Digestive tract tumor onset risk intelligent assessment system

Abstract

The invention is applied to the technical field of digestive tract tumor onset assessment, and particularly discloses an intelligent digestive tract tumor onset risk assessment system which comprises an information acquisition unit and a data fusion assessment unit, and the output end of the information acquisition unit is connected with an information classification and arrangement unit. The receiving end of the data fusion evaluation unit is connected with the transmission section of the information classification and arrangement unit, and the data fusion evaluation unit can evaluate the onset risk of the digestive tract tumor based on clinical data. According to the digestive tract tumor onset risk intelligent assessment system, tumor onset related data are extracted through the information acquisition unit and the information classification and arrangement unit, risk factors are classified in detail, the accuracy of assessment results is improved, and the onset risk results of digestive tract tumors are obtained through the data fusion assessment unit. A risk report is formed through the result output unit, so that a patient can intuitively know the risk level.

Description

Technical Field

[0001] The present invention relates to the technical field of digestive tract tumor incidence risk assessment, and in particular to a digestive tract tumor incidence risk intelligent assessment system. Background Art

[0002] Digestive tract tumors refer to a general term for a large class of benign and malignant tumors that originate in the digestive tract. Digestive tract tumors are one of the most common malignant tumors worldwide, including esophageal cancer, gastric cancer, colorectal cancer, etc. The occurrence of digestive tract tumors is closely related to the patient's daily diet and living habits. The symptoms of digestive tract tumors are related to the location of their occurrence. The symptoms of digestive tract tumors in different growth sites vary. The main symptoms of esophageal cancer or gastroesophageal junction cancer are difficulty swallowing after eating. Gastric cancer often presents with upper abdominal discomfort, stomach pain, indigestion or progressive weight loss. Pancreatic cancer often presents with upper abdominal and back pain. Liver cancer and intestinal cancer often present with abdominal distension, intestinal obstruction, and bloody stools. Because their early symptoms are not obvious, most patients are already in the middle and late stages when diagnosed, and the treatment effect is not good.

[0003] Early detection and assessment of the risk of gastrointestinal tumors are of great significance for improving patients' survival rate and quality of life. The lack of early warning for the risk of gastrointestinal tumors and the inability to detect potential risk factors in a timely manner may cause patients to miss the best time for early intervention and treatment, increasing the possibility of disease worsening. Due to the lack of intuitive risk assessment results, the public may have a vague understanding of their own risk of gastrointestinal tumors, making it difficult to take effective preventive measures in daily life, so that patients may not be diagnosed until the disease progresses to a more serious stage. At this time, the cost of treatment is usually higher, which brings a greater financial burden to individuals. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent assessment system for the risk of digestive tract tumors, so as to solve the problem of lack of early warning of the risk of digestive tract tumors mentioned in the above-mentioned background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent assessment system for the risk of gastrointestinal tumors, comprising an information acquisition unit, the output end of which is connected to an information classification and collation unit, and also comprising a data fusion and assessment unit, the receiving end of which is connected to the transmission segment of the information classification and collation unit, and the data fusion and assessment unit can assess the risk of gastrointestinal tumors based on clinical data.

[0006] Preferably, the information collection unit includes a medical institution information collection module, which collects electronic medical records and physical examination report information. The electronic medical record screening collects data related to digestive tract tumors. The information collection unit is also provided with a questionnaire survey, which collects information by publishing online.

[0007] The above technical solution is used to conduct a comprehensive investigation of tumor incidence data, which is convenient for assessing the risk of disease in patients who fill in the questionnaire.

[0008] Preferably, the information classification and sorting unit integrates and classifies the data collected by the data fusion and evaluation unit, and the information classification and sorting unit classifies the factors of gastrointestinal tumor incidence into genetic factors, disease factors, lifestyle factors, environmental factors and psychological factors through the data collected by the medical institution information collection module.

[0009] The above technical solution is used to classify the risk factors for the disease, which is convenient for subsequent corresponding evaluation.

[0010] Preferably, the data fusion evaluation unit comprehensively receives data from the information classification and collating unit and the information acquisition unit, the data fusion evaluation unit collects clinical data through the information acquisition unit, extracts pathogenic factors from the clinical data, and the pathogenic factors are weighted in combination with corresponding proportions of the information classification and collating unit.

[0011] By adopting the above technical solution, the data can be integrated to facilitate the assignment of weights, thereby obtaining an intuitive risk prediction.

[0012] Preferably, the weight assignment is set according to the score.

[0013] By adopting the above technical solution, the evaluation results can be made more mobile through the correspondence of scores, which can provide better warning effects for patients.

