Intensive care alarm system and method based on medical big data

Abstract

The invention relates to the technical field of intensive care, and discloses an intensive care alarm system based on medical big data, which comprises a monitoring alarm platform, and the monitoring alarm platform comprises a central processing module, a physiological signal acquisition module, a state presentation module and the like; the invention further provides an intensive care alarm method based on the medical big data, and the method comprises the following steps: S1, collecting physiological signals: collecting the physiological signals of a patient through the electrocardiosignal detection device, the respiration detection device, the blood pressure detection device, the blood oxygen detection device and the body temperature detection device. According to the invention, alarm harm can be effectively reduced, delayed treatment of medical staff due to'alarm fatigue 'is avoided, improvement of medical quality is facilitated, an individual device grading alarm threshold value can be set according to parameters of a patient, the real-time state of the patient can be accurately reflected in combination with change influence parameters of an intensive care device, and the possibility of false alarm is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of intensive care, and particularly to an intensive care alarm system and method based on medical big data. Background Art

[0002] Big data monitoring plays a crucial role in multiple fields, especially in health monitoring, environmental monitoring, financial market analysis, etc. By collecting and analyzing data in real time, big data monitoring provides instant insights, helps to detect problems, trends or abnormal behaviors in a timely manner, and in the field of healthcare, it helps the medical team to identify changes in the patient's condition early, issue warnings, and thus take timely intervention measures to improve the treatment effect and patient safety.

[0003] The intensive care status alarm system can monitor the patient's vital signs and important medical indicators in real time, such as heart rate, blood pressure, respiratory rate, etc. When the indicators exceed the normal range, the system will immediately issue an alarm, enabling medical staff to respond quickly and intervene in a timely manner, greatly improving the patient's survival rate and treatment effect. And through continuous monitoring of the patient's status and real-time alarm, medical staff can take measures before the patient's condition deteriorates, avoiding the occurrence of serious health problems or complications.

[0004] However, the existing intensive care units are prone to monitoring fatigue, resulting in medical staff not being able to detect abnormal patient data in a timely manner. Accordingly, the updated Internet of Things intensive care system realizes the function of automatic alarm, but its alarm types are numerous, such as instrument failure, monitoring abnormality, etc., which makes medical staff prone to "alarm fatigue" and ignore the changes in the patient's condition. If not handled in a timely manner, it may lead to patient injury or death, which is not conducive to the improvement of medical quality. Summary of the Invention

[0005] (1) Technical Problems to be Solved

[0006] Aiming at the deficiencies of the existing technology, the present invention provides an intensive care alarm system and method based on medical big data, mainly to solve the problems that the existing intensive care units are prone to monitoring fatigue, resulting in medical staff not being able to detect abnormal patient data in a timely manner. Accordingly, the updated Internet of Things intensive care system realizes the function of automatic alarm, but its alarm types are numerous, such as instrument failure, monitoring abnormality, etc., which makes medical staff prone to "alarm fatigue" and ignore the changes in the patient's condition. If not handled in a timely manner, it may lead to patient injury or death, which is not conducive to the improvement of medical quality.

[0007] (2) Technical Solutions

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

[0009] A critical care alarm system based on medical big data, comprising a monitoring and alarm platform. The monitoring and alarm platform includes a central processing module, a physiological signal acquisition module, a status display module, a reference acquisition module, a weighting and ranking module, an alarm level judgment module, an optimization and adjustment module, and an alarm device. The central processing module is connected to the physiological signal acquisition module. The physiological signal acquisition module is used to collect the physiological signals of the monitored person and transmit them to the central processing module to form a user set. The status display module is connected to the central processing module. The status display module is used to obtain the physiological status display data of the patient. After receiving the status display data, it immediately performs a pre-warning status safety supervision operation on the status display data. The status display module is sequentially connected to the reference acquisition module, the weighting and ranking module, the alarm level judgment module, the optimization and adjustment module, and the alarm device. The reference acquisition module, the weighting and ranking module, the alarm level judgment module, and the optimization and adjustment module are interconnected with each other. The optimization and adjustment module, the central processing module, and the alarm device are interconnected with each other.

