Intelligent follow-up management and drug use monitoring system and method

Abstract

The invention relates to an intelligent follow-up management and medicine use monitoring system and method, and the system comprises a remote patient monitoring module which collects the physiological data of a patient, receives the voice input of the patient and an uploaded medical detection image, and carries out the processing and analysis of the image, so as to obtain the monitoring result of the physiological state and health state of the patient. The information is transmitted to the patient user side and the doctor user side; the adverse drug reaction monitoring module is used for receiving drug information input by the patient user side, identifying whether a user has adverse drug reaction or not in combination with a physiological state monitoring result of the patient, and transmitting an identification result to the patient user side and the doctor user side; the intelligent medicine taking module is used for storing medicines, receiving a medicine taking scheme input by the doctor user side and generating corresponding medicine taking reminding information for different medicines. Compared with the prior art, the system can realize comprehensive, real-time and intelligent management of patients seeing a doctor at different places, effectively improve the treatment efficiency and treatment effect, and improve the treatment compliance of the patients seeing the doctor at different places.

Description

Technical Field

[0001] The present invention relates to the technical field of medical big data processing, and in particular to an intelligent follow-up management and drug use monitoring system and method. Background Art

[0002] Follow-up refers to a method of observation by hospitals to regularly understand patients and guide their recovery through communication or other visits to patients who have been or are currently being treated in the hospital. Follow-up is not only conducive to monitoring the progression of the disease, but also provides data resources for improving medical standards. At present, with the development of medical informatization, intelligent follow-up systems have gradually become an important tool in the medical field. Intelligent follow-up systems can automatically collect and analyze patients' follow-up information and provide doctors with real-time disease monitoring and risk assessment results. However, in the prior art, remote monitoring of patients seeking medical treatment in other places usually relies on independent medical devices or applications that can collect patients' physiological data and transmit it to doctors. However, these systems often lack data security guarantees, and their data analysis capabilities are limited, making it difficult to identify changes in the disease in advance. In addition, communication between doctors and patients usually relies on traditional methods such as telephone and email, which are inefficient and not timely enough.

[0003] In terms of drug use monitoring, traditional methods mainly rely on doctors' observation of patients and patients' self-reporting to identify adverse drug reactions. This method often has problems of underreporting and delayed treatment, because patients may not be able to accurately identify or report adverse reactions in a timely manner, and doctors may miss key information due to busy work; on the other hand, existing timer reminder devices or medicine boxes usually only have simple reminder functions, lacking personalized settings and intelligent management capabilities. Patients need to manually set reminder times according to their medication plans. Once the plan changes, they need to readjust the settings, which is cumbersome and error-prone.

[0004] In summary, the current follow-up management and drug use monitoring for patients mostly exist as independent systems or products, which makes it difficult for patients to receive comprehensive condition monitoring and management after discharge. Doctors are also unable to obtain comprehensive patient data in a timely manner to make subsequent accurate diagnosis and treatment decisions, which is not conducive to improving treatment efficiency and effectiveness. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide an intelligent follow-up management and drug use monitoring system and method. By integrating remote patient monitoring, adverse drug reaction monitoring, and timed reminders and medication assistance functions, a comprehensive follow-up management and drug use monitoring solution is constructed to effectively improve treatment efficiency and treatment effects.

[0006] The object of the present invention can be achieved by the following technical solutions: an intelligent follow-up management and drug use monitoring system, comprising a patient user end, a doctor user end, a remote patient monitoring module, a drug adverse reaction monitoring module and an intelligent medication module, wherein the remote patient monitoring module is used to collect patient physiological data and perform processing and analysis to obtain patient physiological state monitoring results, and transmit the results to the patient user end and the doctor user end;

[0007] The remote patient monitoring module is used to receive patient voice input data and the body medical detection image data uploaded by the patient, obtain the patient health status monitoring results through voice recognition and image recognition, and transmit them to the patient user terminal and the doctor user terminal;

[0008] The adverse drug reaction monitoring module is used to receive drug information input by the patient user end, and identify whether the user has an adverse drug reaction in combination with the patient's physiological state monitoring results, and transmit the identification result to the patient user end and the doctor user end;

[0009] The intelligent medication module is used to store medications, receive medication plans input by a doctor user, and generate corresponding medication reminder information for different medications.

[0010] Furthermore, the remote patient monitoring module includes a physiological data collector, a voice recognition unit, an image recognition unit, a wireless communication unit and a cloud server, wherein the physiological data collector is used to collect the patient's physiological data in real time;

[0011] The speech recognition unit is used to receive the patient's speech input and convert it into text information;

[0012] The image recognition unit is used to extract text information from local medical examination images uploaded by patients;

[0013] The wireless communication unit is used to transmit the collected physiological data, the text information output by the voice recognition unit and the image recognition unit to the cloud server;

[0014] The cloud server processes and analyzes the uploaded physiological data and text information, determines the patient's physiological status monitoring results and health status monitoring results, and transmits them to the patient user end and the doctor user end.