[0014] Preferably, the output end to which the weight is assigned is connected to a Cox proportional hazard model and a decision tree, and the Cox proportional hazard model and the decision tree are arranged in parallel.

[0015] By adopting the above technical solution and two evaluation methods, the evaluation results are made more convincing.

[0016] Preferably, the Cox proportional hazard model is provided with multifactor analysis and survival analysis, the multifactor analysis incorporates multiple risk factors for comprehensive gaps, the survival analysis is provided as a predictive analysis based on time factors, the decision tree is provided with importance ranking, the importance ranking is sorted according to weight assignment, the importance ranking is connected with risk stratification, and the risk stratification divides the risk levels based on the branches and nodes of the decision tree.

[0017] With the above technical solution, the Cox proportional hazard model makes the result data more comprehensive through comprehensive analysis and comparison, and the decision tree makes the hierarchical evaluation.

[0018] Preferably, the data fusion evaluation unit is further provided with an evaluation comparison and mutual check, the receiving end of the evaluation comparison and mutual check is connected to the output end of the cox proportional risk model and the decision tree, and the evaluation comparison and mutual check compares and verifies the evaluation results of the cox proportional risk model and the decision tree.

[0019] By adopting the above technical solution, the evaluation structures can verify each other, thereby improving the accuracy of the evaluation.

[0020] Preferably, the output end of the data fusion evaluation unit is connected to the input end of the risk rating unit, and the output end of the risk rating unit is connected to the input end of the result output unit.

[0021] The above technical solution is adopted to facilitate the structure of subsequent feedback evaluation.

[0022] Preferably, the risk rating unit is provided with a risk classification module and a measure retrieval module. The risk classification module performs comprehensive risk classification on the evaluation structure output by the data fusion evaluation unit. The measure retrieval module retrieves preventive measures corresponding to the risk classification module. The result output unit outputs the final evaluation result, which structure includes the disease risk level, risk factors and recommended measures.

[0023] By adopting the above technical solution, an intuitive risk level is given through the result output unit, and the warning effect is better.

[0024] Compared with the prior art, the invention has the following beneficial effects: the intelligent assessment system for the risk of digestive tract tumors:

[0025] 1. The intelligent assessment system for the risk of digestive tract tumors is equipped with an information collection unit, which can achieve comprehensive and accurate data acquisition, covering the patient's basic information, such as age, gender, family medical history, lifestyle information, including eating habits, exercise frequency, smoking and drinking status, etc., as well as clinical examination data, such as blood indicators, imaging results, etc. Through multi-dimensional and multi-angle information collection, it provides a solid and rich data foundation for subsequent risk assessment;

[0026] 2. Furthermore, an information classification and sorting unit is provided. Based on the information collection unit, various risk factors are classified and sorted, which can clearly present the relationship and hierarchy between different factors, help to have a deeper understanding of the occurrence mechanism of the disease, and classify the risk factors in detail, which can accurately identify the unique risk factor combination of each patient, provide a basis for subsequent evaluation plans, and improve the accuracy and pertinence of the evaluation results;

[0027] 3. Furthermore, the Cox proportional hazard model of the data fusion evaluation unit processes data and time-related risk factors, while the decision tree can intuitively reveal the complex relationship and nonlinear characteristics between variables. Through evaluation, comparison and mutual inspection, they can complement each other and reduce the possible bias and error of a single model, thereby more accurately evaluating the risk of gastrointestinal tumors.

[0028] 4. Furthermore, the assessment results are divided accordingly through the risk rating unit, and then a risk report is formed through the result output unit, so that the risk level of disease can be presented to the user in an intuitive manner to show the possibility of disease, so that non-professionals can also quickly understand their own conditions. The clear pointing out of risk factors enables users to clearly know the key factors that affect their own health, which helps to make targeted adjustments and improvements. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the overall connection structure of the present invention;

[0030] Figure 2 This is a schematic diagram of the structure of the information classification and sorting unit of the present invention;

[0031] Figure 3 This is a structural schematic diagram of a data fusion evaluation unit of the present invention;

[0032] Figure 4 This is a schematic diagram of the structure of the risk rating unit of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] See also Figure 1-4 The present invention provides a technical solution: an intelligent assessment system for the risk of developing digestive tract tumors, including an information collection unit, a medical institution information collection module, an electronic medical record, a physical examination report, a questionnaire survey, an information classification and sorting unit, genetic factors, lifestyle factors, disease factors, environmental factors, psychological factors, a data fusion assessment unit, clinical data, disease factors, weight assignment, a Cox proportional risk model, multifactor analysis, survival analysis, a decision tree, importance ranking, risk stratification, assessment comparison and mutual inspection, a risk rating unit, a risk division module, a measure retrieval module, a result output unit, disease risk level, risk factors, and recommended measures.