[0010] Further, the reference acquisition module is used to obtain status parameter information, analyze the patient parameter information to set a critical care plan, and obtain the configuration requirements of critical care devices based on the critical care plan. The weighting and ranking module is used to configure a critical care device group according to the configuration requirements of critical care devices, assign weights based on the configuration results of the critical care device group, and generate a priority ranking result of the critical care devices in the critical care device group through the weighting situation of the critical care device group.

[0011] On the basis of the foregoing solution, the alarm level judgment module obtains the individual device grading alarm threshold of the critical care devices in the critical care device group and the critical care device change impact parameter input optimized multiple linear regression prediction model based on the critical care plan, and sets the alarm level grading rules. The optimization and adjustment module is used to set the alarm adjustment rules according to the critical care device priority ranking result and the grading alarm impact rules, and optimize and adjust the monitoring status alarm information based on the alarm adjustment rules to obtain an optimized monitoring status alarm signal.

[0012] As a further solution of the present invention, the optimization and adjustment module transmits the optimized monitoring status alarm signal to the central processing module. The central processing module processes the optimized monitoring status alarm signal and then sends the signal to the alarm device, so that the alarm device issues an alarm.

[0013] Further, the physiological signal acquisition module includes an electrocardiogram signal detection device, a respiration detection device, a blood pressure detection device, a blood oxygen detection device, and a body temperature detection device.

[0014] The present invention also proposes a critical care alarm method based on medical big data, comprising the following steps:

[0015] S1: Physiological signal acquisition. The physiological signals of the patient are collected by an electrocardiogram signal detection device, a respiration detection device, a blood pressure detection device, a blood oxygen detection device, and a body temperature detection device, and the collected physiological signals are transmitted to the central processing module for storage to form physiological state data;

[0016] S2: Physiological state data monitoring. After receiving the state performance data, immediately perform early warning state safety supervision operations on the state performance data;

[0017] S3: Sorting. Obtain the patient parameter information, analyze the patient parameter information to set the intensive care plan, obtain the intensive care device configuration requirements based on the intensive care plan, configure the intensive care device group according to the intensive care device configuration requirements, empower based on the configuration result of the intensive care device group, and generate the priority sorting result of the intensive care devices in the intensive care device group through the empowerment situation of the intensive care device group;

[0018] S4: Alarm signal grading optimization. Obtain the individual device grading alarm threshold and the intensive care device change impact parameter of the intensive care devices in the intensive care device group based on the intensive care plan, input the optimized multiple linear regression prediction model, set the alarm level grading rules, set the alarm adjustment rules according to the intensive care device priority sorting result and the grading alarm impact rules, and optimize and adjust the monitoring state alarm information based on the alarm adjustment rules to obtain the optimized monitoring state alarm information;

[0019] S5: Alarm. The alarm device obtains the optimized monitoring state alarm information and makes the alarm device issue an alarm to remind the medical staff.

[0020] On the basis of the foregoing solution, the optimized multiple linear regression prediction model input in S4 includes: constructing a corresponding matrix by integrating user data and corresponding indicators to form a dataset to be predicted, inputting the dataset to be predicted into the optimized multiple linear regression prediction model to obtain a predicted value, so as to obtain the rules for setting the alarm level grading.

[0021] (III) Beneficial effects

[0022] Compared with the prior art, the present invention provides an intensive care alarm system and method based on medical big data, which has the following beneficial effects:

[0023] 1. The present invention reduces the risk of danger caused by abnormal patient data due to the negligence of the accompanying personnel, improves safety, integrates data collection and processing, realizes real-time monitoring of the monitoring data, provides a guarantee for intelligent control, and can effectively reduce the alarm hazard and avoid the delay of treatment by medical staff due to "alarm fatigue", which is beneficial to the improvement of medical quality.