[0015] Furthermore, the physiological data collector includes a heart rate monitor, a blood pressure monitor, a blood glucose meter, and an electroencephalogram monitor, which are used to correspondingly collect different physiological data of the patient, including heart rate, blood pressure, body temperature, respiratory rate, and blood oxygen saturation.

[0016] Furthermore, the cloud server is provided with a preprocessing unit, a first feature extraction unit, a model prediction unit, a database and a comparison unit, and the preprocessing unit is used to clean, standardize and normalize the collected physiological data;

[0017] The first feature extraction unit is used to extract physiological state feature values ​​from the preprocessed physiological data;

[0018] The model prediction unit is preset with a trained physiological state prediction model, the extracted physiological state feature value is input into the physiological state prediction model, and the user's physiological state monitoring result is obtained as an output;

[0019] The database is used to store historical medical data corresponding to different patient users;

[0020] The comparison unit is used to compare the text information with the historical medical data to obtain the user health status monitoring result.

[0021] Further, the adverse drug reaction monitoring module includes a second feature extraction unit and a model recognition unit, wherein the second feature extraction unit is used to extract drug feature values ​​from drug information;

[0022] The model recognition unit is preset with a trained adverse reaction recognition model, and the drug characteristic value and the patient's physiological state monitoring result are input into the adverse reaction recognition model, and the drug adverse reaction recognition result is output.

[0023] Furthermore, the intelligent medication module includes a medicine box body, a control unit, and a reminder unit, and the medicine box body is provided with a plurality of medicine slots for storing different medicines;

[0024] The control unit generates medication reminder information according to the medication regimen and transmits it to the reminder unit;

[0025] The reminder unit uses sound, light, vibration and message push to display medication reminder information to the patient user.

[0026] An intelligent follow-up management and drug use monitoring method comprises the following steps:

[0027] S1. Collect physiological data and corresponding physiological state data samples, and construct a physiological state prediction model through data preprocessing, feature extraction and model training;

[0028] Collect drug information and corresponding adverse reactions and physiological status data samples, and construct an adverse reaction recognition model through feature extraction and model training;

[0029] S2, collecting the current physiological data of the patient user in real time, performing data preprocessing and feature extraction on the current physiological data to obtain the current physiological characteristic value, inputting the current physiological characteristic value into the physiological state prediction model, and outputting the physiological state monitoring result of the patient user;

[0030] Receive the patient's voice input data and uploaded medical test image data in real time, obtain the corresponding text information through voice recognition and image recognition, analyze and compare the text information, and output the patient's health status monitoring results;

[0031] S3, obtaining the current drug information input by the patient user, performing feature extraction on the current drug information to obtain the current drug feature value, inputting the current drug feature value and the physiological state monitoring result into the adverse reaction recognition model, and outputting the adverse reaction recognition result;

[0032] S4. Obtain the medication regimen input by the doctor user, combine it with the current date and time, generate corresponding medication reminder information and transmit it to the patient user.

[0033] Furthermore, the physiological state prediction model in step S1 specifically uses statistical and machine learning methods to perform data modeling on the extracted physiological state feature values ​​to predict the physiological state results;

[0034] The adverse reaction identification model in step S1 specifically uses a machine learning method to perform data modeling on the extracted drug feature values ​​and corresponding physiological states, so as to identify whether an adverse reaction occurs or the possibility of an adverse reaction.

[0035] Furthermore, the medication regimen in step S4 includes the drug name, medication time, dosage and frequency.

[0036] Furthermore, the process of generating corresponding medication reminder information in step S4 includes:

[0037] According to the medication time and frequency corresponding to each drug name, the reminder date and reminder time sequence are sorted;

[0038] It is determined whether the current date and current time can match the values ​​in the reminder date and reminder time sequence. If they match, the corresponding drug name and dosage information are transmitted to the patient user.

[0039] Compared with the prior art, the present invention has the following advantages:

[0040] The present invention designs a patient user end, a doctor user end, a remote patient monitoring module, a drug adverse reaction monitoring module and an intelligent medication module. The remote patient monitoring module is used to collect patient physiological data and process and analyze it to obtain the patient's physiological state monitoring results; it also receives the patient's voice input data and the body medical detection image data uploaded by the patient, obtains the patient's health state monitoring results through voice recognition and image recognition, and transmits the physiological state monitoring results and health state monitoring results to the patient user end and the doctor user end; the drug adverse reaction monitoring module is used to receive the drug information input by the patient user end, and combined with the patient's physiological state monitoring results, it is identified whether the user has an adverse drug reaction, and the identification result is transmitted to the patient user end and the doctor user end; the intelligent medication module is used to store drugs, and the medication plan input by the doctor user end is received, and corresponding medication reminder information is generated for different drugs. In this way, remote patient monitoring, intelligent drug adverse reaction monitoring, and timed reminders are integrated with medication assistance into the same system, so that patients who visit other places can obtain comprehensive follow-up management after discharge, ensuring that both patient users and doctor users can timely and accurately know the patient's current relevant data, effectively improving treatment efficiency and treatment effect.

