A method and system for intelligently extracting key patient information

Abstract

The present invention relates to the field of data processing, and specifically to an intelligent extraction method and system for key patient information, which uses motion data collected by intelligent wearable devices to identify the user's exercise status and exercise intensity, and when the exercise intensity reaches a certain value, the patient's current exercise duration is accumulated. At the same time, the user's vital sign data is collected in real time, and the reference vital sign data matching the patient is determined from the vital sign reference database in combination with the current exercise intensity, and the similarity between the measured vital sign data and the reference vital sign data is calculated. If the similarity is too low, it can be inferred that the patient may be uncomfortable during exercise. At this time, a pre-constructed question template is used to execute the question-and-answer process to find the symptom entities that may occur in the patient during exercise. Finally, the accumulated exercise duration, symptom entity, measured vital sign data, and exercise intensity data are all extracted as key data to provide to doctors, so that doctors have relevant data to refer to and judge the patient's recovery.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method and system for intelligently extracting key patient information. Background Art

[0002] Patients usually have an observation period after receiving treatment. For hospitalized patients, they will be observed by medical staff or professional instruments and equipment to assess their recovery.

[0003] However, for patients who are recovering at home, if they want to understand their recovery status, they can only rely on regular physical examinations and the patients' own descriptions. During this period, doctors cannot understand the patient's recovery status by observing the patient's physical signs. If the condition worsens, due to the lack of data collection methods, doctors cannot intervene in time, resulting in a prolonged recovery period or even difficulty in recovery. Summary of the invention

[0004] In view of this, an object of the present invention is to provide a method and system for intelligently extracting key patient information to solve the problems in the background technology.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The present invention provides an intelligent method for extracting key patient information, comprising the steps of:

[0007] Acquire the patient's identity information, and the patient's measured vital sign data and motion data in a target time period, wherein the measured vital sign data and the motion data are collected by the patient's smart wearable device, and the target time period is a time period of a target duration before the current time point;

[0008] Calculating the patient's current exercise intensity based on the patient's identity information and the exercise data of the target time period; and when the current exercise intensity is greater than a set value, adding the target duration to the accumulated exercise duration of this exercise;

[0009] Determining target reference physical sign data of the patient within the target time period based on the patient's current exercise intensity, the patient's identity information, and a pre-built physical sign reference database, wherein the physical sign reference database includes reference physical sign data of multiple user groups when performing exercises of various intensities;

[0010] Calculating the similarity between the measured physical sign data and the reference physical sign data; when the similarity is less than a preset similarity threshold, executing a question-and-answer process with the patient, wherein the question-and-answer process is used to extract symptom entities;

[0011] The key information of the patient's recovery period is constructed based on the patient's cumulative exercise time, the current exercise intensity, the measured physical sign data of the target time period, and the symptom entity.

[0012] In one embodiment of the present application, obtaining the patient's motion data in a target time period includes:

[0013] Obtain the current motion type and three-axis acceleration data collected by the smart wearable device, wherein the three-axis acceleration data includes the three-axis acceleration at multiple time points ;

[0014] The three-axis acceleration data are filtered respectively to obtain three-axis acceleration filtering data, wherein the three-axis acceleration filtering data includes the three-axis acceleration at multiple time points after filtering. ;

[0015] The three-axis accelerations at the multiple time points Combine them to get the total acceleration ;

[0016] The combined acceleration based on multiple time points Draw the acceleration time domain variation waveform, extract all peak points in the acceleration time domain variation waveform, and calculate the standard deviation of all peak points ;

[0017] Perform fast Fourier transform on the acceleration time domain variation waveform to obtain the acceleration frequency domain variation waveform, and extract the main frequency component from the acceleration frequency domain variation waveform ;

[0018] Based on the standard deviation and the main frequency component Construct motion data.

[0019] In one embodiment of the present application, the current exercise intensity of the patient is calculated based on the patient's identity information and the exercise data of the target time period, including:

[0020] Determining a target weight sequence based on the current exercise type, the patient's identity information, and a pre-constructed exercise type-weight sequence relationship table, wherein the exercise type-weight sequence relationship table includes weight sequences for various user groups when performing various exercise types;

[0021] The exercise data of the target time period is weighted based on the target weight sequence to obtain the current exercise intensity. , wherein the current exercise intensity The mathematical expression is:

[0022]

[0023] In the formula, is the first weight, is the second weight.

[0024] In one embodiment of the present application, the process of constructing the exercise type-weight sequence relationship table includes:

[0025] Acquire a plurality of sample data, wherein the sample data includes user information of a normal user, a type of exercise, actual measured physical sign sample data and exercise sample data sampled when the normal user exercises within a target duration;

[0026] Based on the measured physical sign sample data, multiple sample data The exercise intensity is marked, and the measured physical sign sample data is normalized to obtain multiple sample data containing labels , wherein the measured physical sign sample data includes heart rate and blood pressure;

[0027] Dividing a plurality of sample data containing labels into a plurality of data units based on the user information;

[0028] Classifying the data unit based on the motion type to obtain a plurality of data sub-units;

[0029] Clustering multiple sample data in the data subunit based on motion intensity to obtain multiple intensity clusters;

[0030] The mean value of the motion intensity, the mean value of the standard deviation of the acceleration peak value, and the mean value of the main frequency component of the acceleration frequency domain change waveform in each intensity cluster are calculated; and the mathematical expression of the motion intensity is constructed based on the mean value of the motion intensity, the mean value of the standard deviation of the acceleration peak value, and the mean value of the main frequency component of the acceleration frequency domain change waveform:

[0031]

[0032] In the formula, For the The mean motion intensity of each intensity cluster, For the The standard deviation of the peak acceleration of each intensity cluster, For the The mean value of the main frequency component of the acceleration frequency domain variation waveform of each intensity cluster;

[0033] The mathematical expressions of any two exercise intensities are combined into a system of equations, and the first weight and the second weight To solve, when any data subunit has multiple different first weights Or multiple different second weights When multiple different first weights Or multiple different second weights Find the average and get the updated first weight and the updated second weight ;

[0034] Based on the first weight of each data subunit and the second weight Construct the movement type-weight sequence relationship table.

