CN113506630A - Whole all-round intelligent management system of breast cancer postoperative - Google Patents
Whole all-round intelligent management system of breast cancer postoperative Download PDFInfo
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Abstract
The invention relates to the field of medical data processing, in particular to a breast cancer postoperative whole-course all-round intelligent management system. Comprises an acquisition module for acquiring breast cancer related information of a patient; the risk prediction and evaluation module generates a risk evaluation result after the breast cancer operation; the intelligent follow-up visit indication module is used for carrying out hierarchical management on the patients, generating postoperative follow-up visit scheme indication, automatically replying the remote follow-up visit patients, and carrying out acquisition, statistical analysis on symptom signs, examination reports, Chinese medicine symptoms, four diagnostic visits by hearing and comprehensive treatment information of the patients in the lower peripheral diagnosis and treatment period. The invention has the beneficial effects that: the intelligent follow-up visit mode of 'precise treatment + chronic disease management' is provided for various cancers, the treatment stage, the disease progress condition, the diet work and rest, the change of emotional pressure and the like of a patient after a breast cancer operation are quickly obtained, the disease progress and the physical and mental health of the patient are conveniently intelligently tracked and observed in real time, proper medical advice and help are provided, and the follow-up visit efficiency of the patient after the operation is further improved.
Description
Technical Field
The invention relates to the field of medical data processing, in particular to a breast cancer postoperative whole-course all-round intelligent management system.
Background
With the continuous improvement of modern diagnosis and treatment technologies, the precise treatment is widely developed in the field of tumor treatment, the life cycle of part of malignant tumor patients is obviously improved, correspondingly, part of cancers also enter the category of chronic diseases, for example, the relative survival rates of 5 years, 10 years and 15 years of breast cancer patients reach 70-90%, and the incidence rate of breast cancer is continuously increased while the death rate is kept stable. These mean that breast cancer enters "precise treatment + chronic disease management", and also create great challenges for limited medical manpower and material resources.
At present, an intelligent, systematic and effective breast cancer postoperative whole-course all-round intelligent management system is urgently needed, postoperative whole-course management is provided for various 'precise treatment + chronic disease management' patients including breast cancer, so that the disease progress and physical and mental health of the patients can be intelligently tracked and observed in real time, proper medical advice and help are provided, and postoperative follow-up efficiency of the patients is improved.
Disclosure of Invention
To the problem among the prior art, now provide a whole all-round intelligent management system of breast cancer postoperative, include:
the acquisition module is used for acquiring breast cancer related information of a patient;
the first risk prediction and evaluation module is connected with the acquisition module and used for constructing a first postoperative risk evaluation model of the breast cancer, and processing the breast cancer related information of the patient according to the first postoperative risk evaluation model of the breast cancer to generate a first postoperative risk evaluation result of the breast cancer;
the second risk prediction and evaluation module is respectively connected with the acquisition module and a database in which a plurality of postoperative risk division standards of the breast cancer are prestored, and is used for constructing a second postoperative risk evaluation model of the breast cancer according to the postoperative risk division standards of the breast cancer in the database, processing the breast cancer related information of the patient according to the second postoperative risk evaluation model of the breast cancer and generating a second postoperative risk evaluation result of the breast cancer;
the comprehensive evaluation module is respectively connected with the first risk prediction evaluation module and the second risk prediction evaluation module and is used for analyzing the first postoperative risk evaluation result and the second postoperative risk evaluation result of the breast cancer to generate a comprehensive postoperative risk evaluation result of the breast cancer;
and the intelligent follow-up visit indicating module is connected with the acquisition module and the comprehensive evaluation module and is used for carrying out hierarchical management on the patient and generating an individualized postoperative follow-up visit scheme instruction according to the breast cancer related information of the patient and the comprehensive breast cancer postoperative risk evaluation result, remotely monitoring the patient by follow-up visit and automatically replying, and acquiring and statistically analyzing symptoms, signs, examination reports, traditional Chinese medicine symptoms, four diagnostic visits by hearing and comprehensive treatment information of the patient in the lower peripheral diagnosis and treatment period.
Preferably, the first risk prediction and assessment module comprises:
the acquisition unit is connected with an information database which prestores a plurality of disease patients with known disease progression and is used for acquiring personal basic information, perioperative examination report information, postoperative pathological conditions and disease progression condition information of the plurality of disease patients;
a training unit connected with the acquisition unit and used for constructing and training a first breast cancer postoperative risk prediction evaluation model according to the personal basic information of the disease patients, the perioperative examination report information, the postoperative pathological condition and the disease progression condition;
and the evaluation unit is connected with the training unit and used for processing the breast cancer related information of the patient according to the trained postoperative risk prediction evaluation model to generate a corresponding first postoperative risk prediction evaluation result of the breast cancer.
Preferably, the personal basic information comprises age, height, weight, fertility status, menstruation status, tumor family history, complications and preoperative examination report results;
the postoperative pathological conditions comprise tumor stage, molecular typing, gene detection result, diagnosis date, tumor type, tumor grade, tumor size, regional lymph node metastasis condition, distant metastasis condition, hormone receptor condition and HER2 gene expression condition;
the disease progression is the postoperative risk profile of breast cancer in the patient with the disorder.
Preferably, the acquiring unit includes:
the extraction component adopts a key phrase extraction algorithm to extract corresponding related factors of the first breast cancer risk prediction model from the personal basic information, the perioperative period inspection report information and the postoperative pathological condition;
the training unit is used for constructing and training the first breast cancer postoperative risk prediction evaluation model according to the personal basic information of the disease patients, the perioperative examination report information, the postoperative pathological condition, the first breast cancer risk prediction model related factors and the disease progression condition.
Preferably, the first and second post-breast cancer risk assessment results include high, medium and low risk conditions;
the comprehensive evaluation module takes the high risk as the comprehensive breast cancer postoperative risk evaluation result when the first breast cancer postoperative risk evaluation result or the second breast cancer postoperative risk evaluation result is the high risk.