[0024] 2. The present invention formulates a targeted monitoring plan by analyzing the patient's parameter information in detail, ensuring that each patient can receive the most suitable monitoring measures for their condition, setting individual device grading alarm thresholds according to the patient's parameters, and combining the impact of changes in intensive care devices on parameters to accurately reflect the patient's real-time status and reduce the possibility of false alarms.

[0025] 3. The present invention designs a physiological signal analysis and alarm device. Without the accompaniment of medical staff or family members, in case of a crisis, it can alarm the hospital immediately, enabling the patient to receive timely and effective treatment. At the same time, it saves the patient's physiological data within a certain period and the standard physiological data of the human body, which plays a crucial role in the long-term condition monitoring and analysis of the patient by medical staff. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 FIG. is a schematic structural diagram of a critical care alarm system based on medical big data proposed by the present invention;

[0027] Figure 2 FIG. is a schematic flow structure diagram of a critical care alarm method based on medical big data proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0029] Embodiment 1

[0030] Refer to Figure 1 - Figure 2, A critical care alarm system based on medical big data, including a monitoring and alarm platform. The monitoring and alarm platform includes a central processing module, a physiological signal acquisition module, a status display module, a reference acquisition module, a weight assignment and sorting module, an alarm level judgment module, an optimization and adjustment module, and an alarm device. The central processing module is connected to the physiological signal acquisition module. The physiological signal acquisition module is used to collect the physiological signals of the monitored person and transmit them to the central processing module to form a user set. The status display module is connected to the central processing module. The status display module is used to obtain the physiological status display data of the patient. After receiving the status display data, it immediately performs early warning status safety supervision operations on the status display data. The status display module is sequentially connected to the reference acquisition module, the weight assignment and sorting module, the alarm level judgment module, the optimization and adjustment module, and the alarm device. The reference acquisition module, the weight assignment and sorting module, the alarm level judgment module, and the optimization and adjustment module are interconnected. The optimization and adjustment module, the central processing module, and the alarm device are interconnected. The present invention reduces the risk of danger caused by abnormal patient data due to the negligence of the accompanying personnel, improves safety, and at the same time integrates data acquisition and processing, realizes real-time monitoring of monitoring data, provides guarantee for intelligent control, and can effectively reduce the harm of alarms, avoid delays in treatment caused by "alarm fatigue" of medical staff, and is conducive to the improvement of medical quality.

[0031] In particular, in the present invention, the reference acquisition module is used to obtain status parameter information, analyze the patient parameter information to set a critical care plan, and obtain the configuration requirements of critical care devices based on the critical care plan. The weight assignment and sorting module is used to configure a critical care device group according to the configuration requirements of critical care devices, assign weights based on the configuration results of the critical care device group, and generate a priority sorting result of the critical care devices in the critical care device group through the weight assignment situation of the critical care device group. The alarm level judgment module obtains the individual device grading alarm threshold of the critical care devices in the critical care device group and the optimized multiple linear regression prediction model of the critical care device change influence parameter input based on the critical care plan, and sets the alarm level grading rules. The optimization and adjustment module is used to set the alarm adjustment rules according to the priority sorting result of the critical care devices and the grading alarm influence rules, and optimize and adjust the monitoring status alarm information based on the alarm adjustment rules to obtain an optimized monitoring status alarm signal. The optimization and adjustment module transmits the optimized monitoring status alarm signal to the central processing module. The central processing module processes the optimized monitoring status alarm signal and then sends the signal to the alarm device to make the alarm device issue an alarm. The physiological signal acquisition module includes an electrocardiogram signal detection device, a respiration detection device, a blood pressure detection device, a blood oxygen detection device, and a body temperature detection device.

