Dizziness patient nursing hidden danger analysis method and system

Abstract

The invention relates to the technical field of data analysis, in particular to a dizziness patient nursing hidden danger analysis method and system. Comprising the following steps: S1, detecting related indexes at set time intervals in the whole nursing cycle, and calculating a dizziness hidden danger evaluation value at the current set time according to the related indexes detected at the current set time; s2, setting a dizziness hidden danger evaluation grade, and comparing the dizziness hidden danger evaluation value with the dizziness hidden danger evaluation grade; s3, according to the number of the serious indexes and the number of the conventional indexes in the previous set time, adjusting a first weight and a second weight in the next set time, calculating a dizziness hidden danger evaluation value in the next set time according to the adjusted first weight and the adjusted second weight, updating a first dizziness hidden danger evaluation threshold value and a second dizziness hidden danger evaluation threshold value in the next set time, and calculating the dizziness hidden danger evaluation value in the next set time. Carrying out dizziness hidden danger evaluation at the next set time; and the accuracy and objectivity of dizziness patient nursing hidden danger analysis are improved.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to a method and system for analyzing potential nursing risks in patients with dizziness. Background Technology

[0002] In clinical nursing work, the focus of nursing safety management is on the fact that for hospitalized patients with severe conditions, long hospital stays, poor prognosis, and high disability rates, there are often unsafe hazards in nursing care that can endanger the patient's health or even life.

[0003] In the nursing care of hospitalized patients with dizziness, monitoring their condition is crucial. Timely detection of warning signs, assessment of treatment effectiveness, and prevention of accidents are essential for ensuring patient safety. However, current monitoring of dizziness patients relies heavily on the nursing experience of healthcare workers to identify potential safety hazards. But this experience alone may miss some subtle dangers and lacks objectivity.

[0004] Therefore, there is an urgent need to provide a method and system for analyzing potential nursing risks in dizzy patients, which can improve the accuracy and objectivity of such analysis compared to existing technologies. Summary of the Invention

[0005] This invention addresses the technical problems existing in the prior art and provides a method and system for analyzing potential nursing risks in patients with dizziness.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for analyzing potential nursing risks in patients with dizziness includes the following steps: S1. During the entire nursing cycle, relevant indicators are tested at set intervals. Based on the relevant indicators tested at the current set time, severe indicators and routine indicators are screened, and the first weight of severe indicators and the second weight of routine indicators are obtained, thereby calculating the dizziness risk assessment value at the current set time. S2. Set the dizziness hazard assessment level, specifically: set a first dizziness hazard assessment threshold and a second dizziness hazard assessment threshold, where the first dizziness hazard assessment threshold is less than the second dizziness hazard assessment threshold; when the dizziness hazard assessment value is less than the first dizziness hazard assessment threshold, it indicates a low-level hazard; when the dizziness hazard assessment value is greater than or equal to the first dizziness hazard assessment threshold and less than or equal to the second dizziness hazard assessment threshold, it indicates a medium-level hazard; when the dizziness hazard assessment value is greater than the second dizziness hazard assessment threshold, it indicates a high-level hazard. S3. Based on the number of severe indicators and the number of regular indicators in the previous set time, adjust the first weight and the second weight for the next set time. Calculate the dizziness risk assessment value for the next set time based on the adjusted first weight and second weight. Update the first dizziness risk assessment threshold and the second dizziness risk assessment threshold for the next set time, and conduct a dizziness risk assessment for the next set time.

[0007] Furthermore, in step S1, the dizziness risk assessment value is specifically calculated using the following formula: ; In the above formula, This represents the dizziness risk assessment value at the i-th set time. This represents the first weight at the i-th set time. This represents the first critical indicator deviation value at the i-th set time. Represents the i-th set time. One serious indicator deviation value, This represents the second weight at the i-th set time. This represents the deviation value of the first regular indicator at the i-th set time. Represents the i-th set time. Deviation values ​​of common indicators This represents the total number of critical indicators. This represents the total number of regular indicators.

[0008] Furthermore, and Satisfy the following formula: ; In the above formula, N represents the total number of relevant indicators.

