Disclosure of Invention
The embodiment of the application aims to overcome the limitations of the existing medical observation and management system by providing a medical observation management system which integrates the acquisition of illness state, data monitoring, prediction and personalized intervention.
In order to achieve the above object, an embodiment of the present application provides a medical observation management system, which includes a disease condition acquisition module, a monitoring module, a server and a management platform, wherein,
the illness state acquisition module is used for acquiring current illness state data of a patient and uploading the current illness state data to the server;
the monitoring module is used for monitoring physiological data and behavior data of a patient and uploading the physiological data and the behavior data to the server;
the server is used for receiving and storing the data uploaded by the illness state acquisition module and the monitoring module, and encrypting and backing up the data;
the management platform is used for displaying data in the server in a visual mode, and further comprises a prediction unit, wherein the prediction unit is used for predicting the disease development condition of a patient in a future appointed period based on the data of the server and generating a medical intervention scheme based on the disease development condition.
In an embodiment, the management platform further includes a monitoring frequency adjustment unit, and the monitoring frequency adjustment unit generates different monitoring strategies according to the patient condition data and sends the monitoring strategies to the monitoring module;
the monitoring module performs monitoring of patient physiological data and behavioral data based on the monitoring policy.
In an embodiment, the monitoring frequency adjustment unit generates the monitoring policy based on the steps of:
determining a disease type of the patient from the condition data;
determining the strength of association of each monitorable physiological data and behavioural data with the disease type;
determining a monitoring frequency interval of each type of physiological data and behavior data according to the association strength;
and generating the monitoring strategy according to the monitoring frequency interval.
In an embodiment, the monitoring frequency adjusting unit retrieves a physiological abnormality and a behavioral abnormality corresponding to the disease type from a physiological abnormality and behavioral abnormality database preset in the server according to the disease type of the patient, and calculates a correlation strength between each monitorable physiological data and behavioral data and the disease type according to a correlation between the physiological abnormality and behavioral abnormality and each monitorable physiological data and behavioral data.
In one embodiment, the monitoring frequency adjustment unit calculates correlations between physiological anomalies and behavioral anomalies and each of the monitorable physiological data and behavioral data based on formulas:
r＝cov(X,Y)/(σXσY)；
wherein cov (X, Y) covariance is the covariance of the corresponding physiological anomaly and the monitorable physiological data, or the covariance of the behavioral anomaly and the monitorable behavioral data; (σxσy) is the standard deviation of physiological anomalies from monitorable physiological data, or the standard deviation of behavioral anomalies from monitorable behavioral data.
In an embodiment, after determining the monitoring frequency interval of each type of physiological data and behavioral data, the step of generating the monitoring policy by the monitoring frequency unit further comprises:
determining the disease development trend of the patient according to the disease condition data;
and determining the current monitoring frequency of each type of physiological data and behavior data according to the disease development trend and the monitoring frequency interval.
In an embodiment, determining the current monitoring frequency of each type of physiological data and behavioral data according to the disease development trend and the monitoring frequency interval includes:
when the illness state of the patient is in a stable or improved trend, selecting a lower monitoring frequency from the monitoring frequency interval as the current monitoring frequency of corresponding physiological data or behavior data;
when the condition of the patient is in worsening or critical trend, a higher monitoring frequency is selected from the monitoring frequency interval as the current monitoring frequency of the corresponding physiological data or behavioral data.
In an embodiment, the frequency monitoring unit determines the monitoring frequency corresponding to different disease development trends based on the following steps:
dividing a preset monitoring frequency interval into three parts according to the size of the frequency value, and defining a lower frequency interval, a medium frequency interval and a higher frequency interval respectively;
calculating the frequency average value of the lower frequency interval and the higher frequency interval;
according to the current disease development trend of the patient, mapping the frequency average value of the lower frequency interval with the stable or improved trend, and mapping the frequency average value of the higher frequency interval with the worsening or critical trend to obtain specific monitoring frequencies corresponding to different disease development trends.
In an embodiment, the physiological data comprises at least one of the following data: heart rate, blood pressure, body temperature, blood oxygen saturation, respiratory rate, aerobic adaptation, blood glucose, and expiratory carbon dioxide;
the behavioral data includes at least one of the following data: cough behavior, runny behavior, headache, sleep behavior, exercise behavior, eating behavior, emotional behavior, daily behavior.
In one embodiment, the prediction unit predicts the condition of the patient in a specified future period based on the trained deep learning neural network.