[0035] The intelligent assessment system for the risk of digestive tract tumors can collect data through the information collection unit to assess the risk of the patient's condition, specifically:

[0036] The output end of the information acquisition unit is connected to the information classification and collating unit, and also includes a data fusion and evaluation unit. The receiving end of the data fusion and evaluation unit is connected to the transmission segment of the information classification and collating unit. The data fusion and evaluation unit can evaluate the risk of gastrointestinal tumors based on clinical data. The information acquisition unit includes a medical institution information acquisition module. The medical institution information acquisition module collects electronic medical records and physical examination report information. The electronic medical record screening collects digestive tract tumor-related data. The information acquisition unit is also provided with a questionnaire survey. The questionnaire survey collects information by publishing online. The information classification and collating unit integrates and classifies the data collected by the data fusion and evaluation unit. The information classification and collating unit classifies the factors of digestive tract tumors into genetic factors, disease factors, lifestyle factors, environmental factors and psychological factors through the data collected by the medical institution information acquisition module. The data fusion and evaluation unit comprehensively receives data from the information classification and collating unit and the information acquisition unit. The data fusion and evaluation unit collects clinical data through the information acquisition unit, extracts disease factors from the clinical data, and assigns weights to the disease factors in combination with the corresponding proportions of the information classification and collating unit. The weight assignment adopts the corresponding setting of the score.

[0037] The output end of the weight assignment is connected to the Cox proportional risk model and the decision tree. The Cox proportional risk model and the decision tree are set in parallel. The Cox proportional risk model is set with a multi-factor analysis. The multi-factor analysis incorporates multiple risk factors for comprehensive gaps. The survival analysis is set as a predictive analysis based on time factors. The decision tree is set with importance sorting. The importance sorting is sorted according to the weight assignment. The importance sorting is connected with risk stratification. The risk stratification divides the risk level based on the branches and nodes of the decision tree. The data fusion evaluation unit is also provided with an evaluation comparison and mutual inspection. The receiving end of the evaluation comparison and mutual inspection is connected to the output end of the Cox proportional risk model and the decision tree. The evaluation comparison and mutual inspection compares and verifies the evaluation results of the Cox proportional risk model and the decision tree. The output end of the data fusion evaluation unit is connected to the input end of the risk rating unit. The output end of the risk rating unit is connected to the input end of the result output unit. The risk rating unit is provided with a risk division module and a measure retrieval module. The risk division module performs comprehensive risk division on the evaluation structure output by the data fusion evaluation unit. The measure retrieval module retrieves preventive measures corresponding to the risk division module. The result output unit outputs the final evaluation result. The structure includes the risk level of disease, risk factors and recommended measures.

[0038] When the intelligent assessment system for the risk of digestive tract tumors is used, the existing electronic medical records and physical examination reports related to digestive tract tumors are firstly organized uniformly through the medical institution information collection module of the information collection system, and a questionnaire survey is published by establishing an online webpage to collect various information of the patient through the questionnaire survey, including basic demographic information, eating habits, smoking and drinking history, exercise conditions, family medical history, etc. Then, the data of the medical institution information collection module of the information collection system is sent to the information classification and sorting unit for transmission. The information classification and sorting unit extracts the incidence data due to different reasons such as genetic factors, disease factors, lifestyle factors, environmental factors and psychological factors according to the data of the medical institution information collection module. Genetic factors include direct relatives suffering from digestive tract tumors and having a family history of disease. Diseases caused by diet, smoking, drinking, exercise, sleep, etc. belong to lifestyle factors. Digestive tract tumors caused by gastrointestinal diseases, infections or other diseases belong to disease factors. Digestive tract tumors caused by contact with chemical substances and radioactive substances in the working environment belong to environmental factors. Finally, digestive tract tumors caused by long-term mental stress and emotions belong to psychological factors.