[0009] Furthermore, both critical and routine indicators are selected from relevant value indicators, specifically through the following method: Set the normal range for relevant indicators Severity range of relevant indicators ,in This represents the minimum value within the normal range of the relevant indicator. This indicates the maximum value within the normal range of the relevant indicator. This indicates that medical attention is needed when the relevant indicators are below this value. This indicates that medical attention is needed when the relevant indicators are higher than this value; When the relevant index at the i-th set time is less than or equal to or greater than or equal to When the condition is normal, the relevant indicator is considered a critical indicator; otherwise, it is considered a normal indicator. When the relevant indicator is within the normal range, the deviation value is 0; otherwise, the deviation value of the relevant indicator is obtained by subtracting the current relevant indicator from its nearest maximum or minimum value.

[0010] Furthermore, relevant indicators include heart rate, heart rhythm, blood pressure, blood oxygen saturation, respiratory rate, body temperature, ST segment, and T wave; for the ST segment, its corresponding... , Both are downward shift values. , All are elevated values; for the T wave, its corresponding , , , All are amplitude values.

[0011] Furthermore, , , , Based on the analysis of previous test data of dizzy patients, the following results were obtained: We obtained relevant indicators from all previous dizziness patients, and classified and stored the corresponding data according to different indicators to form heart rate dataset, heart rhythm dataset, blood oxygen saturation dataset, respiratory rate dataset, body temperature dataset, ST segment dataset, and T wave dataset. Each dataset includes a dataset in the dizziness state and a dataset in the non-dizziness state. For different patients, the data in the dizziness state dataset and the non-dizziness state dataset are set as different sample data. For each relevant indicator, the lower and upper quartiles were obtained by applying the quartile method to the data set of the corresponding dizziness state. The lower quartile was set as... The upper 1 / 4 quantile obtained is The lower and upper quartiles were obtained by applying the quartile method to the data in the corresponding dataset under non-dizzy conditions. The lower quartile was set as... The upper 1 / 4 quantile obtained is What was obtained , , , Need to meet and If the conditions are not met, iterative training is performed by successively reducing the amount of data in the dataset under dizziness and the dataset under non-dizziness conditions until the above conditions are met.

[0012] Furthermore, in step S2, the first and second dizziness risk assessment thresholds are dynamically set based on relevant indicators at each set time, specifically expressed by the following formula: ; ; In the above formula, This represents the threshold for assessing the first risk of dizziness at the i-th set time. This represents the threshold for assessing the second risk of dizziness at the i-th set time. Represents the J-th relevant indicator , Represents the J-th relevant indicator J takes values ​​from 1 to N.

[0013] Furthermore, in step S3, while adjusting the first and second weights for the next set time, the update is performed. , , , And based on the adjusted first weight, second weight, and various relevant indicators , , , Calculate the risk assessment value for dizziness at the next set time.

[0014] Furthermore, in step S3, the first and second weights for the next set time are adjusted based on the number of critical indicators and the number of regular indicators at the previous set time. The specific method is as follows: (1) When When the time is set, the first weight of the next set time is increased by 0.01, and the second weight of the next set time is decreased by 0.01; (2) When At that time, the values ​​of the first and second weights remain unchanged for the next set time. (3) When When the time is set, decrease the first weight of the next set time by 0.01 and increase the second weight of the next set time by 0.01; When adjusting the first and second weights for each set time period, the first weight shall be no less than 0.7 and no more than 0.9, and the second weight shall be no less than 0.1 and no more than 0.3. If the first weight is less than 0.7 or greater than 0.9 after adjustment, the first weight shall not be adjusted. If the second weight is less than 0.1 or greater than 0.3 after adjustment, the second weight shall not be adjusted.

[0015] A system for analyzing potential risks in the care of dizzy patients includes a first module, a second module, and a third module. The first module is used to execute the contents of step S1, the second module is used to execute the contents of step S2, and the third module is used to execute the contents of step S3.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention acquires data on dizziness-related indicators at set intervals and calculates a dizziness risk assessment value. Based on the data of relevant indicators at each set time, it dynamically obtains the corresponding dizziness risk assessment level, making the dizziness risk assessment level determined at each set time more accurate. Furthermore, in the calculation of the dizziness risk assessment value at the next set time, the first and second weights are dynamically adjusted to make the calculated dizziness risk assessment value more accurate. This provides accurate and objective feedback on the patient's dizziness risk status, effectively improving the accuracy and objectivity of the analysis of dizziness patient care risks. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0019] like Figure 1 As shown, the present invention provides a method for analyzing potential nursing risks in patients with dizziness, including the following steps: S1. Throughout the nursing cycle, relevant indicators need to be monitored at set intervals. These indicators include heart rate, heart rhythm, blood pressure, blood oxygen saturation, respiratory rate, body temperature, ST segment, and T wave. Based on the relevant indicators monitored at the current set time, the dizziness risk assessment value for that time is calculated using the following formula: ; ; In the above formula, This represents the dizziness risk assessment value at the i-th set time. This represents the first weight at the i-th set time. This represents the first critical indicator deviation value at the i-th set time. Represents the i-th set time. One serious indicator deviation value, This represents the second weight at the i-th set time. This represents the deviation value of the first regular indicator at the i-th set time. Represents the i-th set time. There are several deviation values ​​for common indicators, where N represents the total number of relevant indicators. This represents the total number of critical indicators. This represents the total number of regular indicators.