[0035] In one embodiment of the present application, the process of constructing the vital sign reference database includes:

[0036] Clustering multiple sample data in each data subunit based on heart rate and blood pressure to obtain multiple physical sign clusters;

[0037] Eliminate sample data with abnormal exercise intensity from the physical sign cluster to obtain a screened physical sign cluster;

[0038] Calculate the mean of the exercise intensity of multiple sample data in the screened physical sign cluster , standard deviation of exercise intensity , heart rate average , heart rate standard deviation , mean blood pressure and blood pressure standard deviation ;

[0039] Based on the mean exercise intensity , standard deviation of exercise intensity Exercise intensity range for constructing sign clusters ; Based on the heart rate mean and heart rate standard deviation Constructing heart rate ranges for vital signs clusters ; and based on the mean blood pressure and the standard deviation of blood pressure Constructing blood pressure ranges for sign clusters , where is the range adjustment parameter;

[0040] A physical sign reference database is constructed based on heart rate ranges and blood pressure ranges in multiple exercise intensity ranges.

[0041] In one embodiment of the present application, the measured vital sign data includes measured heart rate and measured blood pressure at multiple time points, and calculating the similarity between the measured vital sign data and the reference vital sign data includes:

[0042] Normalize the measured heart rate and blood pressure at multiple time points respectively, and calculate the measured heart rate at multiple time points after normalization The average , Standard Deviation , and calculate the measured blood pressure at multiple time points after normalization The average , Standard Deviation ;

[0043] Construct the heart rate distribution range for the target time period And blood pressure distribution range ,in, is the range adjustment parameter;

[0044] The similarity is calculated based on the heart rate reference range and blood pressure reference range in the reference vital sign data, and the similarity includes the heart rate similarity Similarity to blood pressure , wherein the heart rate similarity Similarity to the blood pressure The mathematical expression is:

[0045]

[0046] In the formula, is the length of the heart rate reference range, is the length of the overlapping interval between the heart rate reference range and the heart rate distribution range, is the length of the blood pressure reference range, It is the length of the overlapping interval between the blood pressure reference range and the blood pressure distribution range.

[0047] In one embodiment of the present application, executing a question-and-answer process with the patient includes:

[0048] Sending a pre-built question template to the patient, wherein the question template is built based on possible symptom entities;

[0049] When feedback information is received from the patient, the question-and-answer process is completed; when no feedback information is received from the patient, the question-and-answer process returns to sending the pre-built question template to the patient until feedback information is received from the patient, or the number of times the question template is sent exceeds a preset threshold, the question-and-answer process is completed.

[0050] In one embodiment of the present application, when the similarity is less than a preset similarity threshold, executing a question-and-answer process with the patient includes:

[0051] The heart rate similarity Less than the preset similarity threshold or the blood pressure similarity When the similarity is less than a preset threshold, the question-and-answer process with the patient is executed.

[0052] In one embodiment of the present application, it also includes:

[0053] The key information of the patient's recovery period is sent to the target object.

[0054] The present application also provides an intelligent extraction system for key patient information, including:

[0055] An acquisition module, used to acquire the patient's measured physical sign data and motion data in a target time period, wherein the measured physical sign data and the motion data are collected by the patient's smart wearable device, and the target time period is a time period of a target duration before the current time point;

[0056] An intensity calculation module, for calculating the current exercise intensity of the patient based on the exercise data of the target time period; and when the current exercise intensity is greater than a set value, adding the target duration to the cumulative exercise duration of this exercise;

[0057] A reference physical sign determination module, configured to determine target reference physical sign data of the patient within the target time period based on the patient's current exercise intensity, the patient's identity information, and a pre-built physical sign reference database, wherein the physical sign reference database includes reference physical sign data of multiple user groups when performing exercises of various intensities;

[0058] A question-and-answer module, used for calculating the similarity between the measured physical sign data and the reference physical sign data; when the similarity is less than a preset similarity threshold, executing a question-and-answer process with the patient, wherein the question-and-answer process is used for extracting symptom entities;

[0059] The information extraction module is used to construct the key information of the patient's recovery period based on the patient's cumulative exercise time, the current exercise intensity, the measured physical sign data of the target time period, and the symptom entity.