Preferably, the intelligent follow-up indication module includes:
an inline unit, the inline unit comprising:
a follow-up monitoring indication unit, which is used for carrying out hierarchical management on the patient and generating the postoperative follow-up scheme indication with different follow-up monitoring frequencies and suggested contents according to the breast cancer related information of the patient, including the current treatment state, treatment medication condition, blood fat and other related monitoring results and the comprehensive postoperative risk assessment result of the breast cancer; and/or
A life indication unit, configured to generate an individualized life indication of the patient according to the breast cancer related information of the patient, including height, weight, exercise, diet work and the like, and the comprehensive breast cancer postoperative risk assessment result; and/or
A mood indication unit for generating a mood indication of the patient according to the breast cancer related information of the patient including stress, sleep and the like and the comprehensive breast cancer postoperative risk assessment result; and/or
And the requirement indicating unit is used for generating a requirement indication of the patient according to the breast cancer related information of the patient, including birth requirements, treatment related side effects, traditional Chinese and western medicine combined comprehensive treatment and the like, and the risk assessment result and the actual requirements after the comprehensive breast cancer operation.
The breast cancer automatic reply unit is used for generating the breast cancer automatic reply of the patient according to the breast cancer related information of the patient, the comprehensive breast cancer postoperative risk assessment result, the conditions of different treatment stages and related problems in the process of Chinese and Western medicine combined treatment;
an offline unit comprising:
and the breast cancer perioperative period unit is used for collecting and statistically analyzing symptoms, physical signs, examination reports, traditional Chinese medicine symptoms, four diagnostic methods by hearing and inquiry and comprehensive treatment information of the patient.
Preferably, the postoperative follow-up protocol instructions comprise online hierarchical management of patients with different risks and automatic response of different follow-up frequency instructions, follow-up item instructions, daily work and rest suggestions, real-time breast cancer treatment stages, disease progression conditions and follow-up related questions;
the real-time breast cancer treatment stage comprises all completed auxiliary treatment stages, new auxiliary treatment stages, perioperative stages, chemotherapy stages, radiotherapy stages, targeted treatment stages, endocrine treatment stages and disease progression and rescue treatment stages.
Preferably, the emotion indicating unit includes:
an emotion indication presetting unit for presetting a plurality of emotion adjusting fields;
an emotion indication acquisition component for acquiring the user information of the current user and the postoperative risk assessment result;
and the emotion indication generating component is connected with the emotion indication presetting component and the emotion indication acquiring component and is used for selecting corresponding emotion adjusting fields from the emotion adjusting fields according to the user information and the postoperative risk assessment result, combining and adjusting the emotion adjusting fields and outputting the processed emotion adjusting fields as the emotion indications.
Preferably, the breast cancer automatic response module comprises the following fields;
a basic communication corpus field for performing basic communication;
pathological information fields including tumor pathological type, grade, size, stage, typing, immunohistochemical index and recovery fields of indications;
perioperative period-related fields including upper limb edema, rehabilitation, wound healing, stitches removing, drainage, and recovery fields of dietary contraindications;
chemotherapy and neoadjuvant chemotherapy fields, including drugs, treatment sessions, possible side effects and treatments, and recovery fields for assessment, related diet and lifestyle notes;
endocrine treatment fields including auxiliary endocrine, late endocrine, castration-related reply fields;
target notice fields including normative treatment course, dosage adjustment, medical insurance and diet contraindication reply fields;
a menstruation/menopause/fertility treatment field, including menopause determination, menstruation and endometrium response fields;
late stage-related therapy fields, including recovery fields for bone protection, immunotherapy, rescue therapy assessment;
radiotherapy fields, including indications, cautions, side effects and responses to treatment fields;
the Chinese medicine treatment field comprises a recovery field of the related matters of decoction mode, time, taking cautions, first decoction and then administration and clinic treatment;
the post-operation follow-up field comprises a follow-up flow, frequency, follow-up content, various inspection frequencies, abnormal indexes and a reply field for processing;
work and rest fields including hair dyeing, vaccine, tourism, exercise, sleep, mood, weight, and cosmetic return fields;
solid diet-related fields including food, health care products, snacks, return fields for daily nutritional needs;
non-solid diet related fields, including return fields of tobacco and wine, meal replacement, beverages, functional drinks and tea making;
disease-associated fields, including hyperlipidemia or dyslipidemia, osteoporosis, cardiovascular disease, mood, and endometrial thickening recovery fields.
Preferably, the breast cancer underline diagnosis and treatment unit comprises:
an importing unit for importing breast cancer related information of the patient;
an acquisition unit for acquiring the real-time breast cancer treatment stage;
a traditional Chinese medicine symptom collecting unit for collecting and combing the symptoms of the patient in the offline perioperative period;
the system comprises a consultation unit, a diagnosis unit and a control unit, wherein the consultation unit is used for acquiring the change condition of corresponding symptoms of a breast cancer outpatient during consultation;
the real-time acquisition unit is used for acquiring an examination report of a perioperative breast cancer postoperative patient in a periodic follow-up visit in a hospital in real time;
a general treatment information unit for recording the general treatment information of the patient, wherein the general treatment information comprises the prescription of the patient at the visit;
the acquisition unit is used for acquiring information of the four diagnostic methods of the inquiry and the resection;
the searching unit is used for searching so as to accurately position the tumor stage, the positioning molecule type, the treatment state and the symptom of the breast cancer;
a multi-platform deriving unit for deriving the breast cancer-related information, the treatment stage, the combing result of the patient's symptoms in the offline perioperative period, the change, the examination report of the periodic visit of the hospital, the comprehensive treatment information, the information on the four diagnostic findings by the overseas and olfactive study, the tumor stage, the molecular typing, the treatment status, and the symptoms.
