Intelligent case management system and method for breast cancer patients

Abstract

The present invention discloses a comprehensive intelligent case management system and method for breast cancer patients, relating to the technical field of intelligent case management. The system aims to address the inconvenience and poor treatment outcomes experienced by breast cancer patients. The system uses a rehabilitation assessment algorithm built into the case management portal to evaluate feedback information and generate personalized rehabilitation recommendations, helping to improve treatment outcomes and patient satisfaction. Real-time feedback and dynamic adjustment of treatment plans help doctors more precisely control the treatment process, thereby improving treatment efficiency. The real-time feedback mechanism enables medical staff to promptly detect and address abnormal patient reactions, reducing medical risks. The use of FFCA technology to construct a disease assessment model can effectively process complex health data, improving the accuracy and efficiency of data analysis. Cluster analysis can categorize similar patient characteristics, helping to identify potential disease patterns and trends.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent case management, and in particular to a full-process intelligent case management system and method for breast cancer patients. Background Art

[0002] Intelligent case management of breast cancer patients refers to the use of modern information technology, especially artificial intelligence technology, to provide personalized, systematic and continuous medical and health management services for breast cancer patients.

[0003] Chinese patent publication number CN215600099U discloses a patient case management and tracking system, which mainly includes the combination of integrated sensor equipment and mobile technology. It can record patient information and intelligently track the patient's treatment progress, remind medical staff to follow up and track the patient's recovery status in a timely manner, and facilitate medical staff, patients and their families to monitor the patient's condition at any time, facilitate hospitals to effectively track and treat patients, and ensure that patients' conditions can receive timely treatment. At the same time, it also allows medical staff to retrieve the patient's condition tracking progress at any time and analyze the condition. Although the above patent solves the problem of patient case management, the following problems still exist in actual operation:

[0004] 1. After obtaining the patient's health data, no more careful diagnosis and analysis of the disease is conducted, resulting in the inability to formulate a more complete plan based on the disease condition.

[0005] 2. Failure to formulate targeted treatment plans based on the patient's actual condition, and failure to provide patients with more comprehensive treatment reminders during the implementation of the treatment plan, resulting in poor treatment results for patients.

[0006] 3. There was no targeted expert consultation and follow-up monitoring of the patient’s feedback during the treatment process and recovery status, resulting in poor treatment results for the patient. Summary of the Invention

[0007] The purpose of the present invention is to provide a full-process intelligent case management system and method for breast cancer patients. The feedback information is evaluated through the rehabilitation assessment algorithm built into the case management port, and personalized rehabilitation suggestions are generated, which helps to improve treatment effects and patient satisfaction. Real-time feedback and dynamic adjustment of treatment plans help doctors control the treatment process more accurately, thereby improving treatment efficiency. The real-time feedback mechanism enables medical staff to promptly detect and deal with abnormal reactions of patients, reducing medical risks. The use of FFCA technology to construct a disease assessment model can effectively process complex health data and improve the accuracy and efficiency of data analysis. Through cluster analysis, similar patient characteristics can be classified to help identify potential disease patterns and trends, which can solve the problems in the existing technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] The intelligent case management system for breast cancer patients includes:

[0010] Patient information collection unit, used for:

[0011] Patients register on the case management port on mobile terminals, enter their health data after registration, and then pre-process the data to obtain the target health data.

[0012] Data brain chip diagnostic unit, used for:

[0013] Utilize FFCA technology to construct a disease assessment model for target health data, combine the constructed disease assessment model with the brain-like chip and perform intelligent analysis and diagnosis. After the intelligent analysis and diagnosis is completed, the patient's diagnostic data is obtained;

[0014] Intervention planning module to:

[0015] Formulate intervention plans based on patient diagnostic data, and convert the formulated intervention plans into visual data. After the visual data conversion is completed, the patient treatment plan is obtained;

[0016] Intervention Program Coordination Unit to:

[0017] Patients can view their treatment plans in the case management portal and carry out treatment according to the contents of the treatment plan. In addition, the case management portal can assist with medication according to the treatment contents of the patient's treatment plan.

[0018] The data brain chip diagnostic unit is also used for:

[0019] Confirming feature data in the target health data, and performing context-aware analysis on the confirmed feature data, where the context-aware analysis includes time context and environmental context;

[0020] The feature data after context-aware analysis is used to build a disease assessment model using FFCA technology;

[0021] The disease assessment model is constructed by first using FFCA technology to perform cluster analysis on the feature data after context-aware analysis;

[0022] After cluster analysis, a recurrent neural network was used to construct a disease assessment model, and the constructed disease assessment model was optimized and verified.

[0023] Preferably, the data brain chip diagnostic unit optimizes and verifies the constructed disease assessment model, including:

[0024] Extracting multiple verification data corresponding to different disease characteristics from the verification data set, and obtaining verification training data and verification result data from each verification data;

[0025] Based on the impact of different disease characteristics on breast cancer patients, the corresponding validation weights of the validation training data are set;

[0026] Validating the disease assessment model based on the validation training data and validation result data to obtain the deviation and variance of the model under each disease characteristic;

[0027] Based on the deviation and variance of the model under each disease condition feature, the model evaluation value K of the disease condition assessment model is calculated according to the following formula;

[0028]

[0029] Where n represents the number of disease characteristics, represents the verification weight of the i-th disease feature, represents the deviation value of the i-th disease feature, represents the variance value of the i-th disease characteristic, represents the standard deviation value, represents the standard deviation value, represents a constant;

[0030] Determining whether the model evaluation value is greater than a preset evaluation value;

[0031] If so, determining that the condition assessment model does not need to be optimized;

[0032] Otherwise, determining that the condition assessment model needs to be optimized;

[0033] Based on the model evaluation value, the optimization weight F of the disease assessment model is determined according to the following formula;

[0034]

[0035] in, represents the average standard weight, Indicates the preset evaluation value;

[0036] An optimization scheme corresponding to the optimization weight is obtained from a preset optimization scheme set to optimize the condition assessment model.