[0060] The beneficial effects of the present invention are as follows: the intelligent extraction method and system of the key information of a patient of the present invention identifies the user's exercise status and exercise intensity through the exercise data collected by the intelligent wearable device, and accumulates the patient's current exercise time when the exercise intensity reaches a certain value. At the same time, the user's vital sign data is collected in real time, and the reference vital sign data matching the patient is determined from the vital sign reference database in combination with the current exercise intensity, and the similarity between the measured vital sign data and the reference vital sign data is calculated. If the similarity is too low, it can be inferred that the patient may be uncomfortable during exercise. At this time, the pre-constructed question template is used to execute the question-and-answer process to find the symptom entities that may occur during exercise. Finally, the accumulated exercise time, symptom entity, measured vital sign data and exercise intensity data are all extracted as key data to provide to the doctor, so that the doctor has relevant data to refer to and judge the patient's recovery. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0062] Figure 1 This is an application scenario diagram of an intelligent extraction method of key patient information shown in an embodiment of the present application;

[0063] Figure 2 is a flow chart of a method for intelligently extracting key patient information shown in one embodiment of the present application;

[0064] Figure 3 A flowchart for constructing the exercise type-weight sequence relationship table and the physical sign reference database in this application;

[0065] Figure 4 It is a structural diagram of an intelligent extraction system for key patient information shown in one embodiment of the present application. DETAILED DESCRIPTION

[0066] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0067] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show the layers related to the present invention rather than being drawn according to the number, shape and size of the layers in actual implementation. In actual implementation, the type, quantity and proportion of each layer may be changed arbitrarily, and the layer layout may also be more complicated.

[0068] In the following description, numerous details are discussed to provide a more thorough explanation of embodiments of the present invention; however, it is apparent to one skilled in the art that embodiments of the present invention may be practiced without these specific details.

[0069] Figure 1 is an application scenario diagram of an intelligent extraction method of key patient information shown in an embodiment of the present application, such as Figure 1As shown, in this application, smart wearable devices, such as smart bracelets, are used to collect the user's motion data (such as three-axis acceleration, body tilt angle, etc.) and vital sign data (such as heart rate and blood pressure). Limited by the data processing capabilities of smart bracelets, this application uploads the collected data to the cloud for calculation and data processing. In order to identify the user's exercise intensity, cumulative exercise time, and extract the user's exercise intensity, cumulative exercise time, measured vital sign data and other data and provide them to the target object as basic data for judging the user's physical recovery.

[0070] In addition, the information of the relevant persons involved in this application has been obtained with full consent and authorization, and the collection, use and processing of relevant information must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0071] Figure 2 is a flow chart of a method for intelligently extracting key patient information shown in one embodiment of the present application. Figure 2 As shown, a method for intelligently extracting key patient information in this embodiment may include the following steps:

[0072] S210, obtaining the patient's identity information, and the patient's measured physical sign data and motion data in a target time period, wherein the measured physical sign data and the motion data are collected by the patient's smart wearable device, and the target time period is a time period of a target duration before the current time point;

[0073] Specifically, identity information mainly includes age and gender; the physical sign data of people of different ages and genders during exercise are large, so they need to be distinguished.

[0074] The measured vital sign data include the measured heart rate and the measured blood pressure.

[0075] Motion data mainly includes motion type data and three-axis acceleration data collected by smart wearable devices. The acquisition process is as follows:

[0076] S211, obtaining the current motion type and three-axis acceleration data collected by the smart wearable device, wherein the three-axis acceleration data includes the three-axis acceleration at multiple time points ;

[0077] The three-axis acceleration data generated by users in different sports types correspond to different sports intensities. For example, when cycling and running, similar three-axis acceleration data often correspond to different sports intensities. Therefore, it is necessary to use the sports type recognition function of the smart bracelet to identify the user's current sports type.

[0078] Smart bracelets are usually equipped with a variety of sensors, such as accelerometers, gyroscopes, heart rate sensors, etc. These sensors can monitor the user's various physiological parameters and movement status in real time. For example, the accelerometer can detect the user's movement direction and speed changes; the gyroscope can sense the direction and rotation of the wrist. In the prior art, the user's exercise type can be identified by identifying data such as cadence, stride, body tilt angle, arm swing pattern, etc.

[0079] Three-axis acceleration is an important basic data for user exercise intensity.

[0080] S212, filtering the three-axis acceleration data respectively to obtain three-axis acceleration filtering data, wherein the three-axis acceleration filtering data includes the three-axis acceleration at multiple time points after filtering. ;

[0081] This application uses low-pass filtering to smooth the signal, the purpose of which is to remove noise and smooth the signal to facilitate the extraction of useful motion information.

[0082] S213: The three-axis accelerations at the multiple time points are calculated. Combine them to get the total acceleration ;

[0083]

[0084] The combined acceleration is one-dimensional data. Converting three-dimensional data into one-dimensional data can effectively simplify the data dimension and facilitate subsequent data processing.

[0085] S214, based on the combined acceleration at multiple time points Draw the acceleration time domain variation waveform, extract all peak points in the acceleration time domain variation waveform, and calculate the standard deviation of all peak points ;

[0086] The peak point reflects the characteristics of the waveform, including amplitude characteristics and frequency characteristics. Calculating the standard deviation of the peak point amplitude can reflect the stability of acceleration changes.

[0087] S215, performing a fast Fourier transform on the acceleration time domain variation waveform to obtain an acceleration frequency domain variation waveform, and extracting a main frequency component from the acceleration frequency domain variation waveform ;

[0088] By extracting the main frequency component of the acceleration frequency domain change waveform, the main frequency of the acceleration change can be obtained to reflect the speed of the acceleration change.

[0089] S216, based on the standard deviation and the main frequency component Construct motion data.

[0090] This application mainly uses the stability of acceleration changes and the speed of acceleration changes to reflect the intensity of exercise, so the main extraction standard deviation and the main frequency component .