The technical scheme of the invention has the beneficial effects that: the breast cancer postoperative whole-course all-round management system can provide an intelligent follow-up mode of 'accurate treatment + chronic disease management' for various cancers, quickly acquire the treatment stage, the disease progress condition, the diet work and rest, the change of emotional pressure and the like of a patient after a breast cancer operation, is convenient for intelligently tracking and observing the disease progress and the physical and mental health of the patient in real time, provides proper medical advice and help, and further improves the follow-up efficiency of the patient after the operation.
Drawings
FIG. 1 is a schematic structural diagram of an all-round intelligent management system for whole course after breast cancer operation in accordance with a preferred embodiment of the present invention;
FIG. 2 is a schematic diagram of a first risk prediction and assessment module according to a preferred embodiment of the present invention;
FIG. 3 is a schematic diagram of the AUC characteristic curve of the first risk prediction and assessment model constructed according to the preferred embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating the weights of different parameters in the first risk prediction and assessment model according to a preferred embodiment of the present invention;
FIG. 5 is a schematic structural diagram of an intelligent follow-up indication module according to a preferred embodiment of the present invention;
fig. 6 is a schematic structural diagram of an emotion indicating unit in a preferred embodiment of the present invention.
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.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.
The invention provides a breast cancer postoperative whole course all-round management system, as shown in fig. 1, comprising:
the acquisition module 1 is used for acquiring the user information of the current user;
the first risk prediction and evaluation module 2 is connected with the acquisition module 1 and used for constructing a first breast cancer postoperative risk evaluation model, processing breast cancer related information of a patient according to the first breast cancer postoperative risk evaluation model and generating a first breast cancer postoperative risk evaluation result;
the second risk prediction and evaluation module 3 is respectively connected with the acquisition module 1 and a database in which a plurality of postoperative risk division standards of the breast cancer are prestored, and is used for constructing a second postoperative risk evaluation model of the breast cancer according to the postoperative risk division standards of the breast cancer in the database, processing breast cancer related information of the patient according to the second postoperative risk evaluation model of the breast cancer and generating a second postoperative risk evaluation result of the breast cancer;
the comprehensive evaluation module 4 is respectively connected with the first risk prediction evaluation module 2 and the second risk prediction evaluation module 3 and is used for analyzing the first breast cancer postoperative risk evaluation result and the second breast cancer postoperative risk evaluation result to generate a comprehensive breast cancer postoperative risk evaluation result;
an intelligent follow-up visit indicating module 5, which is connected with the acquisition module 1 and the comprehensive evaluation module 4, is used for carrying out hierarchical management on the patient according to the breast cancer related information of the patient and the comprehensive breast cancer postoperative risk evaluation result, generating an individualized postoperative follow-up visit scheme indication, remotely follow-up visiting the monitored patient and carrying out automatic reply, and collecting and statistically analyzing the symptoms, signs, examination reports, traditional Chinese medicine symptoms, four diagnostic visits and comprehensive treatment information of the patient in the lower peripheral diagnosis and treatment period.
Specifically, the invention provides an overall postoperative management system for malignant breast tumors, and provides overall postoperative service management for accurate treatment and chronic disease management of cancer patients, for example, the invention provides an overall postoperative management system for breast cancer patients, so that the overall postoperative management system can perform overall process management for the whole process from disease onset, treatment to rehabilitation of breast cancer patients, can provide inquiry guidance and life instructions for a long postoperative follow-up period, not only improves the survival quality of the breast cancer patients in the postoperative survival time, but also is beneficial to prolonging postoperative recovery, prolonging survival time and the like.
It should be noted that the postoperative indication relates to an indication of the whole postoperative course, so that the current user information can be updated and collected in real time according to the collection module 1, and the real-time accurate management of the whole postoperative course can be realized.
In a preferred embodiment of the present invention, as shown in fig. 2, the first risk prediction and evaluation module 2 includes:
an obtaining unit 21, connected to an information database pre-storing a plurality of disease patients with known disease progression, for obtaining personal basic information, perioperative examination report information, postoperative pathological condition and disease progression condition information of a plurality of disease;
the training unit 22 is connected with the acquisition unit 21 and is used for constructing and training a first breast cancer postoperative risk prediction and evaluation model according to the personal basic information of the disease patient, the perioperative examination report information, the postoperative pathological condition and the disease progression condition;
and the evaluation unit 23 is connected with the training unit 22 and is used for processing the breast cancer related information of the patient according to the trained postoperative risk prediction evaluation model to generate a corresponding first breast cancer postoperative risk prediction evaluation result.
Specifically, the acquisition unit 21 acquires individual basic information of a plurality of disease patients whose disease progression is known, including age, height, weight, fertility status, menstruation status, tumor family history, complications, and preoperative examination report results, perioperative examination report information, postoperative pathology, and disease progression status information; the postoperative pathological conditions comprise tumor stage, molecular typing, gene detection result, diagnosis date, tumor type, tumor grade, tumor size, regional lymph node metastasis condition, distant metastasis condition, hormone receptor condition and HER2 gene expression condition; the disease progression is the postoperative risk of breast cancer in the patient with the disorder; the training unit 22 is used for constructing and training a first postoperative risk assessment model of the breast cancer based on the personal basic information, the perioperative examination report information, the postoperative pathological condition and the disease progression condition of the patient with the disease, so that the known personal basic information, the perioperative examination report information and the postoperative pathological condition of the patient with the disease can be input into the trained postoperative risk assessment model of the breast cancer, and the trained postoperative risk assessment model can input the disease progression condition information of the patient with the disease, namely the postoperative risk assessment result of the first breast cancer, so that the postoperative risk assessment model of the breast cancer can be trained to accurately assess the postoperative recurrence risk of the breast cancer; the evaluation unit 23 processes the breast cancer related information of the patient by using the trained first breast cancer postoperative risk evaluation model, and generates a corresponding first breast cancer postoperative risk prediction evaluation result.