[0032] The present invention also proposes a critical care alarm method based on medical big data, including the following steps:

[0033] S1: Physiological signal acquisition. Physiological signals of the patient are collected through an electrocardiogram signal detection device, a respiration detection device, a blood pressure detection device, a blood oxygen detection device, and a body temperature detection device, and the collected physiological signals are transmitted to the central processing module for storage to form physiological state data;

[0034] S2: Physiological state data monitoring. After receiving the state performance data, immediately perform early warning state safety supervision operations on the state performance data. By designing a physiological signal analysis and alarm device, without the accompaniment of medical staff or family members, in case of a crisis situation, the hospital can be alerted immediately, enabling the patient to receive timely and effective treatment; meanwhile, the physiological data of the patient within a certain period of time and the standard physiological data of the human body are saved, which plays a crucial role in the long-term condition monitoring and analysis of the patient by medical staff;

[0035] S3: Sorting. Obtain the patient parameter information, analyze the patient parameter information to set the intensive care plan, and based on the intensive care plan, obtain the configuration requirements of the intensive care device group. Configure the intensive care device group according to the configuration requirements of the intensive care device group, empower based on the configuration result of the intensive care device group, and generate the priority sorting result of the intensive care devices in the intensive care device group through the empowerment situation of the intensive care device group;

[0036] S4: Alarm signal grading optimization. Based on the intensive care plan, obtain the individual device grading alarm threshold and the change impact parameters of the intensive care devices in the intensive care device group, input the optimized multiple linear regression prediction model, set the alarm level grading rules, set the alarm adjustment rules according to the priority sorting result of the intensive care devices and the grading alarm impact rules, and optimize and adjust the monitoring state alarm information based on the alarm adjustment rules to obtain the optimized monitoring state alarm information. Develop a targeted monitoring plan by analyzing the patient's parameter information in detail to ensure that each patient can receive the most suitable monitoring measures for their condition, and set the individual device grading alarm threshold according to the patient's parameters, combined with the change impact parameters of the intensive care devices, to accurately reflect the patient's real-time state and reduce the possibility of false alarms;

[0037] S5: Alarm. The alarm device obtains and optimizes the monitoring state alarm information to make the alarm device issue an alarm to remind the medical staff.

[0038] It should be noted specifically that the optimized multiple linear regression prediction model input in S4 includes: constructing a corresponding matrix by integrating user data and corresponding indicators to form a dataset to be predicted, inputting the dataset to be predicted into the optimized multiple linear regression prediction model to obtain a predicted value, and thus obtaining the rules for setting the alarm level grading.

[0039] Example 2

[0040] Refer to Figure 1 - Figure 2, A critical care alarm system based on medical big data, comprising a monitoring and alarm platform. The monitoring and alarm platform includes a central processing module, a physiological signal acquisition module, a status display module, a reference acquisition module, a weighting and ranking module, an alarm level judgment module, an optimization and adjustment module, and an alarm device. The central processing module is connected to the physiological signal acquisition module. The physiological signal acquisition module is used to collect the physiological signals of the monitored person and transmit them to the central processing module to form a user set. The status display module is connected to the central processing module. The status display module is used to obtain the physiological status display data of the patient. After receiving the status display data, it immediately performs early warning status safety supervision operations on the status display data. The status display module is sequentially connected to the reference acquisition module, the weighting and ranking module, the alarm level judgment module, the optimization and adjustment module, and the alarm device. The reference acquisition module, the weighting and ranking module, the alarm level judgment module, and the optimization and adjustment module are interconnected. The optimization and adjustment module, the central processing module, and the alarm device are interconnected. The present invention reduces the risk of danger caused by abnormal patient data due to the negligence of the accompanying personnel, improves safety, and at the same time integrates data collection and processing, realizes real-time monitoring of monitoring data, provides a guarantee for intelligent control, and can effectively reduce the harm of alarms, avoid delays in treatment caused by "alarm fatigue" of medical staff, and is conducive to the improvement of medical quality.