[0091] This application adopts a real-time acquisition method, that is, once the data extraction process is triggered, the measured vital sign data and motion data at multiple time points in the target time period before the current time point are used as basic data for analysis, processing and extraction. The trigger condition can be a timed trigger or an active trigger.

[0092] S220, calculating the current exercise intensity of the patient based on the patient's identity information and the exercise data of the target time period; and when the current exercise intensity is greater than a set value, adding the target duration to the accumulated exercise duration of this exercise;

[0093] In this application, the patient's exercise intensity is identified through exercise type data and three-axis acceleration data. In the prior art, data such as heart rate and blood pressure may be introduced to judge the patient's exercise intensity, but this application is aimed at patients in the recovery period, so during exercise, abnormal heart rate and blood pressure data are very likely to appear. Introducing heart rate and blood pressure will lead to problems in judging exercise intensity. Therefore, only exercise type data and three-axis acceleration data are used to identify the patient's exercise intensity. The specific process is as follows:

[0094] S221, determining a target weight sequence based on the current exercise type, the patient's identity information, and a pre-constructed exercise type-weight sequence relationship table, wherein the exercise type-weight sequence relationship table includes weight sequences of various user groups when performing various exercise types;

[0095] S222, weighting the exercise data of the target time period based on the target weight sequence to obtain the current exercise intensity , wherein the current exercise intensity The mathematical expression is:

[0096]

[0097] In the formula, is the first weight, is the second weight.

[0098] In order to more accurately describe the patient's exercise intensity, this application constructs different weight sequences in advance for different exercise types and user groups to adjust the standard deviation. and the main frequency component Perform weighted summation to obtain the current exercise intensity.

[0099] Figure 3 This is a flow chart for constructing the exercise type-weight sequence relationship table and the physical sign reference database in this application, such as Figure 3 As shown, specifically, the process of constructing the motion type-weight sequence relationship table in the present application includes:

[0100] (1) obtaining a plurality of sample data, wherein the sample data includes user information of a normal user, exercise type, actual measured physical sign sample data and exercise sample data sampled when the normal user exercises within a target duration;

[0101] The sample data is generated by normal users wearing smart bracelets when exercising, and is collected after authorization by the normal users.

[0102] (2) Based on the measured physical sign sample data, multiple sample data are The exercise intensity is marked, and the measured physical sign sample data is normalized to obtain multiple sample data containing labels , wherein the measured physical sign sample data includes heart rate and blood pressure;

[0103] Since the sample data is generated by normal users, their heart rate and blood pressure are of reference significance. It is more reasonable to use heart rate and blood pressure to mark exercise intensity. However, considering the individual differences between different users, normal user feedback can be used for marking when standardizing. For example, the exercise intensity ranges from 0 to 100, allowing users to evaluate the intensity of the exercise process themselves.

[0104] (3) dividing the plurality of sample data containing labels into a plurality of data units based on the user information;

[0105] (4) classifying the data unit based on the motion type to obtain a plurality of data sub-units;

[0106] Factors that affect the correspondence between heart rate, blood pressure and other physical data and exercise intensity mainly include age, gender, and exercise type. Therefore, this application first uses user information to divide multiple sample data containing labels into multiple data units, so that each data unit only contains sample data generated by a user group of a specific age and gender. Then the data units are divided again so that each data subunit only contains sample data generated when performing one type of exercise.

[0107] (5) Clustering multiple sample data in the data subunit based on motion intensity to obtain multiple intensity clusters;

[0108] The present application only clusters the exercise intensity, that is, clusters the one-dimensional data, and can use the K-means clustering algorithm to divide it into a fixed number of intensity clusters, such as 2-5 clusters.

[0109] (6) Calculate the mean value of the motion intensity, the mean value of the standard deviation of the acceleration peak value, and the mean value of the main frequency component of the acceleration frequency domain change waveform in each intensity cluster; and construct a mathematical expression for the motion intensity based on the mean value of the motion intensity, the mean value of the standard deviation of the acceleration peak value, and the mean value of the main frequency component of the acceleration frequency domain change waveform:

[0110]

[0111] In the formula, For the The mean motion intensity of each intensity cluster, For the The standard deviation of the peak acceleration of each intensity cluster, For the The mean value of the main frequency component of the acceleration frequency domain variation waveform of each intensity cluster;

[0112] For each intensity cluster, the distribution of its motion intensity is similar. Since the cluster contains sample data generated by a user group when performing the same type of exercise, its motion data is also similar. This application calculates the mean motion intensity, the mean acceleration peak standard deviation, and the mean of the main frequency component of the acceleration frequency domain change waveform within the cluster, thereby reflecting the relationship between motion intensity and motion data in each intensity cluster.

[0113] Since multiple clusters are divided in the previous text, multiple mathematical expressions of exercise intensity can be constructed. In this application, the mathematical expression of exercise intensity is a two-variable linear equation. By combining the two equations, the values ​​of the first weight and the second weight can be solved. In this application, any two mathematical expressions of exercise intensity are combined into a system of equations to calculate one or more solutions of the first weight and the second weight.

[0114] (7) The mathematical expressions of any two exercise intensities are combined into a system of equations, and the first weight and the second weight To solve, when any data subunit has multiple different first weights Or multiple different second weights When multiple different first weights Or multiple different second weights Find the average and get the updated first weight and the updated second weight ;

[0115] Finally, if there is only one set of solutions, the first weight and the second weight can be directly obtained. If there are multiple sets of solutions, their average values ​​can be calculated separately.