[0020] The first weight is greater than the second weight. For the first set time, the first weight is selected as a value between 0.8 and the second weight is selected as a value between 0.2 and 1. The first weight plus the second weight must be equal to 1. The first weight and the second weight must be dynamically adjusted every set time.

[0021] Among them, severe indicators and routine indicators are selected from relevant indicators. The specific judgment method is as follows: set the normal range of relevant indicators. Severity range of relevant indicators ,in This represents the minimum value within the normal range of the relevant indicator. This indicates the maximum value within the normal range of the relevant indicator. This indicates that medical attention is needed when the relevant indicators are below this value. This indicates that medical attention is needed when the relevant indicator is higher than this value; when the relevant indicator at the i-th set time is less than or equal to this value... or greater than or equal to When the relevant indicator is at a critical point, it is considered a severe indicator; otherwise, it is considered a normal indicator. When the relevant indicator is within the normal range, the deviation value is 0. Otherwise, the deviation value of the relevant indicator is obtained by subtracting the current relevant indicator from its nearest maximum or minimum value. For example, if the current relevant indicator is closest to and less than the minimum value in the normal range, it is considered a normal indicator. The deviation value is obtained by subtracting the current relevant indicator from the minimum value in the normal range.

[0022] For segment ST, its corresponding , Both are downward shift values. , All are elevated values. For the T wave, its corresponding... , , , All are amplitude values.

[0023] , , , , , Based on analysis of previous dizziness patient data, the following methods were used: Relevant indicators from all previous dizziness patients were collected and categorized according to these indicators, forming datasets for heart rate, heart rhythm, blood oxygen saturation, respiratory rate, body temperature, ST segment, and T wave. Each dataset includes data from patients in both dizzy and non-dizzy states. For each patient, different samples were assigned to the dizzy and non-dizzy state datasets. For each relevant indicator, the lower and upper quartiles were obtained by applying the quartile method to the corresponding dizziness state datasets. The lower quartile was then set as... The upper 1 / 4 quantile obtained is The lower and upper quartiles were obtained by applying the quartile method to the data in the corresponding dataset under non-dizzy conditions. The lower quartile was set as... The upper 1 / 4 quantile obtained is What was obtained , , , Need to meet and If the conditions are not met, iterative training is performed by successively reducing the amount of data in the dataset under dizziness and the dataset under non-dizziness conditions until the above conditions are met.

[0024] S2. Set dizziness risk assessment levels, specifically: set a first dizziness risk assessment threshold and a second dizziness risk assessment threshold. The first dizziness risk assessment threshold is lower than the second dizziness risk assessment threshold. When the dizziness risk assessment value is lower than the first dizziness risk assessment threshold, it indicates a low-level risk. When the dizziness risk assessment value is greater than or equal to the first dizziness risk assessment threshold and less than or equal to the second dizziness risk assessment threshold, it indicates a medium-level risk. When the dizziness risk assessment value is greater than the second dizziness risk assessment threshold, it indicates a high-level risk. For low-level risks, no risk warning is given; for medium-level risks, a risk warning is given, prompting staff to pay attention to the patient's relevant indicators; for high-level risks, a risk warning is given, mandating that staff pay attention to the patient's relevant indicators.

[0025] The thresholds for assessing the first and second risks of dizziness are dynamically set based on relevant indicators at each set time, and are specifically expressed by the following formula: ; ; In the above formula, This represents the threshold for assessing the first risk of dizziness at the i-th set time. This represents the threshold for assessing the second risk of dizziness at the i-th set time. Represents the J-th relevant indicator , Represents the J-th relevant indicator J takes values ​​from 1 to N.