[0041] In particular, in the present invention, the reference acquisition module is used to obtain status parameter information, analyze the patient parameter information to set a critical care plan, and obtain the configuration requirements of critical care devices based on the critical care plan. The weighting and ranking module is used to configure a critical care device group according to the configuration requirements of critical care devices, assign weights based on the configuration results of the critical care device group, and generate a priority ranking result of the critical care devices in the critical care device group through the weighting situation of the critical care device group. The alarm level judgment module obtains the individual device grading alarm threshold of the critical care devices in the critical care device group and the change impact parameters of the critical care devices based on the critical care plan, inputs them into an optimized multiple linear regression prediction model, and sets the alarm level grading rules. The optimization and adjustment module is used to set the alarm adjustment rules according to the priority ranking result of the critical care devices and the grading alarm impact rules, and optimize and adjust the monitoring status alarm information based on the alarm adjustment rules to obtain an optimized monitoring status alarm signal. The optimization and adjustment module transmits the optimized monitoring status alarm signal to the central processing module. The central processing module processes the optimized monitoring status alarm signal and then sends the signal to the alarm device, causing the alarm device to issue an alarm. The physiological signal acquisition module includes an electrocardiogram signal detection device, a respiration detection device, a blood pressure detection device, a blood oxygen detection device, and a body temperature detection device.

[0042] The present invention also proposes a critical care alarm method based on medical big data, comprising the following steps:

[0043] S1: Physiological signal acquisition. Physiological signals of the patient are acquired through an electrocardiogram signal detection device, a respiration detection device, a blood pressure detection device, a blood oxygen detection device, and a body temperature detection device, and the acquired physiological signals are transmitted to the central processing module for storage to form physiological state data;

[0044] S2: Physiological state data monitoring. After receiving the state performance data, immediately perform early warning state safety supervision operations on the state performance data. By designing a physiological signal analysis and alarm device, without the accompaniment of medical staff or family members, in case of a crisis, the hospital can be alerted immediately, enabling the patient to receive timely and effective treatment; meanwhile, the physiological data of the patient within a certain period of time and the standard physiological data of the human body are saved, which plays a crucial role in the long-term condition monitoring and analysis of the patient by medical staff;

[0045] S3: Sorting. Obtain the patient's parameter information, analyze the patient's parameter information to set up an intensive care plan, and based on the intensive care plan, obtain the configuration requirements of the intensive care device. Configure the intensive care device group according to the configuration requirements of the intensive care device, empower based on the configuration result of the intensive care device group, and generate the priority sorting result of the intensive care devices in the intensive care device group through the empowerment situation of the intensive care device group;

[0046] S4: Alarm signal grading optimization. Based on the intensive care plan, obtain the individual device grading alarm threshold and the change impact parameters of the intensive care device in the intensive care device group, input the optimized multiple linear regression prediction model, set the alarm level grading rules, set the alarm adjustment rules according to the intensive care device priority sorting result and the grading alarm impact rules, and optimize and adjust the monitoring state alarm information based on the alarm adjustment rules to obtain the optimized monitoring state alarm information. Develop a targeted monitoring plan by analyzing the patient's parameter information in detail to ensure that each patient can receive the most suitable monitoring measures for their condition, and set the individual device grading alarm threshold according to the patient's parameters, combined with the change impact parameters of the intensive care device, to accurately reflect the patient's real-time state and reduce the possibility of false alarms;

[0047] S5: Alarm. The alarm device obtains and optimizes the monitoring state alarm information to make the alarm device issue an alarm to remind medical staff.

[0048] It should be noted specifically that the optimized multiple linear regression prediction model input in S4 includes: constructing a corresponding matrix by integrating user data and corresponding indicators to form a dataset to be predicted, inputting the dataset to be predicted into the optimized multiple linear regression prediction model to obtain a predicted value, and thus obtaining the rules for setting the alarm level grading.

