Intelligent blood oxygen monitoring and alarming system

Abstract

The invention relates to the technical field of blood oxygen monitoring, in particular to an intelligent blood oxygen monitoring and alarming system, which comprises a cloud database, an information acquisition port, an information analysis port and an information feedback port, and is characterized in that the cloud database is used for storing static attribute data of a patient, including age, gender, basic disease and treatment stage; the dynamic physiological data comprises real-time oxyhemoglobin saturation, heart rate, respiratory rate, body temperature and medication records; the information acquisition port is used for acquiring static attribute data and dynamic physiological data of a patient; the information analysis port is used for calculating a blood oxygen risk score, predicting a future blood oxygen trend and generating a blood oxygen change index; the information feedback port is used for sending real-time alarm signals or intervention suggestions to medical staff.

Description

Technical Field

[0001] The present invention relates to the technical field of blood oxygen monitoring, and in particular to an intelligent blood oxygen monitoring and alarm system. Background Art

[0002] Blood oxygen saturation (SpO2) is an important indicator for assessing a patient's respiratory and circulatory function, and plays a key role in intensive care, postoperative recovery, and the management of patients with chronic diseases. Existing blood oxygen monitoring technologies mainly rely on finger-clip oximeters, wearable devices, or bedside monitors, which can collect blood oxygen and heart rate data in real time. However, these technologies are mostly limited to single data monitoring and lack the integrated analysis of multi-dimensional information. The alarm mechanism is usually based on a fixed threshold and cannot make intelligent judgments based on individual differences or dynamic trends of patients. In addition, the existing system has weak capabilities in data storage, risk prediction, and personalized intervention, making it difficult to meet the needs of precision medicine. Summary of the Invention

[0003] In response to the shortcomings of the existing technology, the present invention proposes an intelligent blood oxygen monitoring and alarm system. The present invention integrates the patient's static attributes (age, underlying diseases, treatment stage) and dynamic physiological data (blood oxygen, heart rate, medication records) through a cloud database to achieve comprehensive risk assessment; adopts an LSTM neural network model to predict future blood oxygen trends, and uses time series data (such as blood oxygen risk score sequences) to train the model to improve prediction accuracy; intelligently triggers alarms or adjusts nursing strategies based on risk thresholds (MDS0) and blood oxygen variation index (DTI) to reduce false alarms and missed alarms; automated analysis reduces manual intervention and reduces the workload of medical staff.

[0004] To achieve the above object, the technical solution of the present invention is as follows:

[0005] An intelligent blood oxygen monitoring and alarm system includes a cloud database, an information collection port, an information analysis port, and an information feedback port. The cloud database is used to store patients' static attribute data, including age, gender, underlying diseases, and treatment stage; as well as dynamic physiological data, including real-time blood oxygen saturation, heart rate, respiratory rate, body temperature, and medication records; the information collection port is used to collect patients' static attribute data and dynamic physiological data; the information analysis port is used to calculate blood oxygen risk scores, predict future blood oxygen trends, and generate a blood oxygen change index; and the information feedback port is used to send real-time alarm signals or intervention suggestions to medical staff.

[0006] A further improvement of the present invention is that the information collection port includes a patient attribute information collection module and a real-time physiological data collection module. The patient attribute information collection module is used to collect the patient's electronic medical record information, including age, gender, underlying diseases, current medication regimen, and ventilator usage status; the real-time physiological data collection module is used to collect blood oxygen saturation, heart rate, respiratory rate, body temperature and exercise status data through wearable devices or medical sensors.

[0007] A further improvement of the present invention is that the information analysis port includes a data retrieval module, a risk analysis module and a trend prediction module. The data retrieval module is used to retrieve historical blood oxygen data and alarm event records of similar patients matching the current patient from the cloud database; the risk analysis module is used to calculate a real-time risk score based on the blood oxygen risk scoring formula; and the trend prediction module is used to predict future blood oxygen trends using an LSTM neural network model.

[0008] A further improvement of the present invention is that the risk analysis module runs a blood oxygen risk score calculation strategy, and the blood oxygen risk score calculation strategy includes the following specific steps:

[0009] S11. Extract the dynamic blood oxygen data, heart rate, and respiratory rate change curves of the patient in the current treatment stage, and obtain the normal range of similar patients as a benchmark;

[0010] S12. Calculate the blood oxygen risk score. The calculation formula for the blood oxygen risk score is:

[0011]

[0012] Among them, MDS i represents the blood oxygen risk score of the patient at the i-th assessment, k represents the k-th physiological indicator, ΔT k Indicates the total time that the kth indicator exceeds the benchmark range in the current evaluation period, ΔT max Indicates the total duration of the evaluation cycle, ΔS k Indicates the fluctuation range of the kth indicator within the period, s norm It represents the average fluctuation range of the corresponding indicators of patients of the same type, α and β are weight factors, and α+β=1.