According to the medical observation management system, the current disease condition data of the patient is acquired through the disease condition acquisition module, the physiological data and the behavior data of the patient are monitored through the monitoring module, the data are managed through the management platform, the disease development condition of the patient in a specified future period is predicted on the basis of the data through the prediction unit, and a medical intervention scheme is generated in a targeted mode. Therefore, compared with the traditional mode of manually taking care of patients, the medical observation management system of the technical scheme of the application has the advantages of comprehensive monitoring, real-time transmission, visual display, disease prediction, medical intervention scheme generation and the like, can provide comprehensive health management support for medical staff, optimizes medical decision, and improves the treatment effect and satisfaction of patients.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In order that the above-described aspects may be better understood, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps other than those listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. And the use of "first," "second," and "third," etc. do not denote any order, and the terms may be construed as names.
The technical scheme of the application provides a medical observation management system 1.
Referring to fig. 1, fig. 1 is an embodiment of a medical observation management system 1 according to the present application, wherein the medical observation management system 1 includes a disease acquisition module 10, a monitoring module 11, a server 12 and a management platform 13.
Specifically, the condition acquisition module 10 is configured to acquire current condition data of a patient, and upload the data to the server 12 for subsequent monitoring, analysis and prediction.
Specifically, the condition acquisition module 10 uses medical devices, mobile applications, or the like to perform data acquisition of the health condition of the patient. The data collected may include, but is not limited to, a description of the condition, a medication, etc. Wherein the symptom description can be carried out by the patient or the medical staff through inputting words, voice or selecting preset options, and the like. The medication condition refers to recording information such as the current medication, dosage, medication frequency, etc. of the patient.
Further, condition acquisition module 10 may upload the acquired data to server 12 via a network connection (e.g., wi-Fi, bluetooth, mobile data, etc.).
Specifically, the monitoring module 11 is configured to monitor physiological data and behavioral data of a patient and upload the physiological data and behavioral data to the server 12.
Specifically, the monitoring module 11 uses wearable smart devices, sensors, medical instruments, etc. to collect physiological data of the patient, which may include, but is not limited to, heart rate, blood pressure, body temperature, blood oxygen saturation, respiratory rate, aerobic compliance, blood glucose, expiratory carbon dioxide, etc.
Further, the monitoring module 11 also uses a camera, a motion sensor, etc. to monitor the behavior of the patient in real time, and the behavior data include, but are not limited to, cough behavior, runny nose behavior, headache, sleep behavior (such as sleep time, sleep quality, etc.), exercise behavior (such as exercise intensity, exercise type, etc.), eating behavior (such as eating content, feeding frequency, etc.), emotion behavior (such as emotion change, emotion condition, etc.), daily behavior (such as cleaning sanitation, etc.).
In addition, as with the condition acquisition module 10, the monitoring module 11 may upload the acquired data to the server 12 via a network connection (e.g., wi-Fi, bluetooth, mobile data, etc.).
Specifically, the server 12 is a data center and a processing center of the medical observation management system 1, and the server 12 is configured to receive and store the data uploaded by the monitoring module 11 of the disease acquisition module 10, and encrypt and backup the data.
Specifically, the server 12 acts as a data center, receiving patient data uploaded from the condition acquisition module 10 and the monitoring module 11. Such data includes patient condition data, physiological data, behavioral data, and the like. The data needs to be processed by the server 12 and stored in a database for later analysis and querying.
Further, due to the sensitive health data related to the patient, the server 12 needs to take strict security measures to ensure the privacy and security of the data. One of the key measures is to encrypt the data to prevent the data from being illegally acquired during the transmission process. For example, data encryption may be performed by symmetric encryption, asymmetric encryption, hash functions, message authentication codes, public key infrastructure, homomorphic encryption, searchable encryption, and the like.
Meanwhile, in order to prevent data loss and secure data reliability, the server 12 needs to perform data backup periodically. The frequency and method of backup need to be determined according to the importance of the system and the data change situation.
In particular, the viewing platform is used to visually present data in the server 12, which provides a user-friendly graphical interface for medical personnel to view, analyze and manage patient condition data.
Specifically, the management platform 13 visually presents patient data from the server 12. Through various forms such as charts, images, instrument panels and the like, medical staff can intuitively know physiological data, illness state trend and behavior change of a patient, and help the medical staff to better know the health state of the patient. At the same time, the management platform 13 also allows healthcare personnel to query and manage patient data. They can query based on patient ID, time frame, specific physiological parameters, etc. to obtain data and detailed information over a specific period of time. Optionally, the management platform 13 may also set alarm and alert functions, and when the physiological data of the patient exceeds a preset threshold or an abnormality occurs, the system may send an alarm or alert to the healthcare staff to take timely action.