[0116] (8) Based on the first weight of each data subunit and the second weight Construct the movement type-weight sequence relationship table.

[0117] Through the above process, we can obtain the weight sequence corresponding to the exercise data of different user groups when performing different exercises, and bring it into the exercise data of the current user to obtain a more accurate exercise intensity that conforms to the labeling rules.

[0118] When the patient's exercise intensity in the current time period is obtained, if the exercise intensity is greater than a certain value, it is judged that the patient has high-intensity exercise. The continuous exercise time of high-intensity exercise can reflect the patient's physical recovery. Therefore, the continuous exercise time in the target time period is added to the cumulative time of this exercise. The key information is obtained: the cumulative exercise time.

[0119] S230, determining target reference physical sign data of the patient within the target time period based on the patient's current exercise intensity, the patient's identity information, and a pre-constructed physical sign reference database, wherein the physical sign reference database includes reference physical sign data of multiple user groups when performing exercises of various intensities;

[0120] After obtaining the current exercise intensity calculated from the exercise data, the present application also refers to the physical sign data of normal users and the physical sign data of the current patient to determine whether the patient has abnormal physical sign data. If the physical sign is abnormal, whether it is accompanied by abnormal symptoms, these are the key information that needs to be extracted.

[0121] In this application, a pre-built physical sign reference database provides reference physical sign data of normal users when performing similar sports. The method for building the physical sign reference database is as follows:

[0122] like Figure 3 As shown, the construction of the physical sign reference database is based on the data subunits mentioned above.

[0123] (1) Clustering multiple sample data in each data subunit based on heart rate and blood pressure to obtain multiple physical sign clusters;

[0124] This application performs clustering based on heart rate and blood pressure, which can be regarded as clustering two-dimensional data, so the DSCAN density clustering algorithm is used. The degree of aggregation of data points and the number of clusters are controlled by setting the maximum distance within the cluster.

[0125] (2) Eliminating sample data with abnormal exercise intensity from the physical sign cluster to obtain a screened physical sign cluster;

[0126] This application uses a cyclic elimination method to eliminate abnormal sample data, specifically including:

[0127] The means and variances of exercise intensity within the sign clusters were calculated;

[0128] If the variance within the physical sign cluster is greater than the preset variance threshold, it means that the concentration of the exercise intensity data within the cluster is not enough. At this time, calculate the deviation between each data in the cluster and the mean, remove the sample data with the largest deviation, and return to calculating the mean and variance of the exercise intensity within the physical sign cluster until the variance is less than or equal to the variance threshold.

[0129] (3) Calculating the mean of the exercise intensity of multiple sample data in the screened physical sign cluster , standard deviation of exercise intensity , heart rate average , heart rate standard deviation , mean blood pressure and blood pressure standard deviation ;

[0130] After the physical sign clusters were eliminated in the previous article, the distribution characteristics of heart rate and blood pressure are similar, and the exercise intensity values ​​are also relatively concentrated. The exercise intensity within the physical sign cluster can be considered to have a corresponding relationship with the heart rate and blood pressure. In other words, when a normal user group performs a certain exercise, if the exercise intensity reaches the range within the cluster, then the corresponding heart rate and blood pressure will most likely fall into the corresponding heart rate and blood pressure range within the cluster.

[0131] (4) Based on the mean exercise intensity , standard deviation of exercise intensity Exercise intensity range for constructing sign clusters ; Based on the heart rate mean and heart rate standard deviation Constructing heart rate ranges for vital signs clusters ; and based on the mean blood pressure and the standard deviation of blood pressure Constructing blood pressure ranges for sign clusters , where is the range adjustment parameter;

[0132] (5) Construct a physical sign reference database based on heart rate ranges and blood pressure ranges in multiple exercise intensity ranges.

[0133] Finally, a range based on the mean and standard deviation was constructed to construct a reference database of physical signs.

[0134] In this application, the DSCAN clustering algorithm is used to obtain clusters, and then the motion intensity range is obtained. The constructed motion intensity range may have blind spots, that is, it cannot cover the area. In this case, the range adjustment parameters can be adjusted by adjusting the range adjustment parameters. To cover the blind spot.

[0135] After building the vital sign reference database, the corresponding vital sign reference data (range) can be found by collecting the patient's user information, exercise type and exercise intensity.

[0136] S240, calculating the similarity between the measured physical sign data and the reference physical sign data; when the similarity is less than a preset similarity threshold, executing a question-and-answer process with the patient, wherein the question-and-answer process is used to extract symptom entities;

[0137] In the present application, the similarity can reflect whether the patient's measured physical sign data is consistent with the corresponding reference physical sign data. If it is consistent, it means that the recovery is good. If there is a large discrepancy, it means that the recovery is not good and even abnormal symptoms may occur.

[0138] The similarity calculation process is as follows:

[0139] S241, normalizing the measured heart rate and the measured blood pressure at multiple time points respectively, and calculating the normalized measured heart rate at multiple time points The average , Standard Deviation , and calculate the measured blood pressure at multiple time points after normalization The average , Standard Deviation ;

[0140] This application uses Min-Max Normalization to eliminate the data differences between heart rate and blood pressure, which facilitates subsequent calculations and processing.

[0141] Then calculate the measured heart rate The average , Standard Deviation , measured blood pressure The average , Standard Deviation , to reflect the distribution of the patient's heart rate and blood pressure during the target time period.

