CN116421178B

CN116421178B - Auxiliary monitoring method, device, terminal equipment and readable storage medium

Info

Publication number: CN116421178B
Application number: CN202310420881.1A
Authority: CN
Inventors: 贾丽; 刘燕; 彭康祖; 武继军
Original assignee: Hebei Jinshengda Medical Products Co ltd
Current assignee: Hebei Jinshengda Medical Products Co ltd
Priority date: 2023-04-19
Filing date: 2023-04-19
Publication date: 2024-05-28
Anticipated expiration: 2043-04-19
Also published as: CN116421178A

Abstract

The application relates to an auxiliary monitoring method, an auxiliary monitoring device, terminal equipment and a readable storage medium, and relates to the technical field of monitoring. The method comprises the following steps: the method comprises the steps of obtaining current monitoring data corresponding to each monitoring item of a target patient, obtaining an adaptation range corresponding to each monitoring item of the target patient, wherein the adaptation range corresponding to each monitoring item is obtained after the reference range corresponding to each monitoring item is adjusted based on the obtained target disease type, the target disease type is the disease type corresponding to the target patient, judging whether the current monitoring data corresponding to each monitoring item is abnormal or not based on the adaptation range corresponding to each monitoring item, and outputting a first alarm instruction based on the abnormal current monitoring data if the current monitoring data is abnormal. The auxiliary monitoring method, the device, the terminal equipment and the readable storage medium can improve the accuracy of patient monitoring.

Description

Auxiliary monitoring method, device, terminal equipment and readable storage medium

Technical Field

The present application relates to the field of monitoring technologies, and in particular, to a method, an apparatus, a terminal device, and a readable storage medium for auxiliary monitoring.

Background

In a medical setting, an intensive care patient needs to be monitored continuously, and therefore, a monitor is needed, and the monitor is used for analyzing various physiological parameters of the patient and can be compared with a set value, and if an out-of-standard condition occurs, an alarm can be sent out. The doctor can also know the current condition of the patient through the parameters monitored by the monitor.

However, the physical condition of each patient is different, the range corresponding to the physiological parameters of the patient in the normal state may be different, and the patient is monitored through the uniform parameter range, so that the condition of inaccurate monitoring may occur.

Disclosure of Invention

In order to improve the accuracy of patient monitoring, the application provides an auxiliary monitoring method, an auxiliary monitoring device, terminal equipment and a readable storage medium.

The above object of the present application is achieved by the following technical solutions:

in a first aspect, a method of assisted monitoring is provided, the method comprising:

acquiring current monitoring data corresponding to each monitoring item of a target patient;

acquiring the adaptive range corresponding to each monitoring item of a target patient, wherein the adaptive range corresponding to each monitoring item is obtained by adjusting the reference range corresponding to each monitoring item based on the acquired target disease type, and the target disease type is the disease type corresponding to the target patient;

judging whether the current monitoring data corresponding to each monitoring item is abnormal or not based on the adaptive range corresponding to each monitoring item;

if the abnormality exists, outputting a first alarm instruction based on current monitoring data with the abnormality.

By adopting the technical scheme, the current monitoring data respectively corresponding to each monitoring item of the target patient is acquired, normal parameter ranges corresponding to different disease types may be different, the adaptive ranges respectively corresponding to each monitoring item of the target patient are acquired, the adaptive ranges respectively corresponding to each monitoring item are obtained after the reference ranges respectively corresponding to each monitoring item are adjusted based on the acquired target disease types, whether the current monitoring data respectively corresponding to each monitoring item is abnormal or not is judged based on the adaptive ranges respectively corresponding to each monitoring item, namely whether the current monitoring data is in the adaptive range respectively corresponding to each monitoring item, and when the current monitoring data is abnormal, a first alarm instruction is output based on the current monitoring data with the abnormality. In the application, the adaptation range of the target patient is obtained by adjusting the reference range corresponding to each monitoring item respectively through the type of the target disease, and comparing the current monitoring data with the adaptation range of each monitoring item, thereby accurately judging whether the current monitoring data has abnormal data or not and improving the accuracy of monitoring the patient.