[0013] S13, preset risk threshold MDS0, compare the patient's blood oxygen risk score MDS at the i-th assessment i The relationship between the risk threshold MDS0 and the risk threshold MDS0 is as follows: i When the risk threshold MDS0 is greater than or equal to the risk threshold, a high-risk warning signal is sent to the information feedback port. i When the risk threshold MDS0 is less than the threshold, no warning is activated, and the patient's blood oxygen risk score MDSi Transmitted to the trend prediction module.

[0014] A further improvement of the present invention is that the trend prediction module runs future blood oxygen trend prediction, and the future blood oxygen trend prediction includes the following specific steps:

[0015] S21. Obtain the patient's blood oxygen risk score for the previous i assessments, divide this data into a 70% training set and a 30% test set, and construct an MDS input. i-2 、MDS i-1 、MDS i , the output is MDS i+1 The time series prediction model is trained using 70% of the training set to obtain the initial time series prediction model.

[0016] S22. Test the initial time series prediction model using a 30% coefficient test set, optimize the model parameters using mean square error, select the model with the smallest prediction error on the test set as the final time series prediction model, and predict the blood oxygen risk score for the i+1th assessment;

[0017] S23. Output the predicted blood oxygen risk score of the (i+1)th assessment.

[0018] A further improvement of the present invention is that the future blood oxygen trend prediction further includes:

[0019] S24. Extract the blood oxygen risk score MDS of the i+1th assessment i+1 ;

[0020] S25. Calculate the patient's blood oxygen variability index. The calculation formula for the blood oxygen variability index is:

[0021]

[0022] S26: Determine whether it is necessary to send a decision adjustment signal to the information feedback port.

[0023] A further improvement of the present invention is that the time series prediction model in S22 is an LSTM neural network model.

[0024] A further improvement of the present invention is that a DTI threshold is preset. When the blood oxygen variation index DTI is greater than the DTI threshold, the current nursing strategy continues to be executed. When the blood oxygen variation index DTI is less than or equal to the DTI threshold, a decision adjustment signal is sent to the information feedback port.

[0025] The technical effects of the present invention are as follows:

[0026] The present invention proposes an intelligent blood oxygen monitoring and alarm system. This system integrates patients' static attributes (age, underlying diseases, treatment stage) with dynamic physiological data (blood oxygen, heart rate, medication records) through a cloud database to achieve comprehensive risk assessment. It adopts an LSTM neural network model to predict future blood oxygen trends and uses time series data to train the model to improve prediction accuracy. It intelligently triggers alarms or adjusts nursing strategies based on risk thresholds and blood oxygen change indexes to reduce false alarms and missed alarms. Automated analysis reduces manual intervention and reduces the workload of medical staff. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0028] Figure 1 This is a structural diagram of an intelligent blood oxygen monitoring and alarm system of the present invention. DETAILED DESCRIPTION

[0029] Example 1

[0030] This embodiment proposes an intelligent blood oxygen monitoring and alarm system. This system integrates static patient attributes (age, underlying diseases, treatment stage) with dynamic physiological data (blood oxygen, heart rate, medication records) through a cloud database to achieve comprehensive risk assessment. It uses an LSTM neural network model to predict future blood oxygen trends and trains the model using time series data to improve prediction accuracy. It intelligently triggers alarms or adjusts nursing strategies based on risk thresholds and blood oxygen change indices to reduce false positives and missed alerts. Automated analysis reduces manual intervention and reduces the workload of medical staff.

[0031] like Figure 1 As shown, an intelligent blood oxygen monitoring and alarm system includes a cloud database, an information collection port, an information analysis port and an information feedback port. The cloud database is used to store the patient's static attribute data, including age, gender, underlying diseases, and treatment stage; and dynamic physiological data, including real-time blood oxygen saturation, heart rate, respiratory rate, body temperature, and medication records; the information collection port is used to collect the patient's static attribute data and dynamic physiological data; the information analysis port is used to calculate the blood oxygen risk score, predict future blood oxygen trends and generate a blood oxygen change index; the information feedback port is used to send real-time alarm signals or intervention suggestions to medical staff.