[0142] S242: Constructing a heart rate distribution range for a target time period And blood pressure distribution range ,in, is the range adjustment parameter;

[0143] S243, calculating similarity based on the heart rate reference range and blood pressure reference range in the reference vital sign data, wherein the similarity includes heart rate similarity Similarity to blood pressure , wherein the heart rate similarity Similarity to the blood pressure The mathematical expression is:

[0144]

[0145] In the formula, is the length of the heart rate reference range, is the length of the overlapping interval between the heart rate reference range and the heart rate distribution range, is the length of the blood pressure reference range, It is the length of the overlapping interval between the blood pressure reference range and the blood pressure distribution range.

[0146] This application compares two ranges to obtain similarity. Indicates the length of the heart rate distribution range, Indicates the ratio of the length of the heart rate distribution range to the length of the heart rate reference range. Indicates the ratio of the length of the overlap interval between the heart rate reference range and the heart rate distribution range to the length of the heart rate reference range. Both parameters can reflect the similarity of the ranges.

[0147] In this application, , so that the reference range is larger, and the heart rate distribution range in the target time period is smaller. The advantage of this setting is that more reference data can be retained as much as possible, and the main heart rate data in the target time period can be extracted. The fault tolerance rate of abnormal judgment is increased to avoid an overly sensitive abnormal judgment mechanism.

[0148] After obtaining the similarity, if the similarity is too small, it means that the current patient's physical signs during exercise do not conform to the big data rules, and some symptoms may appear, such as feeling a strong or rapid heartbeat, shortness of breath or difficulty breathing, dizziness or mild headache, chest discomfort or pain, blurred vision or temporary vision loss, facial flushing or tinnitus, etc.

[0149] Specifically, in the heart rate similarity Less than the preset similarity threshold or the blood pressure similarity When the similarity is less than a preset threshold, the question-and-answer process with the patient is executed.

[0150] In order to confirm whether the patient has the above symptoms, this application constructs an inquiry template in advance, for example:

[0151] "Do you have any of the following symptoms: (1) feeling a strong or rapid heartbeat; (2) shortness of breath or difficulty breathing; (3) dizziness or mild headache; (4) chest discomfort or pain; (5) blurred vision or temporary vision loss; (6) facial flushing or tinnitus... If yes, please reply with the corresponding number."

[0152] The pre-constructed question template is then sent to the patient; upon receiving feedback from the patient, the question-answering process is completed; for example, by returning to (2) and (5), the corresponding symptom entities "shortness of breath or difficulty" and "blurred vision or temporary loss of vision" can be obtained.

[0153] When no feedback is received from the patient, the system returns to send the pre-built question template to the patient until feedback is received from the patient, or the number of times the question template is sent exceeds a preset threshold, the Q&A process is completed. If the user does not reply, the system will stop sending after three times, ending the communication and only extracting the vital sign data.

[0154] S250, constructing the key information of the patient's recovery period based on the patient's accumulated exercise time, the current exercise intensity, the measured physical sign data of the target time period, and the symptom entity.

[0155] Finally, the accumulated exercise time, the current exercise intensity, the measured physical sign data of the target time period and the symptom entity related to the recovery status are extracted as key information, and the key information of the patient's recovery period is sent to the target object. The target object (such as medical staff or relevant monitoring personnel) can understand the patient's recovery status in real time based on the key information, make timely suggestions or interventions, and optimize the patient's recovery process.

[0156] The present invention provides an intelligent method for extracting key patient information. The method uses motion data collected by an intelligent wearable device to identify the user's exercise status and exercise intensity, and when the exercise intensity reaches a certain value, the patient's current exercise duration is accumulated. At the same time, the user's vital sign data is collected in real time, and the reference vital sign data matching the patient is determined from the vital sign reference database in combination with the current exercise intensity, and the similarity between the measured vital sign data and the reference vital sign data is calculated. If the similarity is too low, it can be inferred that the patient may be uncomfortable during exercise. At this time, a pre-constructed question template is used to execute the question-and-answer process to find the symptom entities that may occur during exercise. Finally, the accumulated exercise duration, symptom entity, measured vital sign data, and exercise intensity data are all extracted as key data to provide to the doctor, so that the doctor has relevant data to refer to and judge the patient's recovery.

[0157] like Figure 4 As shown, the present application also provides an intelligent extraction system for key patient information, including:

[0158] An acquisition module, used to acquire the patient's measured physical sign data and motion data in a target time period, wherein the measured physical sign data and the motion data are collected by the patient's smart wearable device, and the target time period is a time period of a target duration before the current time point;

[0159] An intensity calculation module, for calculating the current exercise intensity of the patient based on the exercise data of the target time period; and when the current exercise intensity is greater than a set value, adding the target duration to the cumulative exercise duration of this exercise;

[0160] A reference physical sign determination module, configured to determine target reference physical sign data of the patient within the target time period based on the patient's current exercise intensity, the patient's identity information, and a pre-built physical sign reference database, wherein the physical sign reference database includes reference physical sign data of multiple user groups when performing exercises of various intensities;

[0161] A question-and-answer module, used for calculating the similarity between the measured physical sign data and the reference physical sign data; when the similarity is less than a preset similarity threshold, executing a question-and-answer process with the patient, wherein the question-and-answer process is used for extracting symptom entities;

[0162] The information extraction module is used to construct the key information of the patient's recovery period based on the patient's cumulative exercise time, the current exercise intensity, the measured physical sign data of the target time period, and the symptom entity.