[0037] Preferably, the data brain chip diagnostic unit is also used for:

[0038] Adapt the optimized and verified disease assessment model to the interface of the brain-like chip;

[0039] After the adaptation process is completed, the optimized and verified disease assessment model will be deployed on the brain-like chip;

[0040] The brain-like chip uses pattern recognition and neural network algorithms to perform real-time diagnosis on the received disease assessment model;

[0041] The diagnosis includes the severity, type, stage of development and complications involved in the patient's condition;

[0042] After the diagnosis is completed, the patient's diagnostic data is obtained.

[0043] Preferably, the patient information collection unit is further used to:

[0044] Patients use mobile terminals to scan the code to register at the case management port. After registration is completed, patients log in to the case management port and enter their personal health data;

[0045] Among them, personal health data includes the patient's basic information, medical history information, physiological indicators, biochemical indicators, historical treatment information, living habits, psychological state, current physical symptoms and medication use;

[0046] Preprocess the entered health data, which includes data cleaning, data standardization, data conversion, feature extraction and data integration;

[0047] After data preprocessing is completed, the target health data is obtained.

[0048] Preferably, the data brain chip diagnostic unit uses a recurrent neural network to construct a disease assessment model after cluster analysis, including:

[0049] An initial model building unit is used to obtain characteristic data after cluster analysis, and perform feature selection and feature conversion on the characteristic data based on multiple disease assessment requirements to obtain multiple target feature sets, and train the multiple target feature sets based on a recurrent neural network to obtain multiple initial assessment models;

[0050] a model integration unit, configured to adjust and verify hyperparameters of the multiple initial evaluation models, obtain multiple reference evaluation models corresponding to the optimal hyperparameters, obtain prediction results of the multiple reference evaluation models, and train the prediction results based on a recurrent neural network to obtain a comprehensive evaluation model;

[0051] A model comparison unit is used to obtain model structure differences between the comprehensive evaluation model and multiple reference evaluation models, and determine whether the model structure differences are all within a preset difference range;

[0052] If so, the comprehensive evaluation model is used as the target evaluation model;

[0053] Otherwise, the model structure of the comprehensive evaluation model is modified based on the model structure difference, and the target evaluation model is obtained according to the modification result;

[0054] The model simplification unit is used to simplify the target assessment model based on the model generalization capability requirements to obtain the final disease assessment model.

[0055] Preferably, the intervention plan formulation unit is further configured to:

[0056] Before formulating an intervention plan, the patient's diagnostic data should be thoroughly analyzed;

[0057] After in-depth analysis, the patient's symptom characteristics, risk assessment information and individual characteristics are obtained from the patient's diagnostic data;

[0058] Build a treatment plan framework based on the patient's diagnostic data, which includes drug therapy, surgical treatment, radiotherapy, chemotherapy, and lifestyle intervention;

[0059] Refine the treatment plan based on the treatment plan framework and in-depth analysis of patient diagnostic data, including specific steps, timelines, and expected goals of treatment;

[0060] Then, the type of drug, dosage, administration time and method of administration are confirmed based on the patient's drug use and treatment response;

[0061] Annotate treatment side effects based on detailed treatment plans and patient medication choices;

[0062] The final treatment plan is converted into a visual form of charts and timelines for presentation, and the patient's treatment plan, treatment time, medication status, and side effect notes are obtained after presentation;

[0063] Label the treatment plan in visualization as the patient treatment plan.

[0064] Preferably, the intervention plan coordination unit is further used to:

[0065] After logging into the case management portal on a mobile terminal, the patient can view the patient's treatment plan;

[0066] Patients receive treatment according to the treatment method and treatment time in the patient's treatment plan, and during the treatment process, patients provide feedback on reactions and side effects to the case management port;

[0067] At the same time, the case management port provides medication assistance reminders based on the drug type, dosage, administration time and administration method in the patient's treatment plan;

[0068] The reminder method is to send the drug type, dosage, administration time and administration method to the patient's mobile terminal for reminder.

[0069] Preferably, it also includes a rehabilitation tracking and supervision unit for:

[0070] When patients receive treatment according to the treatment plan, treatment feedback is recorded in real time. The case management port conducts rehabilitation assessment based on the feedback record, generates rehabilitation suggestions based on the rehabilitation assessment results, and displays the generated rehabilitation suggestions visually.

[0071] Among them, during the treatment process, patients log in to the case management port through mobile terminals and input feedback information in real time;

[0072] Feedback includes treatment implementation, symptoms or side effects, psychological status and impact on daily life, and unexpected events;

[0073] The case management port receives the feedback information and has a built-in rehabilitation assessment algorithm, which performs rehabilitation assessment on the feedback information;

[0074] The rehabilitation assessment process includes symptom analysis, side effect assessment, and treatment compliance assessment;

[0075] Based on the results of the rehabilitation assessment, the case management portal generates personalized rehabilitation recommendations;

[0076] Recovery recommendations include treatment regimen adjustments, side effect management, lifestyle adjustments, and psychological counseling;

[0077] The rehabilitation suggestions are converted into visual form and then transmitted to the mobile terminal for display.