Further, the management platform 13 further includes a prediction unit for predicting a patient's condition in a specified period of time in the future based on the data of the server 12, and generating a medical intervention plan based on the predicted condition.
In particular, the prediction unit may utilize data analysis and machine learning algorithms (e.g., neural networks, regression models, time series models, classification models, etc.) to predict the patient's condition in a specified future period. Wherein, the disease progression refers to the change and evolution of the disease or health state of the patient over time. It is worth noting that the prediction of the state of progression of a disease is very important to healthcare workers, as it can help healthcare workers make more accurate diagnostic and therapeutic decisions.
Furthermore, after the prediction unit determines the disease development condition of the patient within the future specified period, a medical intervention scheme can be given in a targeted manner. The medical intervention regimen is a series of personalized medical measures and advice generated based on the predicted condition of the patient's progress. These interventions can be tailored to the specific situation and needs of the patient to maximize the improvement of the patient's health. Medical intervention protocols include, but are not limited to, medication adjustments, diet and nutrition advice, exercise and rehabilitation programs, periodic monitoring and follow-up programs, psychological support, and the like. The medical intervention scheme can be used for making a treatment decision reference for medical staff, thereby being beneficial to improving the life quality and treatment effect of patients.
It can be understood that the medical observation management system 1 of the present application acquires current condition data of a patient through the condition acquisition module 10, monitors physiological data and behavior data of the patient through the monitoring module 11, manages the data through the management platform 13, predicts the condition of the patient in a specified future period of time based on the data through the prediction unit, and generates a medical intervention scheme in a targeted manner. In this way, compared with the traditional mode of manually taking care of patients, the medical observation management system 1 of the technical scheme of the application has the following advantages:
1. and (3) overall monitoring: the medical observation management system 1 of the present application can comprehensively monitor patient condition data, physiological data, and behavioral data. Such comprehensive monitoring can provide healthcare personnel with a more comprehensive understanding of the health of the patient, helping to make more accurate diagnostic and therapeutic decisions.
2. And (3) real-time transmission: the patient condition acquisition module 10 and the monitoring module 11 can upload patient data to the server 12 in real time, so that medical staff can acquire latest patient information in time. Real-time transmission helps to take intervention measures rapidly in the acute phase of the disease, avoiding delay of treatment timing.
3. Visual display: the management platform 13 visually displays the data in the server 12, including charts, images, dashboards, etc., so that the health status and disease trend of the patient can be intuitively known by the medical staff. The visual display mode can improve the readability and comprehensiveness of the data, and is convenient for medical staff to quickly acquire information.
4. Disease prediction and intervention protocol generation: the prediction unit in the management platform 13 predicts the condition based on the data of the server 12 and generates a personalized medical intervention plan. The function enables medical staff to predict the disease state development of a patient in advance, and to take intervention measures in time, so that the disease state is prevented from deteriorating, and the treatment effect is improved.
In summary, the technical scheme of the application has the functions of comprehensive monitoring, real-time transmission, visual display, disease prediction, intervention scheme generation and the like, can provide comprehensive health management support for medical staff, optimizes medical decision, and improves the treatment effect and satisfaction of patients.
In some embodiments, the management platform 13 further comprises a monitoring frequency adjustment unit that generates different monitoring strategies according to patient condition data and sends the monitoring strategies to the monitoring module 11. Further, the monitoring module 11 performs monitoring of the patient physiological data and the behavioral data based on the monitoring strategy.
Specifically, the monitoring strategy refers to the setting of monitoring plans and frequencies, etc. for physiological data and behavioral data of a patient.
In this embodiment, the monitoring frequency adjusting unit may formulate a personalized monitoring policy according to patient condition data. For example, the monitoring frequency adjusting unit may increase the monitoring frequency as necessary to more accurately capture the change of the condition, while the monitoring frequency may be reduced during the stabilization period, avoiding frequent data collection, and improving the operation efficiency of the system.
In this way, the monitoring module 11 can pertinently adjust the monitoring frequency and the monitoring items according to the different conditions and health states of different patients, so that the health states of the patients can be better known. Meanwhile, by adjusting the monitoring policy, the monitoring module 11 can reduce unnecessary data collection, and save storage resources and data processing resources of the server 12. Monitoring is performed only when needed, avoiding the generation and transmission of large amounts of useless data.