[0163] The intelligent extraction system of a patient's key information of the present invention identifies the user's exercise status and exercise intensity through the exercise data collected by the intelligent wearable device, and accumulates the patient's current exercise time when the exercise intensity reaches a certain value. At the same time, the user's vital sign data is collected in real time, and the reference vital sign data matching the patient is determined from the vital sign reference database in combination with the current exercise intensity, and the similarity between the measured vital sign data and the reference vital sign data is calculated. If the similarity is too low, it can be inferred that the patient may have discomfort during exercise. At this time, the pre-constructed question template is used to execute the question-and-answer process to find the symptom entities that may appear in the patient during exercise. Finally, the accumulated exercise time, symptom entity, measured vital sign data and exercise intensity data are extracted as key data to provide to the doctor, so that the doctor has relevant data to refer to and judge the patient's recovery.

[0164] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0165] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes any one of the methods in this embodiment.

[0166] The computer-readable storage medium in this embodiment can be understood by ordinary technicians in this field: all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0167] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used to communicate, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes each step of the above method.

[0168] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0169] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0170] In the above-mentioned embodiments, although the present invention has been described in conjunction with the specific embodiments of the present invention, many replacements, modifications and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. The embodiments of the present invention are intended to cover all such replacements, modifications and variations falling within the broad scope of the appended claims.

[0171] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. An intelligent method for extracting key patient information, characterized in that: Includes steps: Obtain the patient's identity information, the patient's measured vital sign data and motion data in a target time period, wherein the measured vital sign data and the motion data are collected by the patient's smart wearable device, and the target time period is a time period of a target duration before the current time point; obtain the patient's motion data in the target time period, including: obtaining the current motion type and three-axis acceleration data collected by the smart wearable device, wherein the three-axis acceleration data includes the three-axis acceleration data at multiple time points ; Filter the three-axis acceleration data respectively to obtain three-axis acceleration filtering data, wherein the three-axis acceleration filtering data includes the three-axis acceleration at multiple time points after filtering ; The three-axis acceleration of the multiple time points Combine them to get the total acceleration ; Based on the combined acceleration at multiple time points Draw the acceleration time domain variation waveform, extract all peak points in the acceleration time domain variation waveform, and calculate the standard deviation of all peak points ; Perform fast Fourier transform on the acceleration time domain variation waveform to obtain the acceleration frequency domain variation waveform, and extract the main frequency component from the acceleration frequency domain variation waveform ; Based on the standard deviation and the main frequency component Constructing motion data; The current exercise intensity of the patient is calculated based on the patient's identity information and the exercise data of the target time period; and when the current exercise intensity is greater than a set value, the target duration is added to the cumulative exercise duration of this exercise; the current exercise intensity of the patient is calculated based on the patient's identity information and the exercise data of the target time period, including: determining a target weight sequence based on the current exercise type, the patient's identity information and a pre-constructed exercise type-weight sequence relationship table, wherein the exercise type-weight sequence relationship table includes weight sequences of multiple user groups when performing multiple exercise types; weighting the exercise data of the target time period based on the target weight sequence to obtain the current exercise intensity , wherein the current exercise intensity The mathematical expression is: In the formula, is the first weight, is the second weight; Determining target reference physical sign data of the patient within the target time period based on the patient's current exercise intensity, the patient's identity information, and a pre-built physical sign reference database, wherein the physical sign reference database includes reference physical sign data of multiple user groups when performing exercises of various intensities; Calculating the similarity between the measured physical sign data and the reference physical sign data; when the similarity is less than a preset similarity threshold, executing a question-and-answer process with the patient, wherein the question-and-answer process is used to extract symptom entities; The key information of the patient's recovery period is constructed based on the patient's cumulative exercise time, the current exercise intensity, the measured physical sign data of the target time period, and the symptom entity.

2. The intelligent extraction method of patient key information according to claim 1 is characterized in that: The process of constructing the movement type-weight sequence relationship table includes: Acquire a plurality of sample data, wherein the sample data includes user information of a normal user, a type of exercise, actual measured physical sign sample data and exercise sample data sampled when the normal user exercises within a target duration; Based on the measured physical sign sample data, multiple sample data The exercise intensity is marked, and the measured physical sign sample data is normalized to obtain multiple sample data containing labels , wherein the measured physical sign sample data includes heart rate and blood pressure; Dividing a plurality of sample data containing labels into a plurality of data units based on the user information; Classifying the data unit based on the motion type to obtain a plurality of data sub-units; Clustering multiple sample data in the data subunit based on motion intensity to obtain multiple intensity clusters; The mean value of the motion intensity, the mean value of the standard deviation of the acceleration peak value, and the mean value of the main frequency component of the acceleration frequency domain change waveform in each intensity cluster are calculated; and the mathematical expression of the motion intensity is constructed based on the mean value of the motion intensity, the mean value of the standard deviation of the acceleration peak value, and the mean value of the main frequency component of the acceleration frequency domain change waveform: In the formula, For the The mean motion intensity of each intensity cluster, For the The standard deviation of the peak acceleration of each intensity cluster, For the The mean value of the main frequency component of the acceleration frequency domain variation waveform of each intensity cluster; The mathematical expressions of any two exercise intensities are combined into a system of equations, and the first weight and the second weight To solve, when any data subunit has multiple different first weights Or multiple different second weights When multiple different first weights Or multiple different second weights Find the average and get the updated first weight and the updated second weight ; Based on the first weight of each data subunit and the second weight Construct the movement type-weight sequence relationship table.