In one possible implementation manner, the method for adjusting the reference range corresponding to each monitoring item based on the acquired target disease type to obtain the adaptive range corresponding to each monitoring item includes:

acquiring age data corresponding to a target patient;

determining first adjustment values corresponding to all monitoring items respectively based on the age data and a first preset relation, wherein the first preset relation is used for representing the corresponding relation between the preset age data and the first preset adjustment values corresponding to all the monitoring items respectively;

Determining second adjustment values corresponding to all monitoring items respectively based on the target disease type and a second preset relation, wherein the second preset relation is used for representing the corresponding relation between the preset disease type and the second preset adjustment values corresponding to all monitoring items respectively;

And adjusting the reference range corresponding to each monitoring item respectively based on the first adjustment value and the second adjustment value to obtain the adaptation range corresponding to each monitoring item respectively.

In another possible implementation manner, the acquiring the current monitoring data corresponding to each monitoring item of the target patient respectively further includes:

Acquiring a relation curve corresponding to a target monitoring item, wherein the relation curve is used for representing the relation between historical monitoring data and time;

Determining each target historical moment and target monitoring data corresponding to each target historical moment respectively based on the relation curve and a preset time interval;

and estimating the change trend of the target monitoring item based on the current target monitoring data and the target monitoring data corresponding to each target historical moment, wherein the current target monitoring data is the current monitoring data corresponding to the target monitoring item.

In another possible implementation manner, the predicting the trend of the change of the target monitoring item based on the current target monitoring data and the target monitoring data corresponding to each target historical moment includes:

Determining a history stability value corresponding to each preset history time period based on target monitoring data corresponding to each target history time;

Acquiring a current time, and determining a neighboring time and neighboring monitoring data corresponding to the neighboring time based on the current time, a preset time interval and a relation curve, wherein the neighboring time is the neighboring time of the current time;

determining a current stable value based on the adjacent monitoring data and the current monitoring data, and inputting the current stable value into a trained stable value prediction model to obtain a future stable value of the target monitoring item, wherein the trained stable value prediction model is obtained by training based on the historical stable values respectively corresponding to each preset historical time period;

and determining the change trend of the target monitoring item based on the future stable value.

In another possible implementation manner, the outputting the first alarm instruction based on the current monitoring data with the abnormality includes:

Determining current monitoring data with abnormality as abnormal data, determining a monitoring item corresponding to the abnormal data as an abnormal monitoring item, and determining an abnormal difference value based on the abnormal data and an adaptation range corresponding to the abnormal monitoring item;

If the abnormal difference value is smaller than the first preset difference value, acquiring abnormal duration time;

determining an outlier based on the outlier difference, the outlier duration and a preset weight;

And determining an alarm level based on a first corresponding relation among the abnormal value, the preset abnormal value and the preset alarm level, and outputting a first alarm instruction based on the alarm level.

In another possible implementation, the current monitoring data includes: current carbon dioxide concentration and current blood oxygen concentration;

the method further comprises the steps of:

determining a composite lung index based on the current carbon dioxide concentration and the current blood oxygen concentration;

and if the comprehensive lung index is smaller than the preset index, outputting a second alarm instruction.

In another possible implementation, the determining the integrated lung index based on the current carbon dioxide concentration and the current blood oxygen concentration includes:

Determining a current carbon dioxide difference value based on the current carbon dioxide concentration and an adaptation range corresponding to a carbon dioxide item;

If the current carbon dioxide difference value is smaller than or equal to a second preset difference value, determining a normal blood oxygen concentration range corresponding to the current carbon dioxide concentration based on a preset blood oxygen concentration range, a carbon dioxide concentration and a second corresponding relation between the preset carbon dioxide concentration range and the preset blood oxygen concentration range;

and determining a current blood oxygen concentration difference value based on the current blood oxygen concentration and the normal blood oxygen concentration range, and determining a comprehensive lung index based on the current blood oxygen concentration difference value.