It should be noted that, in order to implement the construction and training of the first postoperative risk assessment model for breast cancer, the patient with the same disease as the current user is in, but the patient with the disease and the current user are in different stages, for example, the patient with the disease may be a patient who has completed all the auxiliary treatment/neoauxiliary treatment/perioperative/chemotherapy/radiotherapy/targeted treatment/endocrine treatment/disease progression rescue treatment/other stages, the recorded patient, and the current user may be a patient before the operation, that is, the current user is in a stage that lags behind the stage of the patient with the disease for training the postoperative risk assessment model.
It should be noted that the information database pre-stored with a plurality of disease patients may include a medical record database, and may also include a scientific research literature database and a database of a plurality of disease examination reports.
In a preferred embodiment of the present invention, the acquiring unit 21 includes:
the extraction component adopts a key phrase extraction algorithm to extract corresponding related factors of the first breast cancer risk prediction model from the personal basic information, the perioperative period inspection report information and the postoperative pathological condition;
and the training unit 22 is used for constructing and training a first breast cancer postoperative risk prediction evaluation model according to the personal basic information of the disease patient, the perioperative examination report information, the postoperative pathological condition, the relevant factors of the first breast cancer risk prediction model and the disease progression condition.
Specifically, for different diseases, the relevant factors affecting the postoperative risk assessment result of the breast cancer are different, so that in the process of constructing and training the postoperative risk assessment model, the relevant factors of the corresponding first breast cancer risk prediction model can be extracted through the extraction component according to the personal basic information, the perioperative examination report information and the postoperative pathological condition.
In a preferred embodiment of the present invention, the extracting component may extract the personal basic information, the perioperative period examination report information and the postoperative pathological condition from the documents such as the case and the examination report by using a key phrase extraction method such as field division, context semantic analysis, etc., and when the patient with the disease condition is a breast cancer patient, the preset personal basic information of the breast cancer patient includes age, height, weight, fertility status, menstruation status, tumor family history, complications and preoperative examination report result; postoperative pathological conditions include diagnosis date, tumor type, tumor grade, tumor size, regional lymph node metastasis, distant metastasis, hormone receptor, HER2 gene expression, and disease progression. That is, when the postoperative risk management system is applied to postoperative total-flow management of a breast cancer patient, the process of acquiring personal basic information, perioperative examination report information and postoperative pathological conditions may be as follows: acquiring personal basic information and perioperative period inspection report information of a breast cancer patient with known postoperative condition, and extracting corresponding description from a medical record according to preset age, tumor type, tumor grade, tumor size, regional lymph node metastasis condition, distant metastasis condition, hormone receptor condition and HER2 gene expression condition, for example, extracting preoperative condition description ' 32 years ' corresponding to age from the medical record aiming at the age, extracting description ' axillary lymph node has tumor metastasis 8/13 ' corresponding to lymph node from the preoperative medical record aiming at the regional lymph node metastasis condition, extracting description ' invasive cancer maximum diameter is 3cm ' corresponding to the tumor size from the preoperative medical record aiming at the tumor size, and extracting description ' ER (1% +) and ER (1% +) medical record) corresponding to estrogen receptor and progestogen receptor from the medical record aiming at the hormone receptor condition, "PR (1% +)", a description "HER-2 (-)" corresponding to the expression of the HER2 gene can be extracted from the medical record for the expression of the HER2 gene, and a description "grade III" corresponding to the grading of the progression of the disease can be extracted from the medical record for the grading of the progression of the disease.
In a preferred embodiment of the present invention, as shown in fig. 3 and 4, the training unit 22 constructs and trains a first breast cancer postoperative risk prediction evaluation model according to the individual basic information of the patient with the disease, the perioperative examination report information, the postoperative pathological condition, the factors related to the first breast cancer risk prediction model, and the disease progression. Specifically, the algorithm steps of the training unit 22 for constructing and training the first breast cancer postoperative risk prediction and evaluation model mainly include:
step 1, reading in data, namely reading in historical inspection data, diagnosis data, operation data and follow-up records of patients in a hospital;
step 6, modeling based on the characteristics of an evaluation function, and performing modeling of a first breast cancer postoperative risk prediction evaluation model by using an Xgboost algorithm;
step 7, deployment, namely deploying the first breast cancer postoperative risk prediction evaluation model in the current network environment, and predicting the postoperative breast cancer risk prediction evaluation model under the condition that the postoperative breast cancer risk prediction evaluation model and the test characteristics are not lost according to the input and triggering conditions of external data in real time;
and 8, monitoring the model, namely continuously monitoring the result of the model for the postoperative risk prediction and evaluation result of the first postoperative breast cancer risk prediction and evaluation model and follow-up visit records in subsequent patient hospitals after the first postoperative breast cancer risk prediction and evaluation model is online so as to judge whether the correctness of the model is stable or not, and mainly monitoring the change range between the value of AUC and the value in the original modeling.
In step 6, the Xgboost algorithm is used for modeling, considering that Xgboost is a lifting tree model, so that it integrates many tree models together to form a strong classifier. The tree model used is a CART regression tree model, and Xgboost has the following advantages: 1. regularization: the learned model can be simpler, and overfitting is prevented; 2. the Xgboost can be processed in parallel: the most time-consuming step for learning the decision tree is to sequence the values of the features, because the step needs to determine the optimal segmentation point, and the Xgboost algorithm is used for modeling, data is sequenced in advance before training, then the data is stored as a block structure, the structure is repeatedly used in the following iteration, the calculation amount is greatly reduced, when the nodes are split, the gain of each feature needs to be calculated, and finally the feature with the maximum gain is selected to be split, so that the gain calculation of each feature can be performed in a multithreading way; 3. pruning: xgboost establishes all subtrees capable of being established from top to bottom, and then prunes by a reverse movement from bottom to top, so that compared with GBM, the Xgboost is not easy to fall into a local optimal solution; 4. built-in cross validation: xgboost allows cross-validation to be used in each round of Boosting iteration. Therefore, the optimal Boosting iteration number can be conveniently obtained, and the GBM only can detect a limited value by using grid search.