[0039] Then the data fusion evaluation unit will extract the pathogenic factors according to the clinical data collected by the medical institution information collection module, and assign weights to the various factors in conjunction with the information classification and sorting unit. Based on the clinical data, it can more accurately reflect the true degree of correlation between each pathogenic factor and the occurrence of gastrointestinal tumors, thereby improving the accuracy of risk assessment. By assigning weights, the qualitative pathogenic factors are converted into quantitative scores, making the risk assessment more accurate and specific, and facilitating the comparison and analysis of the risk levels of different individuals. Since the pathogenic factor combinations of different individuals are different, the weight assignment can generate a unique risk score for each individual, achieving a more personalized assessment, and presenting the assessment results in the form of scores, which more intuitively reflects the individual's risk level, making it easier for patients to understand and accept. The patient information collected from the questionnaire is analyzed using the cox proportional risk model and decision tree according to the weighted scores. The cox proportional risk model performs multifactor analysis and processes the Multiple weighted variables are used to comprehensively evaluate their impact on the risk of gastrointestinal tumors. Survival analysis can not only evaluate the risk of disease, but also predict the patient's survival in the future, providing a reference for treatment and follow-up plans. The importance ranking set by the decision tree automatically screens out the variables that are most important to the risk of disease, helps determine the key influencing factors, and then performs risk stratification. The analysis process and results are displayed in a tree structure, which is more intuitive and easy to understand. The evaluation results of the two methods are then evaluated, compared and checked against each other to produce a more comprehensive and reliable evaluation result. After the evaluation results enter the risk rating unit, the risk classification module will be used to divide the risk level of the results, so that the measure retrieval module can retrieve the corresponding solution, and then the data is transmitted to the result output unit. The result output unit comprehensively organizes the risk level, risk factors and recommended measures, so that patients can more intuitively understand the risk of gastrointestinal tumors and facilitate prevention or treatment.

[0040] Working principle: When using the intelligent assessment system for the risk of gastrointestinal tumors, targeted risk assessments and recommendations can be made based on individual differences and different risk factors, making the assessment results more reliable and accurate, and increasing overall practicality.

[0041] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent assessment system for the risk of developing digestive tract tumors, comprising an information collection unit, wherein the output end of the information collection unit is connected to an information classification and sorting unit, and is characterized in that: It also includes a data fusion evaluation unit, the receiving end of which is connected to the transmission segment of the information classification and sorting unit, and the data fusion evaluation unit can evaluate the risk of gastrointestinal tumors based on clinical data.

2. The intelligent assessment system for the risk of digestive tract tumors according to claim 1, characterized in that: The information collection unit includes a medical institution information collection module, which collects electronic medical records and physical examination report information. The electronic medical record screening collects digestive tract tumor related data. The information collection unit is also provided with a questionnaire survey, which collects information by publishing online.

3. The intelligent assessment system for the risk of digestive tract tumors according to claim 2, characterized in that: The information classification and sorting unit integrates and classifies the data collected by the data fusion and evaluation unit. The information classification and sorting unit classifies the factors of gastrointestinal tumor incidence into genetic factors, disease factors, lifestyle factors, environmental factors and psychological factors through the data collected by the medical institution information collection module.

4. The intelligent assessment system for the risk of digestive tract tumors according to claim 3 is characterized by: The data fusion evaluation unit comprehensively receives data from the information classification and sorting unit and the information collection unit. The data fusion evaluation unit collects clinical data through the information collection unit, extracts pathogenic factors from the clinical data, and assigns weights to the pathogenic factors in combination with corresponding proportions of the information classification and sorting unit.

5. The intelligent assessment system for the risk of digestive tract tumors according to claim 4, characterized in that: The weight assignment is set according to the score.

6. The intelligent assessment system for the risk of digestive tract tumors according to claim 5, characterized in that: The output end assigned with the weight is connected with the Cox proportional hazard model and the decision tree, and the Cox proportional hazard model and the decision tree are arranged in parallel.

7. The intelligent assessment system for the risk of digestive tract tumors according to claim 6, characterized in that: The Cox proportional hazard model is provided with multi-factor analysis and survival analysis, wherein the multi-factor analysis incorporates multiple risk factors for comprehensive gaps, the survival analysis is provided as a predictive analysis based on time factors, the decision tree is provided with importance ranking, the importance ranking is ranked according to weight assignment, the importance ranking is connected with risk stratification, and the risk stratification divides the risk levels based on the branches and nodes of the decision tree.

8. The intelligent assessment system for the risk of digestive tract tumors according to claim 7, characterized in that: The data fusion evaluation unit is also provided with an evaluation comparison and mutual check, the receiving end of which is connected to the output end of the cox proportional risk model and the decision tree, and the evaluation comparison and mutual check compares and verifies the evaluation results of the cox proportional risk model and the decision tree.

9. The intelligent assessment system for the risk of digestive tract tumors according to claim 8, characterized in that: The output end of the data fusion evaluation unit is connected to the input end of the risk rating unit, and the output end of the risk rating unit is connected to the input end of the result output unit.

10. The intelligent assessment system for the risk of digestive tract tumors according to claim 9, characterized in that: The risk rating unit is provided with a risk classification module and a measure retrieval module. The risk classification module performs comprehensive risk classification on the evaluation structure output by the data fusion evaluation unit. The measure retrieval module retrieves preventive measures corresponding to the risk classification module. The result output unit outputs the final evaluation result, which structure includes the disease risk level, risk factors and recommended measures.

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