[0078] Preferably, it also includes a remote monitoring consultation unit for:

[0079] The case management port transmits the patient's feedback records and rehabilitation suggestions to the remote consultation platform for expert consultation, and conducts monitoring and treatment based on the consultation results;

[0080] Among them, the patient's feedback records and rehabilitation suggestions are summarized and the summarized data is encrypted;

[0081] The case management portal logs into the remote consultation platform through a secure network connection;

[0082] The encrypted summary data is uploaded to the remote consultation platform, wherein the case management port confirms the completion of data transmission and receives confirmation information from the remote consultation platform;

[0083] After the remote consultation platform receives the data, the expert logs in to the remote consultation platform to view the patient's feedback records and rehabilitation suggestions;

[0084] Professional analysis will be conducted based on the feedback records and rehabilitation suggestions, and consultation opinions will be obtained after professional analysis;

[0085] The remote consultation platform transmits the consultation opinions to the case management port, which organizes the consultation opinions into documents and monitors the patients according to the documents;

[0086] Custody management includes adjustment of treatment plan and enhanced monitoring measures.

[0087] Intelligent case management methods for breast cancer patients throughout the entire process, including:

[0088] S1: The patient first registers on the case management portal on the mobile terminal and enters personal health data after registration;

[0089] S2: The case management portal uses the patient’s personal health data to assess and diagnose the condition;

[0090] S3: The case management port formulates a treatment plan for the patient based on the condition assessment and diagnosis results;

[0091] S4: The patient opens the case management port on the mobile terminal to view and execute the treatment plan;

[0092] S5: The patient promptly records the feedback information during the treatment process, and the case management port generates rehabilitation suggestions based on the feedback information;

[0093] S6: The case management port transmits the rehabilitation suggestions and feedback records to the remote consultation platform for expert consultation. The remote consultation platform then transmits the consultation records to the case management port. The case management port monitors the consultation records and generates results.

[0094] Compared with the prior art, the present invention has the following beneficial effects:

[0095] 1. The intelligent case management system and method for breast cancer patients provided by the present invention utilizes FFCA technology to construct a disease assessment model, which can effectively process complex health data and improve the accuracy and efficiency of data analysis. Cluster analysis can classify similar patient characteristics to help identify potential disease patterns and trends. The recurrent neural network is used to construct a disease assessment model, which can process time series data, capture the dynamic characteristics of patients' health status over time, and improve the accuracy of predictions.

[0096] 2. The intelligent case management system and method for breast cancer patients provided by this invention allows medical staff to more effectively evaluate treatment outcomes and adjust treatment plans in a timely manner through data analysis, thereby improving the utilization efficiency of medical resources. The real-time feedback mechanism enables medical staff to promptly detect and address patients' adverse reactions, reducing medical risks. The drug type, dosage, administration time, and method are determined based on the patient's medication usage and treatment response, demonstrating the dynamic and flexible nature of treatment plans. Timely adjustments to treatment plans can respond to changes in the patient's condition and ensure treatment effectiveness.

[0097] 3. The present invention provides a comprehensive intelligent case management system and method for breast cancer patients. The case management port evaluates feedback information through a built-in rehabilitation assessment algorithm and generates personalized rehabilitation recommendations, which helps improve treatment efficacy and patient satisfaction. Real-time feedback and dynamic adjustment of treatment plans help doctors control the treatment process more accurately, thereby improving treatment efficiency. Medical staff can track the patient's recovery progress in real time, adjust treatment plans in a timely manner, and strengthen monitoring measures, thereby improving the continuity and pertinence of medical services. Remote consultation and case management reduce the number of times patients travel to and from the hospital, saving time and energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 This is a schematic diagram of the intelligent case management unit for breast cancer patients of the present invention;

[0099] Figure 2 This is a schematic diagram of the entire intelligent case management process for breast cancer patients according to the present invention. DETAILED DESCRIPTION

[0100] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0101] In order to solve the problem in existing technologies that after obtaining the patient's health data, a more thorough diagnosis and analysis of the disease is not carried out, which leads to the inability to formulate a more complete plan based on the disease condition, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0102] The intelligent case management system for breast cancer patients includes:

[0103] Patient information collection unit, used for:

[0104] Patients register on the case management port on mobile terminals, enter their health data after registration, and then pre-process the data to obtain the target health data.

[0105] Data brain chip diagnostic unit, used for:

[0106] Utilize FFCA technology to construct a disease assessment model for target health data, combine the constructed disease assessment model with the brain-like chip and perform intelligent analysis and diagnosis. After the intelligent analysis and diagnosis is completed, the patient's diagnostic data is obtained;

[0107] Intervention planning module to:

[0108] Formulate intervention plans based on patient diagnostic data, and convert the formulated intervention plans into visual data. After the visual data conversion is completed, the patient treatment plan is obtained;

[0109] Intervention Program Coordination Unit to:

[0110] Patients can view their treatment plans in the case management portal and carry out treatment according to the contents of the treatment plan. In addition, the case management portal can assist with medication according to the treatment contents of the patient's treatment plan.

[0111] Rehabilitation tracking and supervision unit is used to:

[0112] When patients receive treatment according to the treatment plan, treatment feedback is recorded in real time. The case management port conducts rehabilitation assessment based on the feedback record, generates rehabilitation suggestions based on the rehabilitation assessment results, and displays the generated rehabilitation suggestions visually.

[0113] Remote monitoring consultation unit, used for:

[0114] The case management port transmits the patient's feedback records and rehabilitation suggestions to the remote consultation platform for expert consultation, and conducts monitoring and treatment based on the consultation results.

