BC guideline III-level recommendation, but not compliance with the "high-level compliance" condition is low-level compliance, and the rating is 1 point; the qualitative results that do not meet NCCN and CSCOBC guidelines are completely met, and the score is 0;
the classification statistical sorting module 6 sorts the diagnosis and treatment technical schemes and the scheme score sum in the rule editing algorithm model 5 to obtain optimized diagnosis and treatment technical schemes under different emphasis points.
The using method of the scoring system comprises the following steps:
s1: when in use, the human-computer interaction input module 1 is used for realizing the basic information input of a patient, the user information management module 2 is used for storing the information of the patient and the information of the diagnosis and treatment stage, wherein the diagnosis and treatment stage information represents the disease diagnosis and treatment operations which are already carried out by the patient, the disease condition evaluation and prediction module 3 generates disease condition information according to the physiological parameters monitored by the user information management module 2, meanwhile, the disease condition evaluation and prediction module 3 can extract medical records and professional clinical medical data from the knowledge database 8 to evaluate and predict the disease condition, to establish a degree of grade of health status, which is based on the patient's health status, classified into A, B, C, D categories, then, performing morbidity risk assessment according to a risk assessment table, dividing the assessment into four risk degree grades of 1-grade low risk, 2-grade medium risk, 3-grade high risk and 4-grade high risk, and simultaneously performing scoring processing on each grade;
S2: the physical therapy scheme decision generation module 4 can receive the patient information and the diagnosis and treatment stage information of the disease condition evaluation prediction module 3, can use the information as the fact to be inferred, processes the fact to be inferred by using the natural language processing rule, and maps the acquired knowledge map to a knowledge map, so that the knowledge map can be automatically mapped to a disease body, the physical therapy scheme decision generation module 4 is used for generating a corresponding disease diagnosis and treatment scheme according to the patient information and the diagnosis and treatment stage information, meanwhile, plan recommendation is carried out from different emphasis points such as basic physical therapy cost, operation risk, conservative treatment, emergency treatment, operation healing period, wound area and the like, therefore, a diagnosis and treatment scheme is generated according to the disease body and the knowledge graph, the data calling module 9 extracts a related knowledge graph network from the knowledge database 8 and sends the related knowledge graph network to the rule editing algorithm model 5 for scheme evaluation scoring optimization;
s3: the rule editing algorithm model 5 carries out weighting and reprocessing on the health condition grades, the risk degree grades and the corresponding diagnosis and treatment schemes, then the weighting coefficient of each grade is multiplied by the grade under the grade to serve as a diagnosis and decision result under the diagnosis and treatment scheme, meanwhile, the diagnosis and decision result is recommended to carry out integral scheme grading on the diagnosis and treatment scheme based on NCCN evidence and CSCO guidelines, the classification statistical ranking module 6 ranks the diagnosis and treatment technical scheme and the scheme grading sum in the rule editing algorithm model 5, the operation treatment optimal scheme establishing module 7 obtains the optimized diagnosis and treatment technical schemes under different side points, the patient condition information is combined with actual diagnosis and treatment clinical data to carry out deep analysis and identification on specific case conditions based on the existing knowledge graph base to determine different diagnosis and treatment schemes, and then the grading processing is carried out on the different diagnosis and treatment schemes, the disease condition identification accuracy of clinical patients is greatly improved, the construction of the disease knowledge graph is guided by clinical data through mapping, and the diagnosis and treatment rule has practical value aiming at the diagnosis and treatment of diseases.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. A diagnosis and treatment suggestion scoring system based on csco guidelines comprises a human-computer interaction input module (1), and is characterized in that: the human-computer interaction input module (1) is electrically connected with the user information management module (2), the user information management module (2) is electrically connected with the illness state evaluation prediction module (3), the illness state evaluation and prediction module (3) is electrically connected with the physical therapy scheme decision generation module (4), the physical therapy scheme decision generation module (4) is a rule logic algorithm model (5), the rule logic algorithm model (5) is electrically connected with a score statistical sorting module (6), the score statistical sorting module (6) is electrically connected with the operation treatment optimal scheme establishing module (7), the illness state evaluation and prediction module (3) is electrically input and connected with a knowledge database (8), the knowledge database (8) is electrically connected with the data calling module (9), the data retrieval module (9) outputs a connection rule logic algorithm model (5) based on CSCO guidelines.
2. The csco guideline-based medical advice scoring system of claim 1, wherein: the user information management module (2) is used for storing patient information and diagnosis and treatment stage information, wherein the diagnosis and treatment stage information represents disease diagnosis and treatment operations which are already performed by a patient.
3. The csco guideline-based medical advice scoring system of claim 2, wherein: the disease condition evaluation and prediction module (3) generates disease condition information evaluation and prediction according to the physiological parameters monitored by the user information management module (2) to determine the grade degree of the health condition, so that the health condition of a patient is divided into A, B, C, D types, then the disease risk evaluation is carried out according to the risk evaluation table, the evaluation is divided into four risk degree grades of 1-grade low risk, 2-grade medium risk, 3-grade high risk and 4-grade high risk, and meanwhile, each grade is subjected to grading processing.