In a second aspect, there is provided an apparatus for assisting in monitoring, the apparatus comprising:

the first acquisition module is used for acquiring current monitoring data corresponding to each monitoring item of the target patient;

The second acquisition module is used for acquiring the adaptive range corresponding to each monitoring item of the target patient, wherein the adaptive range corresponding to each monitoring item is obtained by adjusting the reference range corresponding to each monitoring item based on the acquired target disease type, and the target disease type is the disease type corresponding to the target patient;

The judging module is used for judging whether the current monitoring data corresponding to each monitoring item respectively has abnormality or not based on the adaptive range corresponding to each monitoring item respectively;

And the first output module is used for outputting a first alarm instruction based on the current monitoring data with the abnormality if the abnormality exists.

In one possible implementation, the apparatus further includes: an adjustment module, wherein,

The adjustment module is specifically configured to, when adjusting the reference ranges corresponding to the monitoring items respectively based on the obtained target disease types to obtain the modes of the adaptive ranges corresponding to the monitoring items respectively:

acquiring age data corresponding to a target patient;

In another possible implementation, the apparatus further includes: a third acquisition module, a first determination module and an estimation module, wherein,

The third acquisition module is used for acquiring a relation curve corresponding to the target monitoring item, and the relation curve is used for representing the relation between the historical monitoring data and time;

The first determining module is used for determining each target historical moment and target monitoring data corresponding to each target historical moment respectively based on the relation curve and a preset time interval;

The estimating module is used for estimating the change trend of the target monitoring item based on the current target monitoring data and the target monitoring data corresponding to each target historical moment, wherein the current target monitoring data is the current monitoring data corresponding to the target monitoring item.

In another possible implementation manner, the estimating module is specifically configured to, when estimating the trend of the change of the target monitoring item based on the current target monitoring data and the target monitoring data corresponding to each target historical moment respectively:

In another possible implementation manner, the first output module is specifically configured to, when outputting the first alarm instruction based on current monitoring data with an abnormality:

the apparatus further comprises: a second determination module and a second output module, wherein,

The second determining module is used for determining a comprehensive lung index based on the current carbon dioxide concentration and the current blood oxygen concentration;

and the second output module is used for outputting a second alarm instruction when the comprehensive lung index is smaller than a preset index.

In another possible implementation, the second determining module is specifically configured to, when determining the integrated lung index based on the current carbon dioxide concentration and the current blood oxygen concentration:

In a third aspect, there is provided a terminal device comprising:

One or more processors;

A memory;

one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to: operations corresponding to the method of assisted monitoring according to any of the possible implementations of the first aspect are performed.

In a fourth aspect, a computer readable storage medium is provided, the storage medium storing at least one instruction, at least one program, code set, or instruction set, the at least one instruction, at least one program, code set, or instruction set being loaded and executed by a processor to implement a method of assisted monitoring as shown in any one of the possible implementations of the first aspect.

In summary, the present application includes at least one of the following beneficial technical effects:

Compared with the related art, in the application, by acquiring current monitoring data corresponding to each monitoring item of a target patient, normal parameter ranges corresponding to different disease types may be different, and acquiring adaptation ranges corresponding to each monitoring item of the target patient, wherein the adaptation ranges corresponding to each monitoring item are obtained by adjusting reference ranges corresponding to each monitoring item based on the acquired target disease type, and judging whether the current monitoring data corresponding to each monitoring item is abnormal or not based on the adaptation ranges corresponding to each monitoring item, namely whether the current monitoring data is in the adaptation ranges corresponding to each monitoring item, and outputting a first alarm instruction based on the abnormal current monitoring data when the abnormality exists. In the application, the adaptation range of the target patient is obtained by adjusting the reference range corresponding to each monitoring item respectively through the type of the target disease, and comparing the current monitoring data with the adaptation range of each monitoring item, thereby accurately judging whether the current monitoring data has abnormal data or not and improving the accuracy of monitoring the patient.

Drawings

Fig. 1 is a schematic flow chart of a method for assisting in monitoring according to an embodiment of the present application.