[0032] Example 2

[0033] The embodiment further improves the design based on embodiment 1. The difference is that, in this embodiment, the information acquisition port includes a patient attribute information acquisition module and a real-time physiological data acquisition module. The patient attribute information acquisition module is used to collect patient electronic medical record information, including age, gender, underlying diseases, current medication regimen, and ventilator usage status; the real-time physiological data acquisition module is used to collect blood oxygen saturation, heart rate, respiratory rate, body temperature and exercise status data through wearable devices or medical sensors.

[0034] Example 3

[0035] In this embodiment, the information analysis port includes a data retrieval module, a risk analysis module, and a trend prediction module. The data retrieval module is used to retrieve historical blood oxygen data and alarm event records of similar patients matching the current patient from the cloud database; the risk analysis module is used to calculate a real-time risk score based on the blood oxygen risk scoring formula; and the trend prediction module is used to predict future blood oxygen trends using an LSTM neural network model.

[0036] In this embodiment, the risk analysis module runs a blood oxygen risk score calculation strategy, which includes the following specific steps:

[0037] S11. Extract the dynamic blood oxygen data, heart rate, and respiratory rate change curves of the patient in the current treatment stage, and obtain the normal range of similar patients as a benchmark;

[0038] S12. Calculate the blood oxygen risk score. The calculation formula for the blood oxygen risk score is:

[0039]

[0040] Among them, MDS i represents the blood oxygen risk score of the patient at the i-th assessment, k represents the k-th physiological indicator, ΔT k Indicates the total time that the kth indicator exceeds the benchmark range in the current evaluation period, ΔT max Indicates the total duration of the evaluation cycle, ΔS k Indicates the fluctuation range of the kth indicator within the period, s norm It represents the average fluctuation range of the corresponding indicators of patients of the same type, α and β are weight factors, and α+β=1.

[0041] S13, preset risk threshold MDS0, compare the patient's blood oxygen risk score MDS at the i-th assessment i The relationship between the risk threshold MDS0 and the risk threshold MDS0 is as follows: i When the risk threshold MDS0 is greater than or equal to the risk threshold, a high-risk warning signal is sent to the information feedback port. iWhen the risk threshold MDS0 is less than the threshold, no warning is activated, and the patient's blood oxygen risk score MDS i Transmitted to the trend prediction module.

[0042] Example 4

[0043] This embodiment further improves the design based on the third embodiment. In this embodiment, the trend prediction module performs future blood oxygen trend prediction, and the future blood oxygen trend prediction includes the following specific steps:

[0044] S21. Obtain the patient's blood oxygen risk score for the previous i assessments, divide this data into a 70% training set and a 30% test set, and construct an MDS input. i-2 、MDS i-1 、MDS i , the output is MDS i+1 The time series prediction model is trained using 70% of the training set to obtain the initial time series prediction model.

[0045] S22. Test the initial time series prediction model using a 30% coefficient test set, optimize the model parameters using mean square error, select the model with the smallest prediction error on the test set as the final time series prediction model, and predict the blood oxygen risk score for the i+1th assessment;

[0046] S23. Output the predicted blood oxygen risk score of the (i+1)th assessment.

[0047] In this embodiment, the future blood oxygen trend prediction further includes:

[0048] S24. Extract the blood oxygen risk score MDS of the i+1th assessment i+1 ;

[0049] S25. Calculate the patient's blood oxygen variability index. The calculation formula for the blood oxygen variability index is:

[0050]

[0051] S26: Determine whether it is necessary to send a decision adjustment signal to the information feedback port.

[0052] In this embodiment, the time series prediction model in S22 is an LSTM neural network model.

[0053] In this embodiment, a DTI threshold is preset. When the blood oxygen variation index DTI is greater than the DTI threshold, the current nursing strategy continues to be executed. When the blood oxygen variation index DTI is less than or equal to the DTI threshold, a decision adjustment signal is sent to the information feedback port.

[0054] It should be noted here that the present invention integrates patients' static attributes (age, underlying diseases, treatment stage) and dynamic physiological data (blood oxygen, heart rate, medication records) through a cloud database to achieve comprehensive risk assessment; adopts an LSTM neural network model to predict future blood oxygen trends, and uses time series data to train the model to improve prediction accuracy; intelligently triggers alarms or adjusts nursing strategies based on risk thresholds and blood oxygen change indexes to reduce false alarms and missed alarms; automated analysis reduces manual intervention and reduces the workload of medical staff.

[0055] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0056] It should be understood that determining B based on A does not mean determining B based solely on A. B can also be determined based on A and / or other information.