[0115] Specifically, through the patient information collection unit, patients can update their health data at any time, medical institutions can continuously track the health status of patients and promptly identify potential health problems. The data brain chip diagnostic unit provides doctors with scientific decision-making support to help them develop more reasonable treatment plans and improve the quality of medical services. The visual treatment plan of the intervention plan formulation unit is also convenient for recording and tracking the patient's treatment progress and medication status. The convenience of the mobile terminal of the intervention plan coordination unit allows patients to view treatment plans and medication reminders anytime and anywhere, improving the flexibility and convenience of treatment. The personalized rehabilitation suggestions and optimized treatment plans of the rehabilitation tracking and supervision unit are more likely to improve the treatment effect and promote the patient's rehabilitation. Through the remote monitoring consultation unit, medical staff can track the patient's rehabilitation progress in real time, adjust the treatment plan and strengthen monitoring measures in a timely manner, thereby improving the continuity and pertinence of medical services.

[0116] The patient information collection unit is also used to:

[0117] Patients use mobile terminals to scan the code to register at the case management port. After registration is completed, patients log in to the case management port and enter their personal health data;

[0118] Among them, personal health data includes the patient's basic information, medical history information, physiological indicators, biochemical indicators, historical treatment information, living habits, psychological state, current physical symptoms and medication use;

[0119] Preprocess the entered health data, which includes data cleaning, data standardization, data conversion, feature extraction and data integration;

[0120] After data preprocessing is completed, the target health data is obtained.

[0121] Specifically, patients can scan the code to register and log in to the case management port anytime and anywhere through mobile terminals to enter their personal health data. This flexibility greatly improves the convenience of data entry, allowing patients to operate according to their own schedule and actual situation. Registering and entering data through mobile terminals avoids the cumbersome process of traditional paper filling, reducing patients' waiting time and the administrative burden of medical institutions. The data preprocessing process includes data cleaning, data standardization, data conversion and other steps, which can remove duplicate, redundant and erroneous data, fill in missing values, and ensure the accuracy and completeness of the data. Data preprocessing can also unify and convert data from different sources and in different formats, making the data easier to analyze and use. Patients can update their health data at any time, and medical institutions can continuously track patients' health status and promptly identify potential health problems.

[0122] The data brain chip diagnostic unit is also used for:

[0123] Confirming feature data in the target health data, and performing context-aware analysis on the confirmed feature data, where the context-aware analysis includes time context and environmental context;

[0124] The feature data after context-aware analysis is used to build a disease assessment model using FFCA technology;

[0125] The disease assessment model is constructed by first using FFCA technology to perform cluster analysis on the feature data after context-aware analysis;

[0126] After cluster analysis, a recurrent neural network is used to construct a disease assessment model, and the constructed disease assessment model is optimized and verified;

[0127] Adapt the optimized and verified disease assessment model to the interface of the brain-like chip;

[0128] After the adaptation process is completed, the optimized and verified disease assessment model will be deployed on the brain-like chip;

[0129] The brain-like chip uses pattern recognition and neural network algorithms to perform real-time diagnosis on the received disease assessment model;

[0130] The diagnosis includes the severity, type, stage of development and complications involved in the patient's condition;

[0131] After the diagnosis is completed, the patient's diagnostic data is obtained.

[0132] After cluster analysis in the data brain chip diagnostic unit, a recurrent neural network is used to construct a disease assessment model, including:

[0133] An initial model building unit is used to obtain characteristic data after cluster analysis, and perform feature selection and feature conversion on the characteristic data based on multiple disease assessment requirements to obtain multiple target feature sets, and train the multiple target feature sets based on a recurrent neural network to obtain multiple initial assessment models;

[0134] a model integration unit, configured to adjust and verify hyperparameters of the multiple initial evaluation models, obtain multiple reference evaluation models corresponding to the optimal hyperparameters, obtain prediction results of the multiple reference evaluation models, and train the prediction results based on a recurrent neural network to obtain a comprehensive evaluation model;

[0135] A model comparison unit is used to obtain model structure differences between the comprehensive evaluation model and multiple reference evaluation models, and determine whether the model structure differences are all within a preset difference range;

[0136] If so, the comprehensive evaluation model is used as the target evaluation model;

[0137] Otherwise, the model structure of the comprehensive evaluation model is modified based on the model structure difference, and the target evaluation model is obtained according to the modification result;

[0138] The model simplification unit is used to simplify the target assessment model based on the model generalization capability requirements to obtain a final disease assessment model.

[0139] The beneficial effects of the above design scheme are: by performing feature selection and feature conversion on the feature data based on multiple disease assessment requirements, multiple target feature sets are obtained to realize the training of multiple models, and the accuracy of the model is guaranteed by verifying the hyperparameters of the model. Then, the model is integrated to obtain a comprehensive evaluation model, and the comprehensive evaluation model is corrected based on multiple reference evaluation models to obtain the target evaluation model's recognition and evaluation capabilities for each target feature. Finally, based on the model generalization capability requirements, the target evaluation model is simplified to obtain the final disease assessment model, which ensures the generalization capability of the model and provides an accurate model basis for the diagnosis of the patient's disease.

[0140] The data brain chip diagnostic unit will optimize and verify the constructed disease assessment model, including:

[0141] Extracting multiple verification data corresponding to different disease characteristics from the verification data set, and obtaining verification training data and verification result data from each verification data;

[0142] Based on the impact of different disease characteristics on breast cancer patients, the corresponding validation weights of the validation training data are set;

[0143] Validating the disease assessment model based on the validation training data and validation result data to obtain the deviation and variance of the model under each disease characteristic;

[0144] Based on the deviation and variance of the model under each disease condition feature, the model evaluation value K of the disease condition assessment model is calculated according to the following formula;

[0145]

[0146] Where n represents the number of disease characteristics, represents the verification weight of the i-th disease feature, represents the deviation value of the i-th disease feature, represents the variance value of the i-th disease characteristic, represents the standard deviation value, represents the standard deviation value, represents a constant;

[0147] Determining whether the model evaluation value is greater than a preset evaluation value;

[0148] If so, determining that the condition assessment model does not need to be optimized;

[0149] Otherwise, determining that the condition assessment model needs to be optimized;

[0150] Based on the model evaluation value, the optimization weight F of the disease assessment model is determined according to the following formula;

[0151]

[0152] in, represents the average standard weight, Indicates the preset evaluation value;

[0153] An optimization scheme corresponding to the optimization weight is obtained from a preset optimization scheme set to optimize the condition assessment model.