Of course, the design of the present application is not limited thereto, and in other embodiments, the monitoring module 11 may also perform monitoring of physiological data and behavioral data of the patient based on a preset monitoring strategy.
In some embodiments, the monitoring frequency adjustment unit generates the monitoring policy based on:
s10, determining the disease type of the patient according to the disease condition data.
Specifically, if the disease type of the patient is recorded in the disease condition data, the monitoring frequency adjusting unit may directly determine the disease type of the patient from the disease condition data of the patient. Otherwise, if the disease condition data does not describe the specific disease type of the patient, the monitoring frequency adjusting unit may analyze the disease condition data of the patient, such as symptom descriptions, physiological parameters, etc., to determine the disease type of the patient.
S20, determining the association strength of each monitorable physiological data and behavior data and the disease type.
Wherein monitorable physiological data and behavioral data refer to information relating to the health status of a patient that can be acquired and recorded in real time by sensors, instruments or other monitoring devices in the medical viewing management system 1. Accordingly, non-monitorable physiological data and behavioral data refer to information relating to the health status of a patient that cannot be acquired and recorded in real-time by sensors, instrumentation or other monitoring devices in the medical viewing management system 1. Such data may be data that cannot be collected and monitored in real time due to technical limitations, equipment inadequacies, or other various reasons. For example, the non-monitorable physiological data includes genetic information, cellular level data, internal organ data, etc., and the non-monitorable behavioral data includes thinking and feeling, personal interaction, emotional expression, etc.
In particular, the monitoring frequency adjustment unit evaluates the degree of correlation between each of the monitorable physiological data and behavioral data and the type of disease in which the patient is located. The correlation strength can be determined based on statistical analysis or machine learning methods to find monitoring indicators closely related to the patient disease type.
S30, determining a monitoring frequency interval of each type of physiological data and behavior data according to the association strength.
Specifically, the monitoring frequency adjusting unit classifies the monitorable physiological data and the behavior data into different types according to the correlation strength determined previously, and sets a corresponding monitoring frequency interval for each type. The monitoring index with higher association degree can set more frequent monitoring, while the index with lower association degree can set more loose monitoring frequency.
S40, generating a monitoring strategy according to the monitoring frequency interval.
Specifically, the monitoring frequency adjusting unit formulates a specific monitoring strategy according to the monitoring frequency interval obtained by the steps. The monitoring strategy may include information such as the monitoring frequency, monitoring time period, etc. of each of the physiological data and the behavioral data. These monitoring strategies will be sent to the monitoring module 11 for performing the actual monitoring tasks on the patient physiological data and behavioral data.
According to the scheme, the monitoring frequency adjusting unit determines the disease type of the patient through analysis of the disease condition data, evaluates the association degree of the physiological data and the behavior data with the disease type, sets the monitoring frequency interval according to the association strength, and finally generates a personalized monitoring strategy.
In some embodiments, the monitoring frequency adjusting unit retrieves physiological abnormalities and behavioral abnormalities corresponding to the disease type from a physiological abnormalities and behavioral abnormalities database preset in the server 12 according to the disease type of the patient, and calculates the correlation strength of each monitorable physiological data and behavioral data with the disease type according to the correlation between the physiological abnormalities and behavioral abnormalities and each monitorable physiological data and behavioral data.
Specifically, based on the above embodiment, the process of monitoring the calculated correlation strength of the frequency adjustment unit is as follows:
1. determining the type of disease in the patient: first, the monitoring frequency adjusting unit determines the disease type of the patient according to the disease condition data of the patient. This can be obtained by analyzing and judging information such as symptom descriptions, physical sign manifestations, medical diagnoses, and the like of the patient.
2. Searching a preset physiological abnormality and behavioral abnormality database: the monitoring frequency adjusting unit retrieves physiological abnormalities and behavioral abnormalities corresponding to the type of disease in which the patient is located from a physiological abnormality and behavioral abnormality database preset in the server 12. The abnormal data are collected and arranged according to clinical experience and medical knowledge in advance, and physiological abnormalities and behavioral abnormalities related to different disease types are marked in a database.