4. The csco guideline-based medical advice scoring system of claim 3, wherein: the physical therapy scheme decision generation module (4) can receive the patient information and the diagnosis and treatment stage information of the disease condition evaluation prediction module (3), can be used as a to-be-inferred fact, processes the to-be-inferred fact by using natural language processing rules, and maps the to-be-inferred fact into the knowledge graph, so that the knowledge graph can be automatically mapped onto the disease body, the physical therapy scheme decision generation module (4) is used for generating a corresponding disease diagnosis and treatment scheme according to the patient information and the diagnosis and treatment stage information, and meanwhile, scheme recommendation is performed according to different side points such as physical therapy basic cost, operation risk, conservative treatment, emergency treatment, operation healing cycle and wound area size, so that the diagnosis and treatment scheme is generated according to the disease body and the knowledge graph.
5. The csco guideline-based medical advice scoring system of claim 1, wherein: the rule editing algorithm model (5) carries out weighting processing on the health condition grades, the risk degree grades and the corresponding diagnosis and treatment schemes, then the weighting coefficient of each grade is multiplied by the score under the grade to serve as a diagnosis and decision result under the diagnosis and treatment scheme, and meanwhile the diagnosis and decision result is recommended to carry out integral scheme scoring on the diagnosis and treatment scheme based on the NCCN evidence and the CSCO guideline.
6. The csco guideline-based medical advice scoring system of claim 1, wherein: the classification statistical sorting module (6) sorts the diagnosis and treatment technical schemes and the scheme score sum in the rule editing algorithm model (5) based on the diagnosis and treatment technical schemes and the scheme score sum so as to obtain the optimized diagnosis and treatment technical schemes under different emphasis points.
7. The csco-guideline-based clinical suggestion scoring system of any one of claims 1-6, wherein: the using method of the scoring system comprises the following steps:
s1: when the system is used, the basic information input of a patient is realized through the human-computer interaction input module (1), the user information management module (2) is used for storing patient information and diagnosis and treatment stage information, wherein the diagnosis and treatment stage information represents disease diagnosis and treatment operations of the patient, the disease condition evaluation prediction module (3) generates disease condition information according to physiological parameters monitored by the user information management module (2), and meanwhile, the disease condition evaluation prediction module (3) can extract medical records and professional clinical medical data from the knowledge database (8) to evaluate and predict disease conditions so as to establish the grade of the health condition, so that the disease conditions are classified into A, B, C, D types based on the health condition of the patient, then the morbidity risk evaluation is performed according to the risk evaluation table, and the evaluation is divided into four dangerous degree grades of 1-grade low risk, 2-grade medium risk, 3-grade high risk and 4-grade high risk, simultaneously, scoring each grade;
S2: the physical therapy scheme decision generation module (4) can receive the patient information and the diagnosis and treatment stage information of the disease condition evaluation prediction module 3, can use the information as the fact to be inferred, processes the fact to be inferred by utilizing a natural language processing rule, and maps the acquired knowledge map to a knowledge map, so that the knowledge map can be automatically mapped to a disease body, a physical therapy scheme decision generation module (4) is used for generating a corresponding disease diagnosis and treatment scheme according to patient information and diagnosis and treatment stage information, meanwhile, plan recommendation is carried out from different emphasis points such as basic physical therapy cost, operation risk, conservative treatment, emergency treatment, operation healing period, wound area and the like, therefore, a diagnosis and treatment scheme is generated according to the disease body and the knowledge graph, the data calling module (9) extracts a related knowledge graph network from the knowledge database (8) and sends the related knowledge graph network to the rule editing algorithm model (5) for scheme evaluation and scoring optimization;
s3: the rule editing algorithm model (5) carries out weighted weight processing on the health condition grades, the risk degree grades and the corresponding diagnosis and treatment schemes, then the weight coefficient of each grade is multiplied by the score under the grade to be used as a diagnosis decision result under the diagnosis and treatment scheme, meanwhile, the diagnosis decision result is recommended to carry out integral scheme scoring on the diagnosis and treatment scheme based on NCCN evidence and CSCO guidelines, the classification statistical sorting module (6) sorts the diagnosis and treatment scheme and the scheme score sum based on the diagnosis and treatment technical scheme and the scheme score sum in the rule editing algorithm model (5), the operation treatment optimal scheme establishing module (7) obtains the optimized diagnosis and treatment technical schemes under different emphasis points, the patient information is combined with the actual clinical data to carry out deep analysis and identification on the specific case condition based on the existing knowledge map library to determine different diagnosis and treatment schemes, then, the different diagnosis and treatment schemes are scored, the disease condition identification accuracy of clinical patients is greatly improved, the construction of the disease knowledge graph is guided by clinical data through mapping, and the diagnosis and treatment rule has practical value aiming at the diagnosis and treatment of diseases.
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