[0041] The present invention sets a physiological data collector, a wireless communication unit and a cloud server in a remote patient monitoring module. The wireless communication unit transmits the physiological data collected by the physiological data collector and the text information extracted from the user's voice input data and the medical detection image data to the cloud server. The cloud server is provided with a preprocessing unit, a first feature extraction unit, a model prediction unit, a database and a comparison unit. On the one hand, the preprocessing unit is used to clean, standardize and normalize the collected physiological data; the first feature extraction unit is used to extract physiological state characteristic values ​​from the preprocessed physiological data; and a trained physiological state prediction model is preset in the model prediction unit. The extracted physiological state characteristic values ​​are input into the physiological state prediction model to output the user's physiological state monitoring results. On the other hand, the comparison unit is used to compare the text information with the patient's historical medical data stored in the database to obtain the user's health status monitoring results, which can not only ensure the security and privacy of the patient's user data, but also facilitate doctors to timely understand the patient's current physiological state and health status, thereby facilitating subsequent treatment decisions.

[0042] The present invention sets a second feature extraction unit and a model recognition unit in the adverse drug reaction monitoring module, and the second feature extraction unit extracts the drug feature value from the drug information; the model recognition unit is preset with a trained adverse reaction recognition model, and the drug feature value and the patient's physiological state monitoring result are input into the adverse reaction recognition model, and the adverse drug reaction recognition result can be output, which is conducive to doctors to adjust the drug treatment plan in time, avoid the occurrence or deterioration of adverse reactions, and improve the safety of medication.

[0043] The present invention sets a medicine box body, a control unit, and a reminder unit in the intelligent medication module. The medicine box body is provided with a plurality of medicine slots, which can store different medicines. The control unit generates medication reminder information according to the medication plan and transmits it to the reminder unit, and then the reminder unit displays the medication reminder information to the patient user by using sound, light, vibration and message push. Among them, the control unit first sorts the reminder date and reminder time sequence according to the medication time and frequency corresponding to each drug name; then determines whether the current date and current time can match the values ​​in the reminder date and reminder time sequence. If they match, the corresponding drug name and dosage information are transmitted to the patient user. In this way, the reminder time can be set according to the patient's medication plan and living habits to ensure that the patient takes the medicine on time, providing patients with more comprehensive and personalized medical services. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the system structure of the present invention;

[0045] Figure 2 It is a schematic diagram of the method flow of the present invention;

[0046] Explanation of the marks in the figure: 1. Patient user end, 2. Doctor user end, 3. Remote patient monitoring module, 4. Adverse drug reaction monitoring module, 5. Intelligent medication module. DETAILED DESCRIPTION

[0047] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] Example

[0049] like Figure 1 As shown, an intelligent follow-up management and drug use monitoring system includes a patient user terminal 1, a doctor user terminal 2, a remote patient monitoring module 3, a drug adverse reaction monitoring module 4 and an intelligent medication module 5, wherein the remote patient monitoring module 3 is used to collect patient physiological data and perform processing and analysis to obtain patient physiological state monitoring results, and transmit them to the patient user terminal 1 and the doctor user terminal 2;

[0050] The remote patient monitoring module 3 is used to receive the patient's voice input data and the body medical detection image data uploaded by the patient, obtain the patient's health status monitoring results through voice recognition and image recognition, and transmit them to the patient user terminal 1 and the doctor user terminal 2;

[0051] The remote patient monitoring module 3 includes a physiological data collector, a voice recognition unit, an image recognition unit, a wireless communication unit and a cloud server. The physiological data collector is used to collect the patient's physiological data in real time. In practical applications, the physiological data collector includes a heart rate monitor, a blood pressure monitor, a blood glucose meter, an electroencephalogram meter, etc., which are used to collect different physiological data of the patient, including heart rate, blood pressure, body temperature, respiratory rate, blood oxygen saturation, etc.;

[0052] The speech recognition unit is used to receive the patient's speech input and convert it into text information;

[0053] The image recognition unit is used to extract text information from local medical examination images uploaded by patients;

[0054] The wireless communication unit is used to transmit the collected physiological data, the text information output by the voice recognition unit and the image recognition unit to the cloud server;

[0055] The cloud server processes and analyzes the uploaded physiological data and text information, determines the patient's physiological state monitoring results and health state monitoring results, and transmits them to the patient user end and the doctor user end. The cloud server is provided with a preprocessing unit, a first feature extraction unit, a model prediction unit, a database and a comparison unit. The preprocessing unit is used to clean, standardize and normalize the collected physiological data;

[0056] The first feature extraction unit is used to extract physiological state feature values ​​from the preprocessed physiological data;

[0057] The model prediction unit is preset with a trained physiological state prediction model, and the extracted physiological state feature value is input into the physiological state prediction model, and the user's physiological state monitoring result is output;

[0058] The database is used to store historical medical data corresponding to different patient users;

[0059] The comparison unit is used to compare the text information with the historical medical data to obtain the user's health status monitoring results.

[0060] The remote patient monitoring module 3 not only has efficient voice recognition functions, which can accurately interpret patients' self-reported symptoms and discomfort, but also supports patients to take photos and upload various medical examination reports. Through advanced image processing and data analysis technology, the module can automatically extract and interpret key information from the examination results.