[0154] In this embodiment, the variance value, standard deviation value, and constant are preset according to actual conditions.

[0155] In this embodiment, different disease characteristics, such as the degree of canceration and the area of canceration of breast cancer patients, are classified. The greater the impact of the disease characteristics on the breast cancer patients, the greater the corresponding expedition weight.

[0156] In this embodiment, the validation result data is used to compare with the predicted values to determine the bias and variance.

[0157] In this embodiment, the preset optimization solution set predefines the solution content and the corresponding relationship between the solution and the optimization weight according to specific circumstances.

[0158] The beneficial effects of the above design scheme are: by setting the verification weight of the corresponding verification training data based on the degree of influence of different disease characteristics on breast cancer patients, the disease assessment model is verified based on the verification training data and the verification result data, and the deviation and variance of the model under each disease characteristic are obtained to calculate the model evaluation value. Through classification verification, the determined model evaluation value is made to better meet the actual needs. Based on the model evaluation value, the verification weight is taken into account to determine the optimization of the model, ensuring that the optimized model better meets the actual needs and providing an accurate model basis for the diagnosis of the patient's disease.

[0159] Specifically, by performing context-aware analysis on patient characteristic data (including time and environmental context), we can better understand the patient's specific situation and provide personalized disease assessment and treatment plans. Using FFCA technology to build a disease assessment model can effectively process complex health data and improve the accuracy and efficiency of data analysis. Through cluster analysis, similar patient characteristics can be classified to help identify potential disease patterns and trends. Using recurrent neural networks to build a disease assessment model can process time series data, capture the dynamic characteristics of patients' health status over time, and improve the accuracy of predictions. The optimized disease assessment model is deployed on a brain-like chip, and pattern recognition and neural network algorithms are used for real-time diagnosis, which can quickly respond to changes in patients' health and provide Provide timely medical intervention. The diagnostic results include not only the severity, type and development stage of the patient's condition, but also the identification of complications, providing a comprehensive health assessment to help doctors develop more effective treatment plans. By optimizing and verifying the constructed condition assessment model, the reliability and effectiveness of the model are ensured, and the risks of misdiagnosis and missed diagnosis are reduced. Through data analysis and model construction, scientific decision-making support is provided to doctors to help them develop more reasonable treatment plans and improve the quality of medical services. Patients can understand their own condition assessment process and results, enhance their trust and sense of participation in the treatment plan, and promote patients' self-management and health awareness. The interface adaptation processing between the model and the brain-like chip enables the system to be flexibly adjusted according to the needs of different patients and adapt to different clinical scenarios.

[0160] In order to solve the problem in the existing technology that there is no targeted treatment plan formulated according to the actual condition of the patient, and no more complete treatment reminders for the patient during the implementation of the treatment plan, which leads to poor treatment effect for the patient, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0161] The Intervention Planning Module is also used to:

[0162] Before formulating an intervention plan, the patient's diagnostic data should be thoroughly analyzed;

[0163] After in-depth analysis, the patient's symptom characteristics, risk assessment information and individual characteristics are obtained from the patient's diagnostic data;

[0164] Build a treatment plan framework based on the patient's diagnostic data, which includes drug therapy, surgical treatment, radiotherapy, chemotherapy, and lifestyle intervention;

[0165] Refine the treatment plan based on the treatment plan framework and in-depth analysis of patient diagnostic data, including specific steps, timelines, and expected goals of treatment;

[0166] Then, the type of drug, dosage, administration time and method of administration are confirmed based on the patient's drug use and treatment response;

[0167] Annotate treatment side effects based on detailed treatment plans and patient medication choices;

[0168] The final treatment plan is converted into a visual form of charts and timelines for presentation, and the patient's treatment plan, treatment time, medication status, and side effect notes are obtained after presentation;

[0169] Label the treatment plan in visualization as the patient treatment plan.

[0170] Specifically, the document emphasizes in-depth analysis of patient diagnostic data, which is the foundation for developing effective treatment plans. This data-driven approach ensures more scientific and precise treatment plans, avoiding blindness and arbitrariness. In-depth analysis takes into account the patient's symptom profile, risk assessment information, and individual characteristics, helping to develop treatment plans more tailored to the patient's specific circumstances. Personalized treatment can improve treatment outcomes, reduce unnecessary medical interventions, and enhance patient satisfaction and comfort. The treatment plan framework encompasses multiple approaches, including medication, surgery, radiation therapy, chemotherapy, and lifestyle interventions, embodying the concept of comprehensive care. Based on the patient's specific circumstances, the most appropriate treatment combination can be selected to achieve optimal results. This detailed approach, including specific steps, timelines, and expected goals, makes the treatment process more clear and manageable. Both patients and healthcare professionals can clearly understand treatment progress and expected outcomes, fostering confidence and increased compliance. Medication type, dosage, administration schedule, and route of administration are determined based on the patient's medication usage and response, demonstrating the dynamic and flexible nature of the treatment plan. Timely adjustments to treatment plans can address changes in a patient's condition and ensure treatment effectiveness. Annotations of treatment side effects help patients and healthcare professionals understand potential treatment risks in advance and take appropriate preventative or countermeasures. This improves patient safety and reduces discomfort and complications during treatment. Visualizing treatment plans in the form of charts and timelines makes them more intuitive and accessible. This helps patients and healthcare professionals better communicate and understand treatment plans, improving treatment compliance and cooperation. Visualized treatment plans also facilitate recording and tracking of patients' treatment progress and medication use.