[0057] The above embodiments can be implemented in whole or in part through software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0058] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0059] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0060] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only one type. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0061] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0062] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0063] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0064] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent blood oxygen monitoring and alarm system, characterized in that: The system includes a cloud database, an information collection port, an information analysis port, and an information feedback port. The cloud database is used to store static attribute data of patients, including age, gender, underlying disease, and treatment stage; and dynamic physiological data, including real-time blood oxygen saturation, heart rate, respiratory rate, body temperature, and medication records; The information collection port is used to collect static attribute data and dynamic physiological data of patients; The information analysis port is used to calculate the blood oxygen risk score, predict future blood oxygen trends and generate a blood oxygen change index; The information feedback port is used to send real-time alarm signals or intervention suggestions to medical staff.

2. The intelligent blood oxygen monitoring and alarm system according to claim 1, characterized in that: The information collection port includes a patient attribute information collection module and a real-time physiological data collection module. The patient attribute information collection module is used to collect the patient's electronic medical record information, including age, gender, underlying diseases, current medication regimen, and ventilator usage status; The real-time physiological data acquisition module is used to collect blood oxygen saturation, heart rate, respiratory rate, body temperature and exercise status data through wearable devices or medical sensors.

3. The intelligent blood oxygen monitoring and alarm system according to claim 2, characterized in that: The information analysis port includes a data retrieval module, a risk analysis module, and a trend prediction module. The data retrieval module is used to retrieve historical blood oxygen data and alarm event records of similar patients matching the current patient from the cloud database; The risk analysis module is used to calculate a real-time risk score based on a blood oxygen risk score formula; The trend prediction module is used to predict future blood oxygen trends using an LSTM neural network model.

4. The intelligent blood oxygen monitoring and alarm system according to claim 3, characterized in that: The risk analysis module executes a blood oxygen risk score calculation strategy, which includes the following specific steps: S11. Extract the dynamic blood oxygen data, heart rate, and respiratory rate change curves of the patient in the current treatment stage, and obtain the normal range of similar patients as a benchmark; S12. Calculate the blood oxygen risk score. The calculation formula for the blood oxygen risk score is: Among them, MDS i represents the blood oxygen risk score of the patient at the i-th assessment, k represents the k-th physiological indicator, ΔT k Indicates the total time that the kth indicator exceeds the benchmark range in the current evaluation period, ΔT max Indicates the total duration of the evaluation cycle, ΔS k Indicates the fluctuation range of the kth indicator within the period, s norm It represents the average fluctuation range of the corresponding indicators of patients of the same type, α and β are weight factors, and α+β=1. S13, preset risk threshold MDS0, compare the patient's blood oxygen risk score MDS at the i-th assessment i The relationship between the risk threshold MDS0 and the risk threshold MDS0 is as follows: i When the risk threshold MDS0 is greater than or equal to the risk threshold, a high-risk warning signal is sent to the information feedback port. i When the risk threshold MDS0 is less than the threshold, no warning is activated, and the patient's blood oxygen risk score MDS i Transmitted to the trend prediction module.

5. The intelligent blood oxygen monitoring and alarm system according to claim 4, characterized in that: The trend prediction module performs future blood oxygen trend prediction, and the future blood oxygen trend prediction includes the following specific steps: S21. Obtain the patient's blood oxygen risk score for the previous i assessments, divide this data into a 70% training set and a 30% test set, and construct an MDS input. i-2 、MDS i-1 、MDS i , the output is MDS i+1 The time series prediction model is trained using 70% of the training set to obtain the initial time series prediction model. S22. Test the initial time series prediction model using a 30% coefficient test set, optimize the model parameters using mean square error, select the model with the smallest prediction error on the test set as the final time series prediction model, and predict the blood oxygen risk score for the i+1th assessment; S23. Output the predicted blood oxygen risk score of the (i+1)th assessment.

6. The intelligent blood oxygen monitoring and alarm system according to claim 5, characterized in that: The future blood oxygen trend prediction also includes: S24. Extract the blood oxygen risk score MDS of the i+1th assessment i+1 ; S25. Calculate the patient's blood oxygen variability index. The calculation formula for the blood oxygen variability index is: S26: Determine whether it is necessary to send a decision adjustment signal to the information feedback port.

7. The intelligent blood oxygen monitoring and alarm system according to claim 6, characterized in that: The time series prediction model in S22 is an LSTM neural network model.

8. The intelligent blood oxygen monitoring and alarm system according to claim 7, characterized in that: The specific content of S26 is: presetting a DTI threshold, when the blood oxygen variation index DTI is greater than the DTI threshold, continuing to execute the current nursing strategy; when the blood oxygen variation index DTI is less than or equal to the DTI threshold, sending a decision adjustment signal to the information feedback port.

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