[0061] The adverse drug reaction monitoring module 4 is used to receive the drug information input by the patient user terminal 1, and identify whether the user has an adverse drug reaction in combination with the patient's physiological state monitoring result, and transmit the identification result to the patient user terminal 1 and the doctor user terminal 2;

[0062] The drug adverse reaction monitoring module 4 includes a second feature extraction unit and a model recognition unit, and the second feature extraction unit is used to extract drug feature values ​​from drug information;

[0063] The model recognition unit is preset with a trained adverse reaction recognition model. The drug characteristic values ​​and the patient's physiological state monitoring results are input into the adverse reaction recognition model, and the drug adverse reaction recognition results are output.

[0064] The intelligent medication module 5 is used to store drugs, receive medication plans input by the doctor user terminal 2, and generate corresponding medication reminder information for different drugs. The intelligent medication module 5 includes a medicine box body, a control unit, and a reminder unit. The medicine box body is provided with a plurality of medicine slots for storing different drugs.

[0065] The control unit generates medication reminder information according to the medication regimen and transmits it to the reminder unit;

[0066] The reminder unit uses sound, light, vibration and message push to display medication reminder information to patient users.

[0067] Applying the above system, an intelligent follow-up management and drug use monitoring method is realized, such as Figure 2 As shown, the following steps are included:

[0068] S1. Collect physiological data and corresponding physiological state data samples, and construct a physiological state prediction model through data preprocessing, feature extraction and model training;

[0069] Collect drug information and corresponding adverse reactions and physiological status data samples, and construct an adverse reaction recognition model through feature extraction and model training;

[0070] S2, collecting the current physiological data of the patient user in real time, performing data preprocessing and feature extraction on the current physiological data to obtain the current physiological characteristic value, inputting the current physiological characteristic value into the physiological state prediction model, and outputting the physiological state monitoring result of the patient user;

[0071] Receive the patient's voice input data and uploaded medical test image data in real time, obtain the corresponding text information through voice recognition and image recognition, analyze and compare the text information, and output the patient's health status monitoring results;

[0072] S3, obtaining the current drug information input by the patient user, performing feature extraction on the current drug information to obtain the current drug feature value, inputting the current drug feature value and the physiological state monitoring result into the adverse reaction recognition model, and outputting the adverse reaction recognition result;

[0073] S4. Obtain the medication regimen (including drug name, medication time, dosage and frequency) input by the doctor user, combine it with the current date and time, generate corresponding medication reminder information and transmit it to the patient user.

[0074] In step S1, the physiological state prediction model can use statistical and machine learning methods to perform data modeling on the extracted physiological state feature values ​​to predict the physiological state results;

[0075] The adverse reaction identification model can use machine learning methods to perform data modeling on the extracted drug feature values ​​and corresponding physiological states to identify whether an adverse reaction occurs or the possibility of an adverse reaction.

[0076] In step S4, the process of generating corresponding medication reminder information includes:

[0077] According to the medication time and frequency corresponding to each drug name, the reminder date and reminder time sequence are sorted;

[0078] It is determined whether the current date and current time can match the values ​​in the reminder date and reminder time sequence. If they match, the corresponding drug name and dosage information are transmitted to the patient user.

[0079] This embodiment applies the above solution, and the main process includes:

[0080] 1. Hardware Preparation

[0081] Prepare hardware equipment such as physiological data collectors (such as heart rate monitors, blood pressure monitors, blood glucose meters, and electroencephalogram monitors), smart medicine boxes (including reminder units, control buttons, control components, etc.), and wireless communication modules (such as Bluetooth, Wi-Fi, etc.). Assemble the smart medicine box and ensure that all components are connected correctly and firmly.

[0082] The physiological data collector is connected to the wireless communication module via wired or wireless means, and transmits the collected patient data to the cloud server for analysis. By monitoring the patient's physiological data in real time, it automatically sends alarms to the doctor user end and the patient user end when abnormalities occur.

[0083] The smart medicine box includes the medicine box body, reminder components (including buzzer, LED indicator, etc.), control buttons, controller, and wireless communication module. There are multiple medicine slots in the medicine box body, and each medicine slot is connected to a corresponding reminder component; the control button is used to set the reminder time; the controller is responsible for processing and generating reminder logic according to the medication plan, and synchronizing data with the remote server through the wireless communication module. In this way, the reminder time can be personalized according to the patient's medication plan and living habits to ensure that the patient takes the medicine on time.

[0084] 2. Software Development

[0085] 2.1. Develop software for remote patient monitoring modules to realize the functions of collecting, transmitting and analyzing physiological data as well as the recognition and analysis functions for voice data and image data.

[0086] Specifically include:

[0087] 2.1.1. Data collection: Use sensor technology (such as heart rate monitors, blood pressure monitors, electroencephalogram monitors, etc.) to record patients' physiological parameters in real time and accurately, such as heart rate, blood pressure, body temperature, respiratory rate, blood oxygen saturation, etc. These devices can be integrated into the remote patient monitoring module to ensure the continuity and real-time nature of the data.