Fig. 2 is a schematic structural diagram of an auxiliary monitoring device according to an embodiment of the present application.

Fig. 3 is a schematic structural diagram of a terminal device according to an embodiment of the present application.

Detailed Description

The present application will be described in further detail with reference to fig. 1 to 3.

The present embodiment is only for explanation of the present application and is not to be construed as limiting the present application, and modifications to the present embodiment, which may not creatively contribute to the present application as required by those skilled in the art after reading the present specification, are all protected by patent laws within the scope of claims of the present application.

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In this context, unless otherwise specified, the term "/" generally indicates that the associated object is an "or" relationship.

Embodiments of the application are described in further detail below with reference to the drawings.

The embodiment of the application provides a method for assisting in monitoring, which is executed by a terminal device, wherein the terminal device can be a smart phone, a tablet computer, a notebook computer, a monitor device, a desktop computer and the like, but is not limited to the smart phone, the tablet computer, the notebook computer, the monitor device, the desktop computer and the like, and the method can comprise the following steps as shown in fig. 1:

step S101, current monitoring data corresponding to each monitoring item of the target patient is obtained.

For the embodiment of the application, the acquisition equipment can acquire the current monitoring data corresponding to each monitoring item in real time, can acquire the current monitoring data corresponding to each monitoring item at intervals of preset time, and can acquire the current monitoring data corresponding to each monitoring item when the acquisition instruction triggered by the user is detected, which is not limited in the embodiment of the application.

For the embodiment of the application, the terminal device can acquire the current monitoring data corresponding to each monitoring item respectively from the acquisition device in real time, can acquire the current monitoring data corresponding to each monitoring item respectively from the acquisition device at intervals of preset time, and can acquire the current monitoring data corresponding to each monitoring item respectively from the acquisition device when the instruction triggered by the user is detected, which is not limited in the embodiment of the application.

It should be noted that the acquisition device may be a device independent of the terminal device.

In the embodiment of the application, the display may display current monitoring data corresponding to each monitoring item in real time, or may display current monitoring data corresponding to each monitoring item when a display instruction triggered by a user is detected. Further, the current monitoring data can be displayed in the form of waveforms, the types of waveforms can be scanning or filling, and a user can select the moving speed of the curve.

Step S102, acquiring the adaptation range corresponding to each monitoring item of the target patient.

The adaptive range corresponding to each monitoring item is obtained by adjusting the reference range corresponding to each monitoring item based on the acquired target disease type, wherein the target disease type is the disease type corresponding to the target patient.

For the embodiment of the application, the reference range corresponding to each monitoring item is an initial value, which can be preset by the system and can be set by the physical condition of the patient, such as the health degree and the target disease type. When the physical conditions of the patients are different, the threshold values of the monitoring data triggering alarm corresponding to each detection item are different, and the reference range corresponding to each detection item is adjusted to obtain the adaptive range corresponding to each detection item.

For the embodiment of the present application, the target disease type may be obtained from other devices, or the target disease type input by the user may be obtained, which is not limited in the embodiment of the present application.

Step S103, judging whether the current monitoring data corresponding to each monitoring item is abnormal or not based on the adaptive range corresponding to each monitoring item.

For the embodiment of the application, the adaptive range corresponding to each monitoring item is compared with the current monitoring data corresponding to each monitoring item, whether the current monitoring data is abnormal or not is judged, and when the current monitoring data is not in the adaptive range, the current detection data is abnormal.

Step S104, if the abnormality exists, outputting a first alarm instruction based on current monitoring data with the abnormality.

For the embodiment of the application, when the abnormality exists, the first alarm instruction can comprise the current monitoring data with the abnormality based on the current monitoring data with the abnormality, the first alarm instruction is output, and the first alarm instruction can be at least one of sound, lamplight and characters.