Accordingly, based on the above steps, the effect of the first postoperative risk prediction and evaluation model for breast cancer finally constructed by the present invention can be shown in the following table:
TABLE 1 Effect of the first postoperative Risk prediction evaluation model for Breast cancer
Accordingly, when the Xgboost algorithm is used for constructing the model, the ROCAC output is 89.27%. And the above table shows that the model constructed by the invention is accurate enough.
Furthermore, in the practical application of the invention, the weights of different parameters can be adjusted by combining the actual diagnosis experience of doctors, so that the model finally constructed and trained is more accurate. For example, the invention obtains in advance the data of individual basic information, perioperative examination report information, and postoperative pathological condition, the last screening parameters are 6 parameters, such as "T stage", "ER status", "blood _ carbohydrate antigen 15-3_ quantitative", "blood _ mean platelet volume _ qualitative", "blood _ red blood cell count _ quantitative", "blood _ activated partial thromboplastin time _ qualitative", and "blood _ activated partial thromboplastin time _ quantitative", and the weights set accordingly are: the weight of "T stage" was set to 1.60, the weight of "ER state" was set to 1.24, the weight of "sanguine carbohydrate antigen 15-3_ quantitative" was set to 0.10, the weight of "average platelet volume _ qualitative" was set to 0.95, the weight of "blood _ red blood cell count _ quantitative" was set to 0.24, the weight of "blood _ activated partial thromboplastin time _ qualitative" was set to a weight ratio of 0.66, and the weight of "blood _ activated partial thromboplastin time _ quantitative" was set to 0.37.
Correspondingly, the second breast cancer postoperative risk assessment module constructs a second breast cancer postoperative risk assessment model according to the breast cancer postoperative risk division standard in the database, wherein the breast cancer postoperative risk division standard selected in the invention can be:
when the second postoperative risk assessment result of breast cancer is low risk, it is necessary to satisfy the conditions that metastatic lymph node is negative, the size of the focus in the specimen is pT <2cm, the focus is classified as I grade, the tumor invasion of peritumoral vessels is not seen, ER and/or PR is deleted, HER2/meu gene is amplified after not over-expressing, and the age is more than or equal to 35 years;
when the second postoperative risk assessment result of breast cancer is intermediate risk, the requirement that metastatic lymph nodes are negative and the requirement that the size of the focus in the specimen is pT & gt 2cm, the focus is classified as II grade or III grade, the tumor invasion of peritumoral vessels, ER and/or PR deletion, HER2 gene overexpression or amplification and the age is & lt 35 years is met; or the transferred lymph node is 1 to 4 positive, and the HER2 gene is not over expressed and amplified, and ER or PR expression is not seen;
when the risk assessment result after the second breast cancer operation is high risk, 1 to 4 positive metastatic lymph nodes are required to be satisfied, and the HER2 gene is over-expressed and amplified, and ER or PR expression is not seen; or the metastatic lymph nodes are more than or equal to 4 positive.
It should be noted that other postoperative risk classification standards of breast cancer can be adopted, and the postoperative risk classification standards of breast cancer can be customized according to the diagnosis experience of doctors and stored in the database in advance.
Based on the above, a second breast cancer postoperative risk assessment model is constructed according to the selected breast cancer postoperative risk division standard, and the second breast cancer postoperative risk assessment model is used for processing breast cancer related information of the patient to generate a second breast cancer postoperative risk assessment result.
In a preferred embodiment of the present invention, the first and second post-breast cancer risk assessment results include high, medium and low risk conditions;
and the comprehensive evaluation module is used for taking the high risk as the post-comprehensive breast cancer risk evaluation result when the first post-breast cancer risk evaluation result or the second post-breast cancer risk evaluation result is high risk.
That is, if the trained first post-breast cancer risk assessment model is constructed and processed to obtain a high-risk assessment result, or the second post-breast cancer risk assessment model constructed according to the known/customized partition criteria is processed to obtain a high-risk assessment result, the patient is considered to be a high-risk patient after the surgery, and a more appropriate follow-up plan and follow-up instructions are provided for the patient.
In a preferred embodiment of the present invention, the intelligent follow-up instruction module 5 may include a follow-up monitoring instruction 51, which is used for performing hierarchical management on the patient and generating an individualized post-operation follow-up plan instruction according to the breast cancer related information and the comprehensive breast cancer post-operation risk assessment result of the patient, performing remote follow-up monitoring on the patient and performing automatic reply, and collecting and statistically analyzing symptoms, signs, examination reports, chinese medical symptoms, four diagnostic visits and comprehensive treatment information of the patient in the sub-line peripheral diagnosis and treatment period.
In a preferred embodiment of the present invention, the follow-up instructions in the remote follow-up monitoring are used for performing hierarchical management on the user and generating different follow-up monitoring frequencies and recommended follow-up item instructions according to the user information of the current user, including the current treatment state, the treatment medication condition, the blood fat and other related monitoring results and the postoperative risk assessment result.
Specifically, the follow-up monitoring instruction 51 is used for performing hierarchical management on the patient and generating a post-operation follow-up plan instruction with different follow-up monitoring frequencies and recommended contents according to the breast cancer related information of the patient including the current treatment state, the treatment medication condition, the blood fat and other related monitoring results and the comprehensive breast cancer post-operation risk assessment result, for example, the post-operation follow-up plan instruction corresponding to a user whose post-operation risk assessment is high risk is to perform follow-up every three months, and the post-operation follow-up plan instruction specifically includes: general tests such as breast and lymph node palpation; blood routine, liver and kidney function, tumor index and other blood biochemical detection; breast, axilla, thyroid, supraclavicular lymph nodes, hepatobiliary hypercrypsin, adnexal uterine ultrasound, and breast CT, molybdenum target, and bone density once a year. For example, for a user with a high risk post-operative risk assessment and bone density T < ═ 2.5, corresponding follow-up instructions are generated, such as moderate exercise over 30 minutes daily, eating calcium-enriched food, quitting smoking and alcohol; supplementing calcium and vitamin D; simultaneous bisphosphonate treatment with supplementation of an appropriate calcium agent and vitamin D; drugs with less impact on bone safety are selected to reduce the occurrence of bone safety problems and to increase the frequency of BMD monitoring, such as detection prompts every 6-12 months.