3. The intelligent extraction method of key patient information according to claim 2 is characterized in that: The process of constructing the physical sign reference database includes: Clustering multiple sample data in each data subunit based on heart rate and blood pressure to obtain multiple physical sign clusters; Eliminate sample data with abnormal exercise intensity from the physical sign cluster to obtain a screened physical sign cluster; Calculate the mean of the exercise intensity of multiple sample data in the screened physical sign cluster , standard deviation of exercise intensity , heart rate average , heart rate standard deviation , mean blood pressure and blood pressure standard deviation ; Based on the mean exercise intensity , standard deviation of exercise intensity Exercise intensity range for constructing sign clusters ; Based on the heart rate mean and heart rate standard deviation Constructing heart rate ranges for vital signs clusters ; and based on the mean blood pressure and the standard deviation of blood pressure Constructing blood pressure ranges for sign clusters , where is the range adjustment parameter; A physical sign reference database is constructed based on heart rate ranges and blood pressure ranges in multiple exercise intensity ranges.

4. The intelligent extraction method of key patient information according to claim 1 is characterized in that: The measured vital sign data includes measured heart rate and measured blood pressure at multiple time points, and calculating the similarity between the measured vital sign data and the reference vital sign data includes: Normalize the measured heart rate and blood pressure at multiple time points respectively, and calculate the measured heart rate at multiple time points after normalization The average , Standard Deviation , and calculate the measured blood pressure at multiple time points after normalization The average , Standard Deviation ; Construct the heart rate distribution range for the target time period And blood pressure distribution range ,in, is the range adjustment parameter; The similarity is calculated based on the heart rate reference range and blood pressure reference range in the reference vital sign data, and the similarity includes the heart rate similarity Similarity to blood pressure , wherein the heart rate similarity Similarity to the blood pressure The mathematical expression is: In the formula, is the length of the heart rate reference range, is the length of the overlapping interval between the heart rate reference range and the heart rate distribution range, is the length of the blood pressure reference range, It is the length of the overlapping interval between the blood pressure reference range and the blood pressure distribution range.

5. The intelligent extraction method of key patient information according to claim 1, characterized in that: Conduct a Q&A process with the patient, including: Sending a pre-built question template to the patient, wherein the question template is built based on possible symptom entities; When feedback information is received from the patient, the question-and-answer process is completed; when no feedback information is received from the patient, the question-and-answer process returns to sending the pre-built question template to the patient until feedback information is received from the patient, or the number of times the question template is sent exceeds a preset threshold, the question-and-answer process is completed.

6. The intelligent extraction method of key patient information according to claim 4 is characterized in that: When the similarity is less than a preset similarity threshold, executing a question-and-answer process with the patient, including: The heart rate similarity Less than the preset similarity threshold or the blood pressure similarity When the similarity is less than a preset threshold, the question-and-answer process with the patient is executed.

7. The intelligent extraction method of key patient information according to claim 1, characterized in that: Also includes: The key information of the patient's recovery period is sent to the target object.

8. An intelligent extraction system for key patient information, characterized in that: include: The acquisition module is used to acquire the patient's measured vital sign data and motion data in the target time period, wherein the measured vital sign data and the motion data are collected by the patient's smart wearable device, and the target time period is a time period of the target time before the current time point; acquiring the patient's motion data in the target time period includes: acquiring the current motion type and three-axis acceleration data collected by the smart wearable device, wherein the three-axis acceleration data includes the three-axis acceleration data at multiple time points ; Filter the three-axis acceleration data respectively to obtain three-axis acceleration filtering data, wherein the three-axis acceleration filtering data includes the three-axis acceleration at multiple time points after filtering ; The three-axis acceleration of the multiple time points Combine them to get the total acceleration ; Based on the combined acceleration at multiple time points Draw the acceleration time domain variation waveform, extract all peak points in the acceleration time domain variation waveform, and calculate the standard deviation of all peak points ; Perform fast Fourier transform on the acceleration time domain variation waveform to obtain the acceleration frequency domain variation waveform, and extract the main frequency component from the acceleration frequency domain variation waveform ; Based on the standard deviation and the main frequency component Constructing motion data; The intensity calculation module is used to calculate the current exercise intensity of the patient based on the exercise data of the target time period; and when the current exercise intensity is greater than the set value, the target duration is added to the cumulative exercise duration of this exercise; the current exercise intensity of the patient is calculated based on the patient's identity information and the exercise data of the target time period, including: determining a target weight sequence based on the current exercise type, the patient's identity information and a pre-constructed exercise type-weight sequence relationship table, wherein the exercise type-weight sequence relationship table includes weight sequences of multiple user groups when performing multiple exercise types; weighting the exercise data of the target time period based on the target weight sequence to obtain the current exercise intensity , wherein the current exercise intensity The mathematical expression is: In the formula, is the first weight, is the second weight; A reference physical sign determination module, configured to determine target reference physical sign data of the patient within the target time period based on the patient's current exercise intensity, the patient's identity information, and a pre-built physical sign reference database, wherein the physical sign reference database includes reference physical sign data of multiple user groups when performing exercises of various intensities; A question-and-answer module, used for calculating the similarity between the measured physical sign data and the reference physical sign data; when the similarity is less than a preset similarity threshold, executing a question-and-answer process with the patient, wherein the question-and-answer process is used for extracting symptom entities; The information extraction module is used to construct the key information of the patient's recovery period based on the patient's cumulative exercise time, the current exercise intensity, the measured physical sign data of the target time period, and the symptom entity.

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