3. Calculating the correlation between physiological data and behavioral data and abnormal data: for each monitorable physiological data and behavior data, the monitoring frequency adjusting unit compares the monitorable physiological data and behavior data with physiological anomalies and behavior anomaly data retrieved from the database, and calculates correlations between the monitorable physiological data and behavior data. The correlation can be calculated by using statistical methods such as correlation coefficients, regression analysis and the like, and pattern matching and classification can also be performed by adopting a machine learning algorithm.
4. Calculating the association strength: through the correlation calculation of the physiological data and the behavioral data and the abnormal data, the monitoring frequency adjusting unit can obtain the correlation strength of each monitorable physiological data and behavioral data and the type of the disease of the patient. The association strength indicates the degree of association between the physiological data and the behavioral data and the disease type, and a larger value indicates a higher degree of association.
5. Generating a monitoring strategy: finally, according to the calculated association strength, the monitoring frequency adjusting unit can set a corresponding monitoring frequency interval for each physiological data and each behavior data, and a personalized monitoring strategy is generated. Data with higher correlation strength may require more frequent monitoring, while data with lower correlation strength may set a more relaxed monitoring frequency.
Through the above process, the monitoring frequency adjusting unit can calculate the association strength of each monitorable physiological data and behavior data and the disease type according to the disease type of the patient and the physiological abnormality and behavior abnormality data related to the disease type, and generate a personalized monitoring strategy.
In some embodiments, the monitoring frequency adjustment unit calculates correlations between physiological anomalies and behavioral anomalies and each monitorable physiological data and behavioral data based on formulas:
r＝cov(X,Y)/(σXσY)；
wherein cov (X, Y) covariance is the covariance of the corresponding physiological anomaly and the monitorable physiological data, or the covariance of the behavioral anomaly and the monitorable behavioral data; (σxσy) is the standard deviation of physiological anomalies from monitorable physiological data, or the standard deviation of behavioral anomalies from monitorable behavioral data.
Specifically, the formula for calculating the covariance of two variables (i.e., physiological anomalies and monitorable physiological data, or behavioral anomalies and monitorable behavioral data) is as follows:
where Xi and Yi are the i-th observations of two variables, X-andrespectively the average of the two variables, n being the sample size.
Specifically, the formula for calculating the standard deviation between two variables is as follows:
σX＝√[Σ(Xi-X-)^2/(n-1)]；
specifically, the value of the correlation coefficient r ranges from-1 to 1, and indicates positive correlation when r is a positive value, negative correlation when r is a negative value, and no correlation when r is close to 0. The closer the correlation coefficient is to 1 or-1, the stronger the degree of correlation between the two variables.
In some embodiments, after determining the monitoring frequency interval for each type of physiological data and behavioral data, the step of the monitoring frequency unit generating the monitoring policy further comprises:
s50, determining the disease development trend of the patient according to the disease condition data.
Wherein, in this step, the monitoring frequency unit determines the current patient condition trend by analyzing patient condition data, including physiological data and behavioral data. By observing the change trend of the data, statistical analysis and other methods, the disease progress of the patient can be known, including the conditions of stable, improved or worsened disease state and the like.
S60, determining the current monitoring frequency of each type of physiological data and behavior data according to the disease development trend and the monitoring frequency interval.
Specifically, the monitoring frequency unit combines the disease development trend and the monitoring frequency interval determined before to determine the current monitoring frequency of each type of physiological data and behavior data. Depending on the current patient's condition, it may be desirable to increase the monitoring frequency to capture changes in the condition more timely, or to decrease the monitoring frequency during stabilization to avoid frequent data acquisition.
For example, if the patient's condition tends to significantly deteriorate, the monitoring frequency unit may correspondingly increase the frequency of monitoring critical physiological data (e.g., heart rate, blood pressure, etc.), as well as the change in behavioral data (e.g., movement, sleep, etc.) of interest. During the stable condition, the monitoring frequency unit may moderately decrease the monitoring frequency, and only monitor at a specific time or under a specific event.
It can be understood that the monitoring frequency unit can generate a personalized monitoring strategy more suitable for the current illness state by comprehensively considering the illness state trend and the monitoring frequency interval of the patient. Such monitoring strategies can more accurately reflect the patient's disease condition, provide timely health data to healthcare workers, and help them make more scientific therapeutic and intervention decisions.
In some embodiments, determining the current monitoring frequency of each type of physiological data and behavioral data according to the disease progression trend and the monitoring frequency interval comprises:
when the condition of the patient is in a stable or improved trend, selecting a lower monitoring frequency from the monitoring frequency interval as the current monitoring frequency of the corresponding physiological data or behavior data.