In a preferred embodiment of the present invention, as shown in fig. 5, the intelligent follow-up indication module 5 may comprise a life indication unit 52 for generating a life indication of an individualized patient according to the breast cancer related information of the patient, including height, weight, exercise, diet work and the like, and the postoperative risk assessment result.
Specifically, for example, for a user with a high risk post-operative risk assessment of 150cm height, 60kg weight, and 82cm waist circumference, a corresponding lifestyle prescription is generated with a BMI of 26.7 and a waist circumference of 82cm for obesity, suggesting avoidance of high calorie food, beverages, and moderate increase in physical activity.
In a preferred embodiment of the present invention, as shown in fig. 5, the indication module 3 may comprise an emotion indication unit 53 for generating an emotion indication of the patient according to the breast cancer related information of the patient including stress, sleep, etc. and the comprehensive breast cancer postoperative risk assessment result.
In a preferred embodiment of the present invention, as shown in fig. 6, the emotion indicating unit 53 includes:
an emotion instruction presetting section 531 for presetting a plurality of emotion adjusting fields;
an emotion indication acquisition part 532 for acquiring user information of the current user and a postoperative risk assessment result;
and an emotion instruction generating component 533 connected to the emotion instruction presetting component 531 and the emotion instruction obtaining component 532, configured to select, according to the user information and the postoperative risk assessment result, a corresponding emotion adjusting field from the multiple emotion adjusting fields, perform combination and adjustment processing, and output the processed emotion adjusting field as an emotion instruction.
Specifically, in order to keep good mood of chronic tumor diseases, guarantee quality of life after operation, improve recovery conditions after operation and delay survival time after operation, the invention provides an emotion indication unit 53 which generates corresponding emotion indication by combining and adjusting different emotion adjustment fields so as to adjust emotion of a user. For example, when the emotional indication generating component 533 analyzes mild disorders in which the user has cognitive functions, the generated emotional indication includes prompting the user to take an event, prompting the user to exercise memory, prompting the user to relieve stress, releasing stress from meditation, exercising, prompting the user to reduce alcohol, caffeine intake.
In a preferred embodiment of the present invention, as shown in fig. 5, the intelligent follow-up indication module 5 may include a requirement indication unit 54, configured to generate a requirement indication of the current user according to the user information of the current user, including fertility requirements, treatment-related side reactions, combined traditional Chinese and western medicine treatment, and the like, and the risk assessment result and actual requirements after the comprehensive breast cancer operation. In particular, for the actual needs of different users, corresponding need indications may be generated.
In a preferred embodiment of the present invention, the system may further include an automatic breast cancer recovery unit 55, configured to generate an automatic breast cancer recovery for the patient according to the breast cancer related information of the patient, the post-operation risk assessment result of the breast cancer, the conditions of different treatment stages, and the related problems in the combined traditional Chinese and western medicine treatment process.
In a preferred embodiment of the present invention, as shown in fig. 5, the intelligent follow-up indication module 5 may include a breast cancer perioperative period unit 56 for collecting and statistically analyzing the symptoms, signs, examination reports, Chinese medicine symptoms, four diagnostic visits by people and comprehensive treatment information of the patient.
Specifically, considering the inquiry mode of the Chinese medical diagnosis and treatment by the inspection and the inquiry of the Chinese medical science and the traditional Chinese medicine conditioning, and combining the western medical diagnosis and treatment, the breast cancer perioperative diagnosis and treatment period unit 56 is provided, which can change the internal environment of tumor growth, improve the immune function of patients, prevent tumor recurrence and metastasis, provide more comprehensive postoperative diagnosis and treatment for users, and improve the outpatient efficiency.
In a preferred embodiment of the invention, the postoperative follow-up protocol indication comprises online hierarchical management of patients with different risks and automatic response of different follow-up frequency indications, follow-up item indications, daily work and rest suggestions, real-time breast cancer treatment stages, disease progression conditions and follow-up related questions;
the real-time breast cancer treatment stage comprises all completed auxiliary treatment stages, new auxiliary treatment stages, perioperative stages, chemotherapy stages, radiotherapy stages, targeted treatment stages, endocrine treatment stages and disease progression and rescue treatment stages.
In a preferred embodiment of the present invention, the adenocarcinoma automatic reply module comprises the following fields; a basic communication corpus field for performing basic communication;
pathological information fields including tumor pathological type, grade, size, stage, typing, immunohistochemical index and recovery fields of indications;
perioperative period-related fields including upper limb edema, rehabilitation, wound healing, stitches removing, drainage, and recovery fields of dietary contraindications;
chemotherapy and neoadjuvant chemotherapy fields, including drugs, treatment sessions, possible side effects and treatments, and recovery fields for assessment, related diet and lifestyle notes;
endocrine treatment fields including auxiliary endocrine, late endocrine, castration-related reply fields;
target notice fields including normative treatment course, dosage adjustment, medical insurance and diet contraindication reply fields;
a menstruation/menopause/fertility treatment field, including menopause determination, menstruation and endometrium response fields;
late stage-related therapy fields, including recovery fields for bone protection, immunotherapy, rescue therapy assessment;
radiotherapy fields, including indications, cautions, side effects and responses to treatment fields;
the Chinese medicine treatment field comprises a recovery field of the related matters of decoction mode, time, taking cautions, first decoction and then administration and clinic treatment;
the post-operation follow-up field comprises a follow-up flow, frequency, follow-up content, various inspection frequencies, abnormal indexes and a reply field for processing;
work and rest fields including hair dyeing, vaccine, tourism, exercise, sleep, mood, weight, and cosmetic return fields;
solid diet-related fields including food, health care products, snacks, return fields for daily nutritional needs;
non-solid diet related fields, including return fields of tobacco and wine, meal replacement, beverages, functional drinks and tea making;
disease-associated fields, including hyperlipidemia or dyslipidemia, osteoporosis, cardiovascular disease, mood, and endometrial thickening recovery fields.