[0171] The Intervention Programme Coordination Unit also serves to:

[0172] After logging into the case management portal on a mobile terminal, the patient can view the patient's treatment plan;

[0173] Patients receive treatment according to the treatment method and treatment time in the patient's treatment plan, and during the treatment process, patients provide feedback on reactions and side effects to the case management port;

[0174] At the same time, the case management port provides medication assistance reminders based on the drug type, dosage, administration time and administration method in the patient's treatment plan;

[0175] The reminder method is to send the drug type, dosage, administration time and administration method to the patient's mobile terminal for reminder.

[0176] Specifically, patients can conveniently review their treatment plans on their mobile devices, providing a clearer understanding of treatment steps and expected outcomes. Real-time feedback on treatment reactions and side effects helps strengthen patient engagement and accountability, while also improving treatment compliance. Through the case management portal, patients can accurately understand treatment schedules and methods, reducing delays or errors caused by forgetfulness or misunderstandings. Medication reminders ensure patients take medications on time, in the correct dosage, and according to the correct method, preventing missed doses or overdosing, thereby improving treatment outcomes. The case management portal automatically records patient progress and feedback, reducing the manual recording workload for healthcare professionals. Data analysis allows healthcare professionals to more effectively assess treatment effectiveness and make timely adjustments to treatment plans, thereby improving the efficient use of medical resources. The real-time feedback mechanism enables healthcare professionals to promptly identify and address unusual patient reactions, reducing medical risks. Medication reminders help reduce medical errors caused by improper medication use and improve overall healthcare safety. Patients can use the case management portal to provide feedback to healthcare professionals regarding issues and concerns during treatment, strengthening communication and trust between doctors and patients. Medical staff can also provide patients with personalized guidance and advice through the portal, improving treatment effectiveness and patient satisfaction. The convenience of mobile terminals allows patients to view treatment plans and medication reminders anytime, anywhere, increasing treatment flexibility and convenience. Personalized reminder and feedback mechanisms help improve overall patient satisfaction and experience.

[0177] In order to solve the problem in existing technologies that patients’ feedback during treatment and recovery status are not used for targeted expert consultation and post-monitoring, which leads to poor treatment results for patients, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0178] The rehabilitation tracking and supervision unit is also used to:

[0179] During the treatment process, patients log in to the case management portal through mobile terminals and input feedback information in real time;

[0180] Feedback includes treatment implementation, symptoms or side effects, psychological status and impact on daily life, and unexpected events;

[0181] The case management port receives the feedback information and has a built-in rehabilitation assessment algorithm, which performs rehabilitation assessment on the feedback information;

[0182] The rehabilitation assessment process includes symptom analysis, side effect assessment, and treatment compliance assessment;

[0183] Based on the results of the rehabilitation assessment, the case management portal generates personalized rehabilitation recommendations;

[0184] Recovery recommendations include treatment regimen adjustments, side effect management, lifestyle adjustments, and psychological counseling;

[0185] The rehabilitation suggestions are converted into visual form and then transmitted to the mobile terminal for display.

[0186] Specifically, patients can input real-time feedback via mobile devices, making the treatment process more dynamic and flexible. Real-time feedback helps doctors and treatment teams understand the patient's condition promptly and respond quickly. Feedback covers treatment implementation, symptoms or side effects, psychological status, impact on daily life, and unexpected events, providing a comprehensive view of the patient's condition. This comprehensive data collection helps more accurately assess the patient's recovery progress and potential problems. The case management portal uses a built-in rehabilitation assessment algorithm to evaluate this feedback and generate personalized rehabilitation recommendations. This personalized advice is more tailored to the patient's specific needs, helping to improve treatment effectiveness and patient satisfaction. Rehabilitation recommendations are visualized and displayed on mobile devices, allowing patients to intuitively understand their rehabilitation plan and recommendations. This convenience improves patient engagement and understanding of treatment plans. Real-time feedback and dynamic adjustments to treatment plans help doctors more precisely control the treatment process, thereby improving treatment efficiency. Personalized rehabilitation recommendations and optimized treatment plans are more likely to improve treatment outcomes and promote patient recovery. Through interaction between the mobile device and the case management portal, patients can feel more attentive and personalized care. This interaction helps strengthen communication and trust between doctors and patients, improving patient compliance and satisfaction.

[0187] The remote monitoring consultation unit is also used for:

[0188] Summarize the patient's feedback records and rehabilitation suggestions, and encrypt the summarized data;

[0189] The case management portal logs into the remote consultation platform through a secure network connection;

[0190] The encrypted summary data is uploaded to the remote consultation platform, wherein the case management port confirms the completion of data transmission and receives confirmation information from the remote consultation platform;

[0191] After the remote consultation platform receives the data, the expert logs in to the remote consultation platform to view the patient's feedback records and rehabilitation suggestions;

[0192] Professional analysis will be conducted based on the feedback records and rehabilitation suggestions, and consultation opinions will be obtained after professional analysis;

[0193] The remote consultation platform transmits the consultation opinions to the case management port, which organizes the consultation opinions into documents and monitors the patients according to the documents;

[0194] Custody management includes adjustment of treatment plan and enhanced monitoring measures.