Compared with the related art, in the embodiment of the application, by acquiring current monitoring data corresponding to each monitoring item of a target patient, normal parameter ranges corresponding to different disease types may be different, and acquiring adaptation ranges corresponding to each monitoring item of the target patient, wherein the adaptation ranges corresponding to each monitoring item are obtained by adjusting reference ranges corresponding to each monitoring item based on the acquired target disease types, and judging whether the current monitoring data corresponding to each monitoring item is abnormal or not based on the adaptation ranges corresponding to each monitoring item, namely whether the current monitoring data is in the corresponding adaptation range, and outputting a first alarm instruction based on the abnormal current monitoring data when the abnormality exists. In other words, in the embodiment of the application, the adaptive range of the target patient is obtained by adjusting the reference ranges corresponding to the monitoring items respectively according to the type of the target disease, and comparing the current monitoring data with the adaptive range of the monitoring items, so as to accurately judge whether the current monitoring data has abnormal data or not, and improve the accuracy of monitoring the patient.

The patient's disease state may differ, and the corresponding threshold value for the corresponding monitoring term may differ, and the threshold value for the monitoring term may be related to the age of the patient in addition to the disease state of the patient. The method for adjusting the reference range corresponding to each monitoring item based on the obtained target disease type to obtain the adaptive range corresponding to each monitoring item specifically may include: acquiring age data corresponding to a target patient; determining first adjustment values corresponding to all monitoring items respectively based on the age data and a first preset relation; determining second adjustment values corresponding to the monitoring items respectively based on the target disease type and a second preset relation; and adjusting the reference range corresponding to each monitoring item respectively based on the first adjustment value and the second adjustment value to obtain the adaptation range corresponding to each monitoring item respectively.

The first preset relationship is used for representing the corresponding relationship between the preset age data and the first preset adjustment value corresponding to each monitoring item, and the second preset relationship is used for representing the corresponding relationship between the preset disease type and the second preset adjustment value corresponding to each monitoring item.

For the embodiment of the present application, the terminal device may obtain the age data corresponding to the target patient from other devices, or may obtain the age data corresponding to the target patient input by the user, which is not limited in the embodiment of the present application.

For the embodiment of the present application, the first preset adjustment value and the second preset adjustment value are the magnitudes of the values adjusted upward or downward. And determining the first preset adjustment value corresponding to each monitoring item matched with the age data based on the first preset relation between the preset age data and the first preset adjustment value corresponding to each monitoring item. The first preset adjustment value is a value to be adjusted for the age data of each monitoring item, for example, when the age data is 12, the first preset adjustment value corresponding to the monitoring item a is-15, and the first preset adjustment value corresponding to the monitoring item B is-25.

For the embodiment of the application, based on the second preset relation between the preset disease type and the second preset adjustment value corresponding to each monitoring item, the second adjustment value corresponding to each monitoring item matched with the target disease type is determined from the second preset adjustment values corresponding to each monitoring item. For example, the target disease type is type B, for the preset disease type a, the adjustment value corresponding to the monitoring item a is +10, the adjustment value corresponding to the monitoring item B is-16, the adjustment value corresponding to the monitoring item C is +5, for the preset disease type B, the adjustment value corresponding to the monitoring item a is-10, the adjustment value corresponding to the monitoring item B is-10, the adjustment value corresponding to the monitoring item C is +5, then the second adjustment values respectively corresponding to the respective monitoring items corresponding to the target disease type B are the adjustment value corresponding to the monitoring item a is-10, the adjustment value corresponding to the monitoring item B is-10, and the adjustment value corresponding to the monitoring item C is +5.

For the embodiment of the application, after the first adjustment value and the second adjustment value corresponding to each monitoring item are determined, the average value of the first adjustment value and the second adjustment value of the same monitoring item can be adjusted for the reference range corresponding to each monitoring item so as to obtain the adaptation range corresponding to each monitoring item. For example, if the reference range of the monitoring item A is greater than 80%, the first adjustment value is-10, and the second adjustment value is-15, the adaptation range of the monitoring item A is greater than 67.5%.