In a preferred embodiment of the present invention, the breast cancer offline circumference diagnosis and treatment unit includes:
an importing unit for importing breast cancer related information of a patient;
an acquisition unit for acquiring real-time breast cancer treatment stages;
the traditional Chinese medicine symptom acquisition unit is used for acquiring symptoms of patients in a lower-line perioperative period and combing the symptoms;
the system comprises a consultation unit, a diagnosis unit and a control unit, wherein the consultation unit is used for acquiring the change condition of corresponding symptoms of a breast cancer outpatient during consultation;
the real-time acquisition unit is used for acquiring an examination report of a perioperative breast cancer postoperative patient in a periodic follow-up visit in a hospital in real time;
the comprehensive treatment information unit is used for recording the comprehensive treatment information of the patient, and the comprehensive treatment information comprises the prescription of the patient for the visit;
the acquisition unit is used for acquiring information of the four diagnostic methods of the inquiry and the resection;
the searching unit is used for searching so as to accurately position the tumor stage, the positioning molecule type, the treatment state and the symptom of the breast cancer;
the multi-platform deriving unit is used for deriving breast cancer related information, treatment stages, combing results and change conditions of symptoms of patients in offline peripheral diagnosis and treatment periods, examination reports of periodic visits of an outer hospital, comprehensive treatment information, information of four diagnostic methods of inquiring and hearing, tumor staging, molecular typing, treatment states and symptoms.
The technical scheme provided by the invention has the beneficial effects that the breast cancer postoperative whole-course all-round management system is provided, an intelligent follow-up mode of 'accurate treatment + chronic disease management' can be provided for various cancers, the treatment stage, the disease progress condition, the diet work and rest, the change of emotional pressure and the like of a patient after the breast cancer operation can be rapidly obtained, the disease progress and the physical and mental health of the patient can be intelligently tracked and observed in real time, and appropriate medical advice and help can be provided, so that the postoperative follow-up efficiency of the patient can be improved.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.
Claims (10)
1. The utility model provides a whole all-round intelligent management system of breast cancer postoperative which characterized in that includes:
the acquisition module is used for acquiring breast cancer related information of a patient;
the first risk prediction and evaluation module is connected with the acquisition module and used for constructing a first postoperative risk evaluation model of the breast cancer, and processing the breast cancer related information of the patient according to the first postoperative risk evaluation model of the breast cancer to generate a first postoperative risk evaluation result of the breast cancer;
the second risk prediction and evaluation module is respectively connected with the acquisition module and a database in which a plurality of postoperative risk division standards of the breast cancer are prestored, and is used for constructing a second postoperative risk evaluation model of the breast cancer according to the postoperative risk division standards of the breast cancer in the database, processing the breast cancer related information of the patient according to the second postoperative risk evaluation model of the breast cancer and generating a second postoperative risk evaluation result of the breast cancer;
the comprehensive evaluation module is respectively connected with the first risk prediction evaluation module and the second risk prediction evaluation module and is used for analyzing the first postoperative risk evaluation result and the second postoperative risk evaluation result of the breast cancer to generate a comprehensive postoperative risk evaluation result of the breast cancer;
and the intelligent follow-up visit indicating module is connected with the acquisition module and the comprehensive evaluation module and is used for carrying out hierarchical management on the patient and generating an individualized postoperative follow-up visit scheme instruction according to the breast cancer related information of the patient and the comprehensive breast cancer postoperative risk evaluation result, remotely monitoring the patient by follow-up visit and automatically replying, and acquiring and statistically analyzing symptoms, signs, examination reports, traditional Chinese medicine symptoms, four diagnostic visits by hearing and comprehensive treatment information of the patient in the lower peripheral diagnosis and treatment period.
2. The system for full-scale intelligent post-breast cancer management according to claim 1, wherein the first risk prediction and assessment module comprises:
the acquisition unit is connected with an information database which prestores a plurality of disease patients with known disease progression and is used for acquiring personal basic information, perioperative examination report information, postoperative pathological conditions and disease progression condition information of the plurality of disease patients;
a training unit connected with the acquisition unit and used for constructing and training a first breast cancer postoperative risk prediction evaluation model according to the personal basic information of the disease patients, the perioperative examination report information, the postoperative pathological condition and the disease progression condition;
and the evaluation unit is connected with the training unit and used for processing the breast cancer related information of the patient according to the trained postoperative risk prediction evaluation model to generate a corresponding first postoperative risk prediction evaluation result of the breast cancer.
3. The breast cancer postoperative global intelligent management system according to claim 2, wherein the personal basic information includes age, height, weight, fertility status, menstruation status, tumor family history, complications and preoperative examination report results;
the postoperative pathological conditions comprise tumor stage, molecular typing, gene detection result, diagnosis date, tumor type, tumor grade, tumor size, regional lymph node metastasis condition, distant metastasis condition, hormone receptor condition and HER2 gene expression condition;
the disease progression is the postoperative risk profile of breast cancer in the patient with the disorder.
4. The breast cancer postoperative global intelligent management system according to claim 2, wherein the acquisition unit comprises:
the extraction component adopts a key phrase extraction algorithm to extract corresponding related factors of the first breast cancer risk prediction model from the personal basic information, the perioperative period inspection report information and the postoperative pathological condition;
the training unit is used for constructing and training the first breast cancer postoperative risk prediction evaluation model according to the personal basic information of the disease patients, the perioperative examination report information, the postoperative pathological condition, the first breast cancer risk prediction model related factors and the disease progression condition.
5. The breast cancer postoperative global intelligent management system according to claim 1,
the first and second post-breast cancer risk assessment results include high, medium, and low risk conditions;
the comprehensive evaluation module takes the high risk as the comprehensive breast cancer postoperative risk evaluation result when the first breast cancer postoperative risk evaluation result or the second breast cancer postoperative risk evaluation result is the high risk.