In particular, as the patient's condition is relatively stable or evolving in a good direction, a lower frequency of monitoring is sufficient to capture changes in the condition and frequent data collection may be reduced so as not to unduly interfere with the patient's daily life.
When the condition of the patient is in worsening or critical trend, a higher monitoring frequency is selected from the monitoring frequency interval as the current monitoring frequency of the corresponding physiological data or behavioral data.
In particular. Because the patient's condition has a worsening or critical trend, it is necessary to monitor changes in the condition more closely, discover abnormalities in time and take urgent interventions. The higher monitoring frequency can acquire important health data more timely, and provide more information about the condition of the patient for medical staff.
It can be understood that such a monitoring frequency strategy fully considers the condition change of the patient, and flexibly adjusts the monitoring frequency according to different condition trends, so as to achieve the purpose of accurately monitoring the health state of the patient under different conditions. By selecting the monitoring frequency according to the disease priority, medical staff can obtain timely and reasonable physiological data and behavior data, help them to better know the health condition of the patient, and make timely intervention and treatment decisions.
In some embodiments, the frequency monitoring unit determines the monitoring frequency corresponding to different disease development trends based on the following steps:
s110, dividing a preset monitoring frequency interval into three parts according to the size of a frequency value, and defining a lower frequency interval, a medium frequency interval and a higher frequency interval respectively;
s120, calculating the frequency average value of a lower frequency interval and a higher frequency interval;
s130, according to the current disease development trend of the patient, mapping the frequency average value of the lower frequency interval with the stable or improved trend, and mapping the frequency average value of the higher frequency interval with the worsening or critical trend to obtain specific monitoring frequencies corresponding to different disease development trends.
It can be appreciated that through the above scheme, the monitoring frequency can be intelligently selected according to the disease development trend of the patient, and a personalized monitoring strategy is generated. Therefore, each patient can obtain customized monitoring service according to the disease state of the patient, the monitoring requirements of the patient are better met, and the monitoring effectiveness and accuracy are improved. In addition, the monitoring frequency is dynamically adjusted according to the illness state trend, so that unnecessary frequent monitoring is avoided, and manpower and equipment resources are saved. Meanwhile, for patients with stable illness conditions, the reduction of the monitoring frequency is also helpful for reducing the workload of medical staff.
In some embodiments, the prediction unit predicts a patient's condition for a specified period of time in the future based on the trained deep learning neural network.
Specifically, the deep learning neural network is a powerful machine learning method, is suitable for processing complex nonlinear relations and large-scale data, and can automatically learn characteristic representation and modes from the data.
For example, the prediction unit may enable prediction of the patient's condition within a specified period of time in the future based on the following steps:
1. data preparation: historical data for the patient is collected, including characteristic data related to the progress of the condition and corresponding condition status or progress. These characteristic data may include physiological indicators, laboratory test results, imaging data, behavioral data, etc., and the condition status may be represented numerically.
2. Data preprocessing: preprocessing the collected data, including data cleaning, feature selection, missing value processing and the like. Ensuring the quality and integrity of data is critical to training deep learning models.
3. Constructing a deep learning model: a suitable deep learning neural network structure, such as a Convolutional Neural Network (CNN) for processing image data, a cyclic neural network (RNN) or a long short time memory network (LSTM) for processing sequence data, a transducer network, etc., is selected. And designing a proper network structure according to the characteristics and the prediction targets of the data.
4. Model training: the preprocessed data is separated into a training set and a validation set, and the training set is used to train the deep learning model. In the training process, the model optimizes the weight through a back propagation algorithm, and the fitting capacity of training data is gradually improved.
5. Model verification and tuning: the validation set is used to evaluate the performance of the trained deep learning model, and different evaluation metrics such as Mean Square Error (MSE), mean Absolute Error (MAE), etc. may be used. And optimizing the model according to the evaluation result to improve the generalization capability of the model.
6. And (3) predicting: after the deep learning model is trained and passes verification, new unknown data can be input into the model for prediction. And obtaining the predicted value of the disease development condition of the patient in a future appointed period through the predicted result of the deep learning model.
7. Model optimization and updating: as more data accumulates and conditions change, the deep learning model may need to be updated and optimized periodically to maintain its accuracy and adaptability.
It can be understood that the deep learning neural network is utilized to predict the disease development condition of the patient in the future appointed period, so that complex data relation and mode can be better captured, and the accuracy and effect of prediction are improved.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.