[0195] Specifically, patient feedback records and rehabilitation suggestions are encrypted after data aggregation. This ensures data security during transmission and storage, preventing the leakage of sensitive information. The case management portal logs into the remote consultation platform via a secure network connection, further enhancing data transmission security. The remote consultation platform enables experts to access patient feedback records and rehabilitation suggestions promptly, regardless of geographical location, for professional analysis and consultation advice. A data transmission confirmation mechanism ensures data integrity and reliability, improving the accuracy and efficiency of consultations. The case management portal conveniently organizes and analyzes consultation opinions, documents them, and provides personalized monitoring and treatment plans for patients. Through the case management portal, medical staff can track patients' recovery progress in real time, adjust treatment plans, and strengthen monitoring measures in a timely manner, improving the continuity and relevance of medical services. Remote consultation and case management reduce patient visits to the hospital, saving time and effort, and enhancing the patient experience. Professional consultation advice and personalized monitoring and treatment plans help patients better understand and implement their rehabilitation plans, accelerating recovery progress. The remote consultation platform enables more efficient use of medical resources, reducing waste and duplication of resources. Through data aggregation and analysis, medical institutions can better understand patients' rehabilitation needs and problems, thereby optimizing the allocation and use of medical resources.

[0196] Intelligent case management methods for breast cancer patients throughout the entire process, including:

[0197] S1: The patient first registers on the case management portal on the mobile terminal and enters personal health data after registration;

[0198] S2: The case management portal uses the patient’s personal health data to assess and diagnose the condition;

[0199] S3: The case management port formulates a treatment plan for the patient based on the condition assessment and diagnosis results;

[0200] S4: The patient opens the case management port on the mobile terminal to view and execute the treatment plan;

[0201] S5: The patient promptly records the feedback information during the treatment process, and the case management port generates rehabilitation suggestions based on the feedback information;

[0202] S6: The case management port transmits the rehabilitation suggestions and feedback records to the remote consultation platform for expert consultation. The remote consultation platform then transmits the consultation records to the case management port. The case management port monitors the consultation records and generates results.

[0203] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0204] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. The intelligent case management system for breast cancer patients is characterized by: include: Patient information collection unit, used for: Patients register on the case management port on mobile terminals, enter their health data after registration, and then pre-process the data to obtain the target health data. Data brain chip diagnostic unit, used for: Utilize FFCA technology to construct a disease assessment model for target health data, combine the constructed disease assessment model with the brain-like chip and perform intelligent analysis and diagnosis. After the intelligent analysis and diagnosis is completed, the patient's diagnostic data is obtained; Intervention planning module to: Formulate intervention plans based on patient diagnostic data, and convert the formulated intervention plans into visual data. After the visual data conversion is completed, the patient treatment plan is obtained; Intervention Program Coordination Unit to: Patients can view their treatment plans in the case management portal and carry out treatment according to the contents of the treatment plan. In addition, the case management portal can assist with medication according to the treatment contents of the patient's treatment plan. The data brain chip diagnostic unit is also used for: Confirming feature data in the target health data, and performing context-aware analysis on the confirmed feature data, where the context-aware analysis includes time context and environmental context; The feature data after context-aware analysis is used to build a disease assessment model using FFCA technology; The disease assessment model is constructed by first using FFCA technology to perform cluster analysis on the feature data after context-aware analysis; After cluster analysis, a recurrent neural network is used to construct a disease assessment model, and the constructed disease assessment model is optimized and verified; The data brain chip diagnostic unit will optimize and verify the constructed disease assessment model, including: Extracting multiple verification data corresponding to different disease characteristics from the verification data set, and obtaining verification training data and verification result data from each verification data; Based on the impact of different disease characteristics on breast cancer patients, the corresponding validation weights of the validation training data are set; Validating the disease assessment model based on the validation training data and validation result data to obtain the deviation and variance of the model under each disease characteristic; Based on the deviation and variance of the model under each disease condition feature, the model evaluation value K of the disease condition assessment model is calculated according to the following formula; Where n represents the number of disease characteristics, represents the verification weight of the i-th disease feature, represents the deviation value of the i-th disease feature, represents the variance value of the i-th disease characteristic, represents the standard deviation value, represents the standard deviation value, represents a constant; Determining whether the model evaluation value is greater than a preset evaluation value; If so, determining that the condition assessment model does not need to be optimized; Otherwise, determining that the condition assessment model needs to be optimized; Based on the model evaluation value, the optimization weight F of the disease assessment model is determined according to the following formula; in, represents the average standard weight, Indicates the preset evaluation value; An optimization scheme corresponding to the optimization weight is obtained from a preset optimization scheme set to optimize the condition assessment model.

2. The intelligent case management system for breast cancer patients according to claim 1, characterized in that: The data brain chip diagnostic unit is also used for: Adapt the optimized and verified disease assessment model to the interface of the brain-like chip; After the adaptation process is completed, the optimized and verified disease assessment model will be deployed on the brain-like chip; The brain-like chip uses pattern recognition and neural network algorithms to perform real-time diagnosis on the received disease assessment model; The diagnosis includes the severity, type, stage of development and complications involved in the patient's condition; After the diagnosis is completed, the patient's diagnostic data is obtained.

3. The intelligent case management system for breast cancer patients according to claim 2, characterized in that: The patient information collection unit is further used to: Patients use mobile terminals to scan the code to register at the case management port. After registration is completed, patients log in to the case management port and enter their personal health data; Among them, personal health data includes the patient's basic information, medical history information, physiological indicators, biochemical indicators, historical treatment information, living habits, psychological state, current physical symptoms and medication use; Preprocess the entered health data, which includes data cleaning, data standardization, data conversion, feature extraction and data integration; After data preprocessing is completed, the target health data is obtained.