[0088] In addition, patients can actively report their symptom changes or discomfort through voice input, and the system converts the patient's voice into text information through voice recognition technology for further analysis and recording. In terms of technical implementation, natural language processing (NLP) technology is used to understand the patient's description and match it with a known medical symptom database to identify possible health problems.

[0089] Monitoring and analysis: Compare the patient's voice input with historical data, analyze the changing trends of symptoms, and provide doctors with dynamic information on the patient's health status.

[0090] Patients can also upload the results of blood tests and other tests conducted at local medical institutions to the system in the form of photos, and the system will automatically identify and analyze the test results through image recognition technology. In terms of technical implementation, optical character recognition (OCR) technology can be used to extract text information from photos and interpret the test results in combination with the medical knowledge base.

[0091] Monitoring and analysis: Compare the identified test results with the patient's medical history and physiological parameters, analyze the patient's health status, and report any abnormalities or changes that require attention to the doctor in a timely manner.

[0092] 2.1.2 Data preprocessing: Clean the collected raw physiological data, including removing noise, outliers and duplicate data, to ensure the quality and consistency of the data. In addition, the data needs to be standardized and normalized to eliminate the dimensional differences between different physiological parameters.

[0093] 2.1.3. Feature extraction: Extract meaningful features from preprocessed physiological data, such as RR interval, P wave, QRS complex in electrocardiogram data, or energy distribution in different frequency bands in brain wave data. These features can reflect the patient's physiological state.

[0094] 2.1.4. Data modeling: Use statistical and machine learning methods (such as linear regression, logistic regression, support vector machine, neural network, etc.) to model the extracted features to reveal the relationship between physiological parameters and predict future physiological states.

[0095] 2.1.5. Interpretation of results: Combine specific physiological knowledge and clinical experience to interpret the modeling results. Through data visualization technology (such as line graphs, bar graphs, scatter plots, etc.), complex physiological data and analysis results are intuitively displayed to provide decision support for doctors.

[0096] Analysis results:

[0097] The final analysis results may include the patient's physiological status assessment, potential health risk warning, disease diagnosis suggestions, etc. These results help doctors understand the patient's health status in a timely manner and develop personalized treatment plans.

[0098] 2.2. Develop software for the intelligent adverse drug reaction monitoring module, including a drug information input interface, adverse reaction identification algorithm, and alarm system.

[0099] Specifically include:

[0100] 2.2.1. Data input: The patient's drug use information is input into the system through the drug information input interface. This information includes drug name, dosage, medication time, etc.

[0101] 2.2.2. Feature extraction: Extract key features from drug usage information, such as drug type, mechanism of action, common adverse reactions, etc.

[0102] 2.2.3. Algorithmic modeling: Use machine learning algorithms (such as decision trees, random forests, neural networks, etc.) to model the extracted features to identify patterns of adverse drug reactions.

[0103] 2.2.4. Real-time monitoring: Combine the patient's physiological data (such as heart rate, blood pressure, body temperature, etc.) and drug use information to monitor in real time whether the patient has adverse reactions.

[0104] 2.2.5. Alarm system: Once an adverse reaction is identified, the system immediately triggers an alarm to notify doctors or nursing staff to take timely measures.

[0105] Analysis results:

[0106] The final analysis result may be a binary classification result (i.e., whether an adverse reaction occurs) or a probability value (indicating the possibility of an adverse reaction). These results help doctors adjust drug treatment plans in a timely manner to avoid the occurrence or deterioration of adverse reactions.

[0107] 2.3. Develop control software for the smart medicine box to realize the setting of reminder time, processing of reminder logic and data synchronization with the remote server.

[0108] In terms of reminder logic processing, in addition to the medication reminder design, this embodiment further designs a reminder for returning to the hospital for a review and a reminder for the next medication cycle, specifically including:

[0109] 2.3.1. Reminder type settings

[0110] Medication reminder: Users can set specific medication time, dosage and frequency through the smart medicine box's control software or related applications.

[0111] Return to hospital reminder: According to the patient's treatment plan or doctor's orders, set the specific date and time for returning to the hospital for reexamination or treatment.

[0112] Medication reminder for the next visit cycle: Based on the patient's medication cycle and visit plan, set the reminder time before the start of the next medication cycle.

[0113] 2.3.2 Logical judgment and triggering

[0114] Time matching: The software checks in real time whether the current time matches the set reminder time.

[0115] Conditional judgment: For medication reminders, determine whether it is time to take the medication; for reminders to return to the hospital and reminders to take medication for the next visit cycle, determine whether the current date has reached or is close to the set date.

[0116] Trigger reminders: When the reminder conditions are met, the software triggers reminders through smart pill boxes, mobile apps or other connected devices in the form of audio, visual, vibration or push notifications.

[0117] 2.3.3. Customization of reminder content

[0118] Medication reminder content: including detailed information such as drug name, dosage, and medication time.

[0119] Contents of the return reminder: including the date, time, purpose (such as follow-up examination, treatment, etc.) of returning to the hospital and the documents or items that need to be brought (such as examination reports, medical records, etc.).