For the embodiment of the application, a first adjustment value is obtained through the age and a first preset relation of the preset age and the first preset adjustment corresponding to each monitoring item respectively; obtaining a second adjustment value through a target disease type and a second preset relation between the preset disease type and a second preset adjustment value corresponding to each monitoring item respectively, adjusting a reference range by integrating the first adjustment value and the second adjustment value, and accurately determining an adaptation range according to the age of a patient and the target disease type.

After the adaptation range is determined in the steps, comparing the current monitoring data with the adaptation range, judging whether the current monitoring data has abnormal data or not, wherein the current monitoring data corresponding to the monitoring item is possibly transient when the current monitoring data has problems, has no threat to a patient, and is determined by combining the abnormal data and the abnormal duration when alarming the abnormal data. Based on the current monitoring data with abnormality, outputting a first alarm instruction may specifically include: determining current monitoring data with abnormality as abnormal data, determining a monitoring item corresponding to the abnormal data as an abnormal monitoring item, and determining an abnormal difference value based on the abnormal data and an adaptation range corresponding to the abnormal monitoring item; if the abnormal difference value is smaller than the first preset difference value, acquiring abnormal duration time; determining an alarm level based on the anomaly difference value, the anomaly duration time and a preset weight; and outputting a first alarm instruction based on the alarm level. In the embodiment of the application, when the abnormal difference value is larger than the first preset difference value, an early warning instruction is output.

For the embodiment of the application, the abnormal data are current monitoring data with abnormality, the abnormal monitoring items are monitoring items with abnormality, the difference value between the average value of the upper limit and the lower limit of the adaptation range corresponding to the abnormal monitoring items and the abnormal data is determined to be an abnormal difference value, when the abnormal difference value is smaller than a first preset difference value, the abnormal duration time is obtained, and the alarm level is jointly determined based on the abnormal duration time and the abnormal difference value. For example, the anomaly difference is 20, the duration is 10 minutes, the preset weight corresponding to the anomaly difference is 0.6, the preset weight corresponding to the anomaly duration is 0.4, and the anomaly value is 16. Based on a first corresponding relation between a preset abnormal value and a preset alarm level, an alarm level corresponding to the abnormal value is determined, and a first alarm instruction is output based on the alarm level.

For the embodiment of the application, the first alarm instruction can be at least one of sound, light and text, and the alarm sounds are different according to different alarm grades, for example, the sound with high alarm grade is 'Du-Du', and the sound with low alarm grade is 'Du-Du'; the alarm levels are different, the color and the flicker frequency of the lamplight are different, for example, the lamplight with high alarm level is red, the flicker frequency is fast, and the sound with low alarm level is yellow and the flicker frequency is slow; the alarm level is different, and characters displayed by the display are different, for example, characters with high alarm level are 'alarm prompt information is red background and white character', and sounds with low alarm level are 'alarm prompt information is red background and white character'.

For the embodiment of the application, the abnormal data and the adaptation range are used for determining the abnormal difference value, the abnormal value is accurately determined based on the abnormal difference value, the abnormal duration time and the preset weight, the alarm grade is determined based on the abnormal value, and the alarm is carried out based on the alarm grade, so that a worker can master the abnormal degree of a patient, and the accuracy of monitoring the patient is improved.

In addition to requiring real-time monitoring of the patient, it is also necessary to estimate the condition of the patient over a future period of time. Acquiring current monitoring data corresponding to each monitoring item of the target patient, wherein the current monitoring data can also comprise a relation curve corresponding to the target monitoring item, and the relation curve is used for representing the relation between historical monitoring data and time; determining each target historical moment and target monitoring data corresponding to each target historical moment respectively based on the relation curve and a preset time interval; and estimating the change trend of the target monitoring item based on the current target monitoring data and the target monitoring data respectively corresponding to each target historical moment. In the embodiment of the application, the current target monitoring data is the current monitoring data corresponding to the target monitoring item.

For the embodiment of the application, each target historical moment is a moment in a relation curve, in order to ensure that the change trend of the target monitoring item is accurately estimated, the intervals of adjacent target historical moments need to be the same, namely, the target historical moment is determined based on a preset time interval, and the historical monitoring data corresponding to the target historical moment is the target monitoring data. And determining the change trend of the target monitoring item in the preset time according to the change trend or stability of the target monitoring data at each target historical moment.