[0049] In the description in this document, it should be noted that relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

Claims

1. A critical care alarm system based on medical big data, including a monitoring alarm platform, characterized in that: The monitoring alarm platform includes a central processing module, a physiological signal acquisition module, a state performance module, a reference acquisition module, a weighted ranking module, an alarm level judgment module, an optimization adjustment module and an alarm device. The central processing module is connected to the physiological signal acquisition module, and the physiological signal acquisition module is used to collect the physiological signals of the monitored person and transmit them to the central processing module to form a user set. The state performance module is connected to the central processing module, and the state performance module is used to obtain the patient's physiological state performance data. After receiving the state performance data, the state performance module immediately performs a warning state safety supervision operation on the state performance data. The state performance module is connected to the reference acquisition module, the weighted ranking module, the alarm level judgment module, the optimization adjustment module and the alarm device in sequence. The reference acquisition module, the weighted ranking module, the alarm level judgment module and the optimization adjustment module are interconnected, and the optimization adjustment module, the central processing module and the alarm device are interconnected.

2. According to claim 1, a critical care alarm system based on medical big data is characterized in that: The reference acquisition module is used to obtain status parameter information, analyze patient parameter information to set up a critical care plan, and obtain critical care device configuration requirements based on the critical care plan. The weighted sorting module is used to configure a critical care device group according to the critical care device configuration requirements, grant weights based on the critical care device group configuration results, and generate a priority sorting result of critical care devices in the critical care device group through the weighting status of the critical care device group.

3. The intensive care alarm system based on medical big data according to claim 1 is characterized in that: The alarm level judgment module obtains the individual device classification alarm thresholds of the critical care devices in the critical care device group based on the critical care plan and the critical care device change influencing parameters, inputs them into the optimized multiple linear regression prediction model, and sets the alarm level classification rules. The optimization adjustment module is used to set the alarm adjustment rules according to the critical care device priority sorting results and the classification alarm influencing rules, and optimizes and adjusts the monitoring status alarm information based on the alarm adjustment rules to obtain the optimized monitoring status alarm signal.

4. The intensive care alarm system based on medical big data according to claim 1 is characterized in that: The optimization adjustment module transmits the optimization monitoring state alarm signal to the central processing module, and the central processing module processes the optimization monitoring state alarm signal and sends the signal to the alarm device, so that the alarm device sounds an alarm.

5. The intensive care alarm system based on medical big data according to claim 4 is characterized in that: The physiological signal acquisition module includes an electrocardiogram signal detection device, a breathing detection device, a blood pressure detection device, a blood oxygen detection device and a body temperature detection device.

6. A critical care alarm method based on medical big data, characterized in that: The following steps are involved: S1: Physiological signal collection, collecting the patient's physiological signals through an electrocardiogram signal detection device, a breathing detection device, a blood pressure detection device, a blood oxygen detection device and a body temperature detection device, and transmitting the collected physiological signals to a central processing module for storage to form physiological status data; S2: Physiological status data monitoring: after receiving the status performance data, immediately perform early warning status safety supervision operations on the status performance data; S3: Sorting, obtaining patient parameter information, analyzing the patient parameter information to set up a critical care plan, and obtaining critical care device configuration requirements based on the critical care plan, configuring a critical care device group according to the critical care device configuration requirements, granting weights based on the critical care device group configuration results, and generating a priority ranking result of critical care devices in the critical care device group through the granting of the critical care device group; S4: Alarm signal classification optimization, based on the intensive care plan, obtain the individual device classification alarm thresholds and intensive care device change influencing parameters of the intensive care device in the intensive care device group, input the optimized multivariate linear regression prediction model, set the alarm level classification rules, set the alarm adjustment rules according to the intensive care device priority ranking results and the classification alarm influence rules, and optimize and adjust the monitoring status alarm information based on the alarm adjustment rules to obtain the optimized monitoring status alarm information; S5: Alarm, the alarm device obtains and optimizes the monitoring status alarm information, so that the alarm device sounds an alarm to remind medical staff.

7. The intensive care alarm method based on medical big data according to claim 6 is characterized in that: Inputting the optimized multiple linear regression prediction model in S4 includes: constructing a corresponding matrix based on user data and corresponding indicators to form a data set to be predicted, inputting the data set to be predicted into the optimized multiple linear regression prediction model to obtain a predicted value, thereby obtaining a rule for setting alarm level classification.

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