[0120] Contents of the next medication cycle reminder during the visit: including the start date, time, drug name, dosage and possible medication precautions for the next medication cycle.

[0121] 2.3.4 Data synchronization and recording

[0122] Synchronize with remote server: The control software or related applications of the smart medicine box synchronize data with the remote server to ensure that information such as reminder settings, medication records, and return records are updated in real time and can be viewed by doctors or caregivers.

[0123] Medication and visit records: The software automatically records the patient's medication and visit information, including medication time, dosage, whether the medication is taken on time, visit date and time, etc., to provide data support for the doctor's follow-up treatment.

[0124] 2.3.5 Flexible adjustment and optimization

[0125] User feedback: Allow patients or family members to provide feedback through the software, such as adjusting reminder times, changing medication plans, etc.

[0126] Doctor adjustment: Doctors can adjust reminder settings and medication plans through the remote server or software interface based on the patient's actual situation and treatment progress.

[0127] Intelligent optimization: The software can automatically optimize reminder logic and medication plans based on the patient's medication habits and visit records, thereby improving the accuracy and effectiveness of reminders.

[0128] Through this design, the reminder logic not only covers medication reminders, but also extends to reminders for returning to the hospital and reminders for the next medication cycle, providing patients with more comprehensive and personalized medical services. At the same time, data synchronization and recording functions as well as flexible adjustment and optimization mechanisms ensure the accuracy and practicality of the reminder logic.

[0129] Through the software design of the above three aspects, they can be logically interconnected and work together to form the core functions of this solution. Through the close cooperation of data collection, analysis, adverse reaction identification and reminder logic processing, the system can achieve comprehensive monitoring and personalized reminders for patients, providing strong support for patients' health management and disease treatment.

[0130] 3. System Integration and Testing

[0131] In the test application, the specific steps include:

[0132] 1. Patient registration and login:

[0133] Patients register through a mobile device or computer application and log in to the intelligent integrated patient management and medication monitoring system.

[0134] 2. Physiological data monitoring:

[0135] The patient wears or carries a physiological data collector (such as a heart rate monitor, blood pressure monitor, etc.), which collects the patient's physiological data in real time and transmits it to the cloud server for analysis through a wireless communication module.

[0136] Patients and doctors can view real-time monitoring data and historical data through the application to understand the patient's health status.

[0137] 3. Medication management:

[0138] Patients enter their medication plan into the application, including information such as medication name, medication schedule, and medication dosage.

[0139] The smart medicine box automatically sets the reminder time according to the patient's medication plan, and emits sound and light prompts when the reminder time arrives to remind the patient to take the medicine.

[0140] After taking the medicine, the patient can confirm the medication status through the application, and the system will record the patient's medication history.

[0141] 4. Adverse reaction monitoring:

[0142] Patients enter information about the medications they are taking into the application, and the system will automatically monitor and identify possible adverse reactions.

[0143] Once an adverse reaction is detected, the system will immediately send an alarm to the doctor and the patient, reminding the doctor to deal with it in a timely manner and adjust the medication plan.

[0144] 5. Doctor management:

[0145] Doctors can use the application to view patients' real-time monitoring data, medication history, and adverse reactions, and gain a comprehensive understanding of their health status.

[0146] Doctors can adjust medication regimens or give treatment recommendations based on the patient's actual situation and communicate with the patient through the application.

[0147] 4. System Deployment

[0148] Finally, the entire system (including hardware and software) is deployed to achieve the following operation process:

[0149] 1. Data collection and preprocessing:

[0150] The system first collects the patient's physiological data through the sensor equipment integrated in the remote patient monitoring module, and pre-processes the data (such as denoising, standardization, etc.) to ensure the accuracy and consistency of the data.

[0151] 2. Physiological data analysis:

[0152] The preprocessed physiological data is input into the data analysis module. Through feature extraction, data modeling and other steps, the system can evaluate the patient's physiological state, identify potential health risks, and provide decision support for subsequent adverse reaction identification and reminder logic processing.

[0153] 3. Identification of adverse reactions:

[0154] At the same time, the system combines the patient's drug use information (such as drug name, dosage, medication time, etc.) and uses the adverse reaction recognition algorithm to monitor in real time whether the patient has adverse drug reactions. Once an adverse reaction is identified, the system immediately triggers an alarm and generates corresponding treatment suggestions.

[0155] 4. Reminder logic processing:

[0156] According to the results of physiological data analysis, adverse reaction identification results and other related factors (such as medication plan, visit plan, etc.), the reminder logic processing module generates personalized reminder information. These reminders include medication reminders, return to hospital reminders, and reminders for the next medication visit, and are notified to patients or medical staff in a variety of ways through smart medicine boxes, mobile phone applications or other connected devices.

[0157] 5. Feedback and Adjustment:

[0158] After receiving the reminder information, patients or medical staff can take corresponding measures (such as taking medication on time, returning to the hospital for reexamination, etc.). At the same time, the system can also adjust and optimize the reminder logic, medication plan, etc. according to the patient's feedback and actual situation to ensure the accuracy and effectiveness of monitoring and reminders.