It should be noted that the preset time interval may be ten minutes or one hour, and the specific time interval range is not limited in the embodiment of the present application.

For the embodiment of the application, the change trend of the target detection item is estimated through the current monitoring data and the target monitoring data respectively corresponding to each target historical moment, so that the physiological parameter state of the patient is obtained in advance, and the accuracy of monitoring the patient is further improved.

Specifically, the change trend of the target monitoring item in the preset time can be estimated through the stability of the historical monitoring data. Based on the current monitoring data and the target monitoring data respectively corresponding to each target historical moment, the change trend of the target monitoring item is estimated, which specifically comprises the following steps: determining a history stability value corresponding to each preset history time period based on the target history time and target monitoring data corresponding to each target history time; acquiring a current time, and determining a neighboring time and neighboring monitoring data corresponding to the neighboring time based on the current time, a preset time interval and a relation curve, wherein the neighboring time is the neighboring time of the current time; determining a current stable value based on the adjacent time, the current time, the adjacent monitoring data and the current monitoring data, and inputting the current stable value into the trained prediction model to obtain a future stable value of the target monitoring item; and determining the change trend of the target monitoring item based on the stable value.

The trained stable value prediction model is obtained based on historical stable value training.

For the embodiment of the application, according to the target historical time and the target monitoring data corresponding to each target historical time, the historical stability values corresponding to each preset historical time period are determined, for example. The target history time is: 8:00, 8:15, 8:30, 8:45 and 9:00, and the preset time period is half an hour, determining a historical stability value based on target monitoring data corresponding to 8:00, 8:15 and 8:30 respectively, determining a historical stability value based on target monitoring data corresponding to 8:15, 8:30 and 8:45 respectively, and determining a historical stability value based on target monitoring data corresponding to 8:30, 8:45 and 9:00 respectively.

For the embodiment of the application, the difference value between the target monitoring data and the average value of the target monitoring data in each preset time period is determined, and the average value of the sum of the absolute values of the difference values corresponding to each target monitoring data is used as the historical stable value, for example, the average value is 83 when the monitoring data corresponding to 8:00 is 81, the monitoring data corresponding to 8:15 is 85, the absolute value of the sum of the absolute values of the difference values corresponding to each target monitoring data is 4, the average value of the sum of the absolute values is 1.33, and the historical stable value is 1.33.

For the embodiment of the present application, the adjacent time is a historical time which is different from the current time by a preset time interval, for example, the current time is 11:20, the preset time interval is 15 minutes, and the adjacent time is 11:05. And determining a future stable value through the proximity monitoring data and the current stable value, and inputting the current stable value into the trained stable value prediction model. The future stable value and the preset stable value can be compared to determine the change trend of the target monitoring item, if the future stable value is smaller than the preset stable value, the change trend is slow change, if the future stable value is larger than the preset stable value, the change trend is rapid change, and when the change trend is rapid change, the patient may be abnormal.

For the embodiment of the application, the historical stable values of each preset time period are determined through each historical monitoring data, so that the change condition of the historical monitoring data is obtained more accurately, the stable value prediction model is calculated rapidly, the efficiency of predicting the future stable value is improved, and the change trend of the target monitoring item is obtained rapidly.

Another possible implementation manner of the embodiment of the present application, the current monitoring data includes: current carbon dioxide concentration and current blood oxygen concentration; the method may further comprise: determining a comprehensive lung index (INTEGRATED PULMONARY INDEX, IPI) based on the current carbon dioxide concentration and the current blood oxygen concentration; and if the comprehensive lung index is smaller than the preset index, outputting a second alarm instruction. In the embodiment of the application, the comprehensive lung index is determined according to the deviation state of the current carbon dioxide concentration and the current blood oxygen concentration compared with the preset value respectively, when the comprehensive lung index is smaller than the preset index, the patient may be dangerous, and when the comprehensive lung index is smaller than the preset index, a second alarm instruction is output.