[0026] S3. Based on the number of critical indicators and the number of regular indicators at the previous set time, adjust the first and second weights for the next set time. Update the values ​​of each relevant indicator detected at the previous set time. , , , And based on the adjusted first weight, second weight, and various relevant indicators , , , Calculate the dizziness risk assessment value for the next set time, and update the first dizziness risk assessment threshold and the second dizziness risk assessment threshold for the next set time, and conduct a dizziness risk assessment for the next set time.

[0027] Based on the number of critical indicators and the number of regular indicators in the previous time setting, adjust the first and second weights for the next time setting, as follows: (1) When When the time is set, the first weight of the next set time is increased by 0.01, and the second weight of the next set time is decreased by 0.01.

[0028] (2) When At that time, the values ​​of the first weight and the second weight remain unchanged for the next set time.

[0029] (3) When When the time is set, decrease the first weight of the next set time by 0.01 and increase the second weight of the next set time by 0.01.

[0030] When adjusting the first and second weights for each set time period, the first weight shall be no less than 0.7 and no more than 0.9, and the second weight shall be no less than 0.1 and no more than 0.3. If the first weight is less than 0.7 or greater than 0.9 after adjustment, the first weight shall not be adjusted. If the second weight is less than 0.1 or greater than 0.3 after adjustment, the second weight shall not be adjusted.

[0031] The present invention also provides a nursing risk analysis system for dizziness patients, including a first module, a second module and a third module. The first module is used to execute the contents of step S1, the second module is used to execute the contents of step S2, and the third module is used to execute the contents of step S3.

[0032] This invention acquires data on dizziness-related indicators at set intervals and calculates a dizziness risk assessment value. Based on the data of relevant indicators at each set time, it dynamically obtains the corresponding dizziness risk assessment level, making the dizziness risk assessment level determined at each set time more accurate. Furthermore, in the calculation of the dizziness risk assessment value at the next set time, the first and second weights are dynamically adjusted to make the calculated dizziness risk assessment value more accurate. This provides accurate and objective feedback on the patient's dizziness risk status, effectively improving the accuracy and objectivity of the analysis of dizziness patient care risks.

[0033] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions made by those skilled in the art to the technical solution of the present invention do not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A method for analyzing potential nursing risks in patients with dizziness, characterized in that, Includes the following steps: S1. During the entire nursing cycle, relevant indicators are tested at set intervals. Based on the relevant indicators tested at the current set time, severe indicators and routine indicators are screened, and the first weight of severe indicators and the second weight of routine indicators are obtained, thereby calculating the dizziness risk assessment value at the current set time. S2. Set the dizziness hazard assessment level, specifically: set a first dizziness hazard assessment threshold and a second dizziness hazard assessment threshold, where the first dizziness hazard assessment threshold is less than the second dizziness hazard assessment threshold; when the dizziness hazard assessment value is less than the first dizziness hazard assessment threshold, it indicates a low-level hazard; when the dizziness hazard assessment value is greater than or equal to the first dizziness hazard assessment threshold and less than or equal to the second dizziness hazard assessment threshold, it indicates a medium-level hazard; when the dizziness hazard assessment value is greater than the second dizziness hazard assessment threshold, it indicates a high-level hazard. S3. Based on the number of severe indicators and the number of regular indicators in the previous set time, adjust the first weight and the second weight for the next set time. Calculate the dizziness risk assessment value for the next set time based on the adjusted first weight and second weight. Update the first dizziness risk assessment threshold and the second dizziness risk assessment threshold for the next set time, and conduct a dizziness risk assessment for the next set time.

2. The method for analyzing potential nursing risks in dizzy patients according to claim 1, characterized in that, In step S1, the dizziness risk assessment value is calculated using the following formula: ； In the above formula, This represents the dizziness risk assessment value at the i-th set time. This represents the first weight at the i-th set time. This represents the first critical indicator deviation value at the i-th set time. Represents the i-th set time. One serious indicator deviation value, This represents the second weight at the i-th set time. This represents the deviation value of the first regular indicator at the i-th set time. Represents the i-th set time. Deviation values ​​of common indicators This represents the total number of critical indicators. This represents the total number of regular indicators.

3. The method for analyzing potential nursing risks in dizzy patients according to claim 2, characterized in that, and Satisfy the following formula: ； In the above formula, N represents the total number of relevant indicators.