[0159] To sum up, under the traditional medical management model, patients often face management difficulties after discharge, difficulty in timely detection of worsening of their condition, underreporting or delayed treatment of adverse drug reactions, and low medication compliance. These problems seriously affect the treatment effect and quality of life of patients.

[0160] In order to solve these problems, this solution combines advanced technologies such as remote monitoring technology, deep learning and knowledge graph technology, and intelligent control technology to design a comprehensive system that integrates remote patient monitoring, intelligent adverse drug reaction monitoring, and timed reminders and medication assistance. By real-time monitoring of patient physiological data, abnormalities can be detected and alarms can be sent in a timely manner; through intelligent recognition algorithms, adverse drug reactions can be automatically identified and reported; through smart medicine boxes, patients can be ensured to take medicine on time and improve treatment compliance.

[0161] This solution has the following advantages:

[0162] 1. Comprehensive monitoring and timely warning: Through the remote patient monitoring module, the patient's physiological data can be collected and analyzed in real time. Once an abnormality is found, an alarm will be immediately sent to the doctor and the patient to ensure that the condition is treated in a timely manner.

[0163] 2. Intelligent identification and risk reduction: The adverse drug reaction monitoring module uses advanced algorithms to automatically identify and report adverse drug reactions, reduce underreporting and delayed treatment, and improve medication safety.

[0164] 3. Personalized reminders to improve compliance: Timed reminders and medication assistance devices (smart medicine boxes) can personalize reminder times based on the patient's medication plan and lifestyle habits to ensure that patients take medication on time and improve treatment compliance.

[0165] 4. Data integration and decision support: This system integrates multiple functions such as patient monitoring, adverse drug reaction monitoring and medication assistance, realizes centralized management and analysis of data, provides doctors with more comprehensive patient information, and helps to make more accurate diagnosis and treatment decisions.

[0166] 5. Easy to expand and highly compatible: The system design is open and easy to integrate and expand with other medical management systems or equipment to meet the needs of different medical institutions and patients.

[0167] 6. User-friendly and easy to operate: The actual operation of the system is simple and easy to understand, and both patients and doctors can quickly get started using it.

[0168] The principle of this solution is to use modern information technology and intelligent algorithms to achieve comprehensive, real-time, and intelligent management of patients, thereby solving the problems existing in the traditional medical management model, improving medical efficiency, reducing medical risks, and improving the quality of life of patients. This solution not only conforms to the development trend of medical technology, but also meets the actual clinical needs, and has broad application prospects and social value.

[0169] This solution integrates the patient user end, doctor user end, remote patient monitoring module, adverse drug reaction monitoring module and intelligent medication module. The remote patient monitoring module not only has efficient voice recognition function, can accurately analyze the patient's self-reported symptoms and discomfort, but also supports patients to take photos and upload various medical examination reports. Through advanced image processing and data analysis technology, the module can automatically extract and interpret key information in the examination results, and at the same time, combine the patient's real-time or regular physiological parameters (such as blood pressure, heart rate, blood sugar, etc.) to collect and analyze, and generate a comprehensive patient physiological status monitoring report. These accurate data and analysis results will be synchronized to the patient user end and the doctor user end in real time, providing timely and accurate health information support for both parties. The adverse drug reaction monitoring module receives and integrates the drug use information input by the patient user end, combines the physiological status data provided by the remote patient monitoring module, and uses intelligent algorithms to identify and evaluate whether the patient has adverse drug reactions. Once a potential risk or abnormality is found, the module will quickly generate an early warning report and push it to the patient and doctor in real time to ensure that necessary measures can be taken in time to ensure patient safety. The intelligent medication module integrates drug storage and intelligent management functions. It can not only automatically distribute and manage drugs for patients according to the personalized medication plan input by the doctor user, but also generate scientific medication reminders and follow-up notices according to the characteristics of the drug and the needs of the patient. Compared with the existing technology, the present invention can achieve all-round, real-time and intelligent management of patients who seek medical treatment in other places, which not only significantly improves the management efficiency and treatment effect of patients who seek medical treatment in other places, but also greatly enhances the treatment compliance and self-management ability of patients, injecting new vitality into the digital transformation of the medical industry and the development of smart medicine.

Claims

1. An intelligent follow-up management and drug use monitoring system, characterized in that: The system comprises a patient user terminal (1), a doctor user terminal (2), a remote patient monitoring module (3), a drug adverse reaction monitoring module (4) and an intelligent medication module (5), wherein the remote patient monitoring module (3) is used to collect and process the patient's physiological data to obtain the patient's physiological state monitoring results, and transmit the results to the patient user terminal (1) and the doctor user terminal (2); The remote patient monitoring module (3) is used to receive patient voice input data and body medical detection image data uploaded by the patient, obtain the patient health status monitoring results through voice recognition and image recognition, and transmit them to the patient user terminal (1) and the doctor user terminal (2); The adverse drug reaction monitoring module (4) is used to receive drug information input by the patient user terminal (1), and identify whether the user has an adverse drug reaction in combination with the patient's physiological state monitoring result, and transmit the identification result to the patient user terminal (1) and the doctor user terminal (2); The intelligent medication module (5) is used to store medications, receive medication regimens input by the doctor user terminal (2), and generate corresponding medication reminder information for different medications.