6. The post-operative global management system according to claim 1, wherein the intelligent follow-up indication module comprises:
an inline unit, the inline unit comprising:
a follow-up monitoring indication unit, which is used for carrying out hierarchical management on the patient and generating the postoperative follow-up scheme indication with different follow-up monitoring frequencies and suggested contents according to the breast cancer related information of the patient, including the current treatment state, treatment medication condition, blood fat and other related monitoring results and the comprehensive postoperative risk assessment result of the breast cancer; and/or
A life indication unit, configured to generate an individualized life indication of the patient according to the breast cancer related information of the patient, including height, weight, exercise, diet work and the like, and the comprehensive breast cancer postoperative risk assessment result; and/or
A mood indication unit for generating a mood indication of the patient according to the breast cancer related information of the patient including stress, sleep and the like and the comprehensive breast cancer postoperative risk assessment result; and/or
And the requirement indicating unit is used for generating a requirement indication of the patient according to the breast cancer related information of the patient, including birth requirements, treatment related side effects, traditional Chinese and western medicine combined comprehensive treatment and the like, and the risk assessment result and the actual requirements after the comprehensive breast cancer operation.
The breast cancer automatic reply unit is used for generating the breast cancer automatic reply of the patient according to the breast cancer related information of the patient, the comprehensive breast cancer postoperative risk assessment result, the conditions of different treatment stages and related problems in the process of Chinese and Western medicine combined treatment;
an offline unit comprising:
and the breast cancer perioperative period unit is used for collecting and statistically analyzing symptoms, physical signs, examination reports, traditional Chinese medicine symptoms, four diagnostic methods by hearing and inquiry and comprehensive treatment information of the patient.
7. The system for whole course management after breast cancer operation as claimed in claim 6, wherein the instructions of the postoperative follow-up protocol comprises on-line hierarchical management of patients with different risks and automatic response of different follow-up frequency instructions, follow-up item instructions, daily work and rest advice, real-time breast cancer treatment stage, disease progression and follow-up related questions;
the real-time breast cancer treatment stage comprises all completed auxiliary treatment stages, new auxiliary treatment stages, perioperative stages, chemotherapy stages, radiotherapy stages, targeted treatment stages, endocrine treatment stages and disease progression and rescue treatment stages.
8. The system for global post-operative management of breast cancer according to claim 6, wherein the mood indicating unit comprises:
an emotion indication presetting unit for presetting a plurality of emotion adjusting fields;
an emotion indication acquisition component for acquiring the user information of the current user and the postoperative risk assessment result;
and the emotion indication generating component is connected with the emotion indication presetting component and the emotion indication acquiring component and is used for selecting corresponding emotion adjusting fields from the emotion adjusting fields according to the user information and the postoperative risk assessment result, combining and adjusting the emotion adjusting fields and outputting the processed emotion adjusting fields as the emotion indications.
9. The system for whole post-operative management of breast cancer according to claim 6, wherein the breast cancer automatic response module comprises the following fields;
a basic communication corpus field for performing basic communication;
pathological information fields including tumor pathological type, grade, size, stage, typing, immunohistochemical index and recovery fields of indications;
perioperative period-related fields including upper limb edema, rehabilitation, wound healing, stitches removing, drainage, and recovery fields of dietary contraindications;
chemotherapy and neoadjuvant chemotherapy fields, including drugs, treatment sessions, possible side effects and treatments, and recovery fields for assessment, related diet and lifestyle notes;
endocrine treatment fields including auxiliary endocrine, late endocrine, castration-related reply fields;
target notice fields including normative treatment course, dosage adjustment, medical insurance and diet contraindication reply fields;
a menstruation/menopause/fertility treatment field, including menopause determination, menstruation and endometrium response fields;
late stage-related therapy fields, including recovery fields for bone protection, immunotherapy, rescue therapy assessment;
radiotherapy fields, including indications, cautions, side effects and responses to treatment fields;
the Chinese medicine treatment field comprises a recovery field of the related matters of decoction mode, time, taking cautions, first decoction and then administration and clinic treatment;
the post-operation follow-up field comprises a follow-up flow, frequency, follow-up content, various inspection frequencies, abnormal indexes and a reply field for processing;
work and rest fields including hair dyeing, vaccine, tourism, exercise, sleep, mood, weight, and cosmetic return fields;
solid diet-related fields including food, health care products, snacks, return fields for daily nutritional needs;
non-solid diet related fields, including return fields of tobacco and wine, meal replacement, beverages, functional drinks and tea making;
disease-associated fields, including hyperlipidemia or dyslipidemia, osteoporosis, cardiovascular disease, mood, and endometrial thickening recovery fields.
10. The breast cancer postoperative global management system of claim 6, wherein the breast cancer offline peripheral diagnosis and treatment unit comprises:
an importing unit for importing breast cancer related information of the patient;
an acquisition unit for acquiring the real-time breast cancer treatment stage;
a traditional Chinese medicine symptom collecting unit for collecting and combing the symptoms of the patient in the offline perioperative period;
the system comprises a consultation unit, a diagnosis unit and a control unit, wherein the consultation unit is used for acquiring the change condition of corresponding symptoms of a breast cancer outpatient during consultation;
the real-time acquisition unit is used for acquiring an examination report of a perioperative breast cancer postoperative patient in a periodic follow-up visit in a hospital in real time;
a general treatment information unit for recording the general treatment information of the patient, wherein the general treatment information comprises the prescription of the patient at the visit;
the acquisition unit is used for acquiring information of the four diagnostic methods of the inquiry and the resection;
the searching unit is used for searching so as to accurately position the tumor stage, the positioning molecule type, the treatment state and the symptom of the breast cancer;
a multi-platform deriving unit for deriving the breast cancer-related information, the treatment stage, the combing result of the patient's symptoms in the offline perioperative period, the change, the examination report of the periodic visit of the hospital, the comprehensive treatment information, the information on the four diagnostic findings by the overseas and olfactive study, the tumor stage, the molecular typing, the treatment status, and the symptoms.
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