4. The method for analyzing potential nursing risks in dizzy patients according to claim 2, characterized in that, Both critical and routine indicators are selected from relevant value indicators, and the specific method is as follows: Set the normal range for relevant indicators Severity range of relevant indicators ,in This represents the minimum value within the normal range of the relevant indicator. This indicates the maximum value within the normal range of the relevant indicator. This indicates that medical attention is needed when the relevant indicators are below this value. This indicates that medical attention is needed when the relevant indicator is higher than this value; When the relevant index at the i-th set time is less than or equal to or greater than or equal to When the condition is normal, the relevant indicator is considered a critical indicator; otherwise, it is considered a normal indicator. When the relevant indicator is within the normal range, the deviation value is 0; otherwise, the deviation value of the relevant indicator is obtained by subtracting the current relevant indicator from its nearest maximum or minimum value.

5. The method for analyzing potential nursing risks in dizzy patients according to claim 4, characterized in that, Relevant indicators include heart rate, heart rhythm, blood pressure, blood oxygen saturation, respiratory rate, body temperature, ST segment, and T wave; for the ST segment, its corresponding... , Both are downward shift values. , All are elevated values; for the T wave, the corresponding , , , All are amplitude values.

6. The method for analyzing potential nursing risks in dizzy patients according to claim 5, characterized in that, , , , Based on the analysis of previous test data of dizzy patients, the following results were obtained: We obtained relevant indicators from all previous dizziness patients, and classified and stored the corresponding data according to different indicators to form heart rate dataset, heart rhythm dataset, blood oxygen saturation dataset, respiratory rate dataset, body temperature dataset, ST segment dataset, and T wave dataset. Each dataset includes a dataset in the dizziness state and a dataset in the non-dizziness state. For different patients, the data in the dizziness state dataset and the non-dizziness state dataset are set as different sample data. For each relevant indicator, the lower and upper quartiles were obtained by applying the quartile method to the data set of the corresponding dizziness state. The lower quartile was set as... The upper 1 / 4 quantile obtained is The lower and upper quartiles were obtained by applying the quartile method to the data in the corresponding dataset under non-dizzy conditions. The lower quartile was set as... The upper 1 / 4 quantile obtained is What was obtained , , , Need to meet and If the conditions are not met, iterative training is performed by successively reducing the amount of data in the dataset under dizziness and the dataset under non-dizziness conditions until the above conditions are met.

7. The method for analyzing potential nursing risks in dizzy patients according to claim 4, characterized in that, In step S2, the first and second dizziness risk assessment thresholds are dynamically set based on relevant indicators at each set time, specifically expressed by the following formula: ； ； In the above formula, This represents the threshold for assessing the first risk of dizziness at the i-th set time. This represents the threshold for assessing the second risk of dizziness at the i-th set time. Represents the J-th relevant indicator , Represents the J-th relevant indicator J takes values ​​from 1 to N.

8. The method for analyzing potential nursing risks in dizzy patients according to claim 4, characterized in that, In step S3, while adjusting the first and second weights for the next set time, update... , , , And based on the adjusted first weight, second weight, and various relevant indicators , , , Calculate the risk assessment value for dizziness at the next set time.

9. The method for analyzing potential nursing risks in dizzy patients according to claim 2, characterized in that, In step S3, the first and second weights for the next set time are adjusted based on the number of critical indicators and the number of regular indicators at the previous set time. The specific method is as follows: (1) When When the time is set, the first weight of the next set time is increased by 0.01, and the second weight of the next set time is decreased by 0.01; (2) When At that time, the values ​​of the first and second weights remain unchanged for the next set time. (3) When When the time is set, decrease the first weight of the next set time by 0.01 and increase the second weight of the next set time by 0.01; When adjusting the first and second weights for each set time period, the first weight must be no less than 0.7 and no more than 0.9, and the second weight must be no less than 0.1 and no more than 0.

3. If the adjusted first weight is less than 0.7 or greater than 0.9, the first weight will not be adjusted again. If the second weight is less than 0.1 or greater than 0.3 after adjustment, no further adjustment will be made.

10. A system for analyzing potential risks in the care of dizzy patients, characterized in that, The method for analyzing potential nursing risks in dizzy patients according to any one of claims 1-9 includes a first module, a second module, and a third module. The first module is used to perform the content of step S1, the second module is used to perform the content of step S2, and the third module is used to perform the content of step S3.

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