4. The intelligent case management system for breast cancer patients according to claim 3, characterized in that: After cluster analysis in the data brain chip diagnostic unit, a recurrent neural network is used to construct a disease assessment model, including: An initial model building unit is used to obtain characteristic data after cluster analysis, and perform feature selection and feature conversion on the characteristic data based on multiple disease assessment requirements to obtain multiple target feature sets, and train the multiple target feature sets based on a recurrent neural network to obtain multiple initial assessment models; a model integration unit, configured to adjust and verify hyperparameters of the multiple initial evaluation models, obtain multiple reference evaluation models corresponding to the optimal hyperparameters, obtain prediction results of the multiple reference evaluation models, and train the prediction results based on a recurrent neural network to obtain a comprehensive evaluation model; A model comparison unit is used to obtain model structure differences between the comprehensive evaluation model and multiple reference evaluation models, and determine whether the model structure differences are all within a preset difference range; If so, the comprehensive evaluation model is used as the target evaluation model; Otherwise, the model structure of the comprehensive evaluation model is modified based on the model structure difference, and the target evaluation model is obtained according to the modification result; The model simplification unit is used to simplify the target assessment model based on the model generalization capability requirements to obtain a final disease assessment model.

5. The intelligent case management system for breast cancer patients according to claim 4 is characterized in that: The intervention program formulation unit is also used to: Before formulating an intervention plan, the patient's diagnostic data should be thoroughly analyzed; After in-depth analysis, the patient's symptom characteristics, risk assessment information and individual characteristics are obtained from the patient's diagnostic data; Build a treatment plan framework based on the patient's diagnostic data, which includes drug therapy, surgical treatment, radiotherapy, chemotherapy, and lifestyle intervention; Refine the treatment plan based on the treatment plan framework and in-depth analysis of patient diagnostic data, including specific steps, timelines, and expected goals of treatment; Then, the type of drug, dosage, administration time and method of administration are confirmed based on the patient's drug use and treatment response; Annotate treatment side effects based on detailed treatment plans and patient medication choices; The final treatment plan is converted into a visual form of charts and timelines for presentation, and the patient's treatment plan, treatment time, medication status, and side effect notes are obtained after presentation; Label the treatment plan in visualization as the patient treatment plan.

6. The intelligent case management system for breast cancer patients according to claim 5, characterized in that: The intervention program coordination unit is also responsible for: After logging into the case management portal on a mobile terminal, the patient can view the patient's treatment plan; Patients receive treatment according to the treatment method and treatment time in the patient's treatment plan, and during the treatment process, patients provide feedback on reactions and side effects to the case management port; At the same time, the case management port provides medication assistance reminders based on the drug type, dosage, administration time and administration method in the patient's treatment plan; The reminder method is to send the drug type, dosage, administration time and administration method to the patient's mobile terminal for reminder.

7. The intelligent case management system for breast cancer patients according to claim 6, characterized in that: It also includes a rehabilitation follow-up supervision unit for: When patients receive treatment according to the treatment plan, treatment feedback is recorded in real time. The case management port conducts rehabilitation assessment based on the feedback record, generates rehabilitation suggestions based on the rehabilitation assessment results, and displays the generated rehabilitation suggestions visually. Among them, during the treatment process, patients log in to the case management port through mobile terminals and input feedback information in real time; Feedback includes treatment implementation, symptoms or side effects, psychological status and impact on daily life, and unexpected events; The case management port receives the feedback information and has a built-in rehabilitation assessment algorithm, which performs rehabilitation assessment on the feedback information; The rehabilitation assessment process includes symptom analysis, side effect assessment, and treatment compliance assessment; Based on the results of the rehabilitation assessment, the case management portal generates personalized rehabilitation recommendations; Recovery recommendations include treatment regimen adjustments, side effect management, lifestyle adjustments, and psychological counseling; The rehabilitation suggestions are converted into visual form and then transmitted to the mobile terminal for display.

8. The intelligent case management system for breast cancer patients according to claim 7, characterized in that: Also includes a remote monitoring consultation unit for: The case management port transmits the patient's feedback records and rehabilitation suggestions to the remote consultation platform for expert consultation, and conducts monitoring and treatment based on the consultation results; Among them, the patient's feedback records and rehabilitation suggestions are summarized and the summarized data is encrypted; The case management portal logs into the remote consultation platform through a secure network connection; The encrypted summary data is uploaded to the remote consultation platform, wherein the case management port confirms the completion of data transmission and receives confirmation information from the remote consultation platform; After the remote consultation platform receives the data, the expert logs in to the remote consultation platform to view the patient's feedback records and rehabilitation suggestions; Professional analysis will be conducted based on the feedback records and rehabilitation suggestions, and consultation opinions will be obtained after professional analysis; The remote consultation platform transmits the consultation opinions to the case management port, which organizes the consultation opinions into documents and monitors the patients according to the documents; Custody management includes adjustment of treatment plan and enhanced monitoring measures.

9. A method for intelligent case management of breast cancer patients throughout the entire process, as used in the intelligent case management system for breast cancer patients throughout the entire process as claimed in claim 8, characterized in that: include: S1: The patient first registers on the case management portal on the mobile terminal and enters personal health data after registration; S2: The case management portal uses the patient’s personal health data to assess and diagnose the condition; S3: The case management port formulates a treatment plan for the patient based on the condition assessment and diagnosis results; S4: The patient opens the case management port on the mobile terminal to view and execute the treatment plan; S5: The patient promptly records the feedback information during the treatment process, and the case management port generates rehabilitation suggestions based on the feedback information; S6: The case management port transmits the rehabilitation suggestions and feedback records to the remote consultation platform for expert consultation. The remote consultation platform then transmits the consultation records to the case management port. The case management port monitors the consultation records and generates results.

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