2. The intelligent follow-up management and drug use monitoring system according to claim 1, characterized in that: The remote patient monitoring module (3) comprises a physiological data collector, a voice recognition unit, an image recognition unit, a wireless communication unit and a cloud server, wherein the physiological data collector is used to collect the patient's physiological data in real time; The speech recognition unit is used to receive the patient's speech input and convert it into text information; The image recognition unit is used to extract text information from local medical examination images uploaded by patients; The wireless communication unit is used to transmit the collected physiological data, the text information output by the voice recognition unit and the image recognition unit to the cloud server; The cloud server processes and analyzes the uploaded physiological data and text information, determines the patient's physiological status monitoring results and health status monitoring results, and transmits them to the patient user terminal (1) and the doctor user terminal (2).

3. The intelligent follow-up management and drug use monitoring system according to claim 2, characterized in that: The physiological data collector includes a heart rate monitor, a blood pressure monitor, a blood glucose meter, and an electroencephalogram monitor, which are used to correspondingly collect different physiological data of the patient, including heart rate, blood pressure, body temperature, respiratory rate, and blood oxygen saturation.

4. The intelligent follow-up management and drug use monitoring system according to claim 2, characterized in that: The cloud server is provided with a preprocessing unit, a first feature extraction unit, a model prediction unit, a database and a comparison unit, wherein the preprocessing unit is used to clean, standardize and normalize the collected physiological data; The first feature extraction unit is used to extract physiological state feature values ​​from the preprocessed physiological data; The model prediction unit is preset with a trained physiological state prediction model, the extracted physiological state feature value is input into the physiological state prediction model, and the user's physiological state monitoring result is output; The database is used to store historical medical data corresponding to different patient users; The comparison unit is used to compare the text information with the historical medical data to obtain the user health status monitoring result.

5. The intelligent follow-up management and drug use monitoring system according to claim 1, characterized in that: The adverse drug reaction monitoring module (4) comprises a second feature extraction unit and a model recognition unit, wherein the second feature extraction unit is used to extract a drug feature value from drug information; The model recognition unit is preset with a trained adverse reaction recognition model, and the drug characteristic value and the patient's physiological state monitoring result are input into the adverse reaction recognition model, and the drug adverse reaction recognition result is output.

6. The intelligent follow-up management and drug use monitoring system according to claim 1, characterized in that: The intelligent medication module (5) comprises a medicine box body, a control unit, and a reminder unit. The medicine box body is provided with a plurality of medicine slots for storing different medicines. The control unit generates medication reminder information according to the medication regimen and transmits it to the reminder unit; The reminder unit displays medication reminder information to the patient user by means of sound, light, vibration and message push.

7. An intelligent follow-up management and drug use monitoring method, applied to an intelligent follow-up management and drug use monitoring system as claimed in claim 1, characterized in that: The following steps are involved: S1. Collect physiological data and corresponding physiological state data samples, and construct a physiological state prediction model through data preprocessing, feature extraction and model training; Collect drug information and corresponding adverse reactions and physiological status data samples, and construct an adverse reaction recognition model through feature extraction and model training; S2, collecting the current physiological data of the patient user in real time, performing data preprocessing and feature extraction on the current physiological data to obtain the current physiological characteristic value, inputting the current physiological characteristic value into the physiological state prediction model, and outputting the physiological state monitoring result of the patient user; Receive the patient's voice input data and uploaded medical test image data in real time, obtain the corresponding text information through voice recognition and image recognition, analyze and compare the text information, and output the patient's health status monitoring results; S3, obtaining the current drug information input by the patient user, performing feature extraction on the current drug information to obtain the current drug feature value, inputting the current drug feature value and the physiological state monitoring result into the adverse reaction recognition model, and outputting the adverse reaction recognition result; S4. Obtain the medication regimen input by the doctor user, combine it with the current date and time, generate corresponding medication reminder information and transmit it to the patient user.

8. The intelligent follow-up management and drug use monitoring method according to claim 7, characterized in that: The physiological state prediction model in step S1 specifically uses statistical and machine learning methods to perform data modeling on the extracted physiological state feature values ​​to predict the physiological state results; The adverse reaction identification model in step S1 specifically uses a machine learning method to perform data modeling on the extracted drug feature values ​​and corresponding physiological states, so as to identify whether an adverse reaction occurs or the possibility of an adverse reaction.

9. The intelligent follow-up management and drug use monitoring method according to claim 7, characterized in that: The medication regimen in step S4 includes the drug name, medication time, dosage and frequency.

10. The intelligent follow-up management and drug use monitoring method according to claim 9, characterized in that: The process of generating corresponding medication reminder information in step S4 includes: According to the medication time and frequency corresponding to each drug name, the reminder date and reminder time sequence are sorted; It is determined whether the current date and current time can match the values ​​in the reminder date and reminder time sequence. If they match, the corresponding drug name and dosage information are transmitted to the patient user.

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