For the embodiment of the present application, the step of determining the comprehensive lung index based on the current carbon dioxide concentration and the current blood oxygen concentration may be performed before the step of determining whether the current monitoring data has an abnormality based on the adaptive range corresponding to each monitoring item, may be performed after the step of determining whether the current monitoring data has an abnormality based on the adaptive range corresponding to each monitoring item, or may be performed simultaneously with the step of determining whether the current monitoring data has an abnormality based on the adaptive range corresponding to each monitoring item, which is not limited in the embodiment of the present application.

For the embodiment of the application, when the monitoring item is carbon dioxide, the corresponding current monitoring data is the current carbon dioxide concentration, and when the monitoring item is an oxygen blood item, the current monitoring data is the current oxygen blood concentration.

Specifically, determining the integrated lung index based on the current carbon dioxide concentration and the current blood oxygen concentration may specifically include: determining a current carbon dioxide difference value based on the current carbon dioxide concentration and an adaptation range of the carbon dioxide term; if the current carbon dioxide difference value is smaller than or equal to a second preset difference value, determining a normal blood oxygen concentration range corresponding to the current carbon dioxide concentration based on a second corresponding relation among the preset blood oxygen concentration range, the preset carbon dioxide concentration range and the preset blood oxygen concentration range; a current blood oxygen concentration difference is determined based on the current blood oxygen concentration and the normal blood oxygen concentration range, and a composite lung index is determined based on the current blood oxygen concentration difference. In the embodiment of the application, the blood oxygen concentration of the patient corresponding to different carbon dioxide concentrations in the normal state is different. For example, at a carbon dioxide concentration of 60% -65%, the patient's blood oxygen concentration is 90% -92%.

For the embodiment of the application, the current carbon dioxide concentration needs to be compared with the corresponding adaptation range of the carbon dioxide item, and whether the current carbon dioxide concentration is in the adaptation range of the carbon dioxide item or not is judged, namely, the difference value between the current carbon dioxide concentration and the upper limit or the lower limit of the adaptation range of the carbon dioxide item is determined as the current carbon dioxide difference value. Comparing the current carbon dioxide difference value with the second preset difference value, wherein the current carbon dioxide difference value is smaller than or equal to the second preset difference value, and indicating that the current carbon dioxide concentration is in a normal range.

For the embodiment of the application, when the current carbon dioxide concentration is within the normal range, the normal blood oxygen concentration range corresponding to the current carbon dioxide concentration is determined based on the preset blood oxygen concentration range, the preset carbon dioxide concentration range and the first corresponding relation of the preset blood oxygen concentration range. For example, when the preset carbon dioxide concentration is 60% -65%, the normal blood oxygen concentration range is 90% -92%, when the preset carbon dioxide concentration is 65% -70%, the normal blood oxygen concentration range is 92% -96%, and for the current carbon dioxide concentration of 68%, the corresponding normal blood oxygen concentration range is 92% -96%. For the embodiment of the application, the current blood oxygen concentration and the normal blood oxygen concentration range can be used for determining the current blood oxygen concentration difference value, and the comprehensive lung index corresponding to the current blood oxygen concentration difference value is determined based on the preset blood oxygen concentration difference value and the preset comprehensive lung index.

For the embodiment of the application, the comprehensive lung index is more accurately determined by comparing the current carbon dioxide concentration with the current blood oxygen concentration through the corresponding relation between the preset carbon dioxide concentration and the preset current blood oxygen concentration.

The above embodiment describes a method of auxiliary monitoring from the viewpoint of a method flow, and the following embodiment describes an apparatus of auxiliary monitoring from the viewpoint of a virtual module or a virtual unit, which is described in detail in the following embodiment.

An embodiment of the present application provides an apparatus for assisting monitoring, as shown in fig. 2, the apparatus 20 for assisting monitoring may specifically include: a first acquisition module 21, a second acquisition module 22, a judgment module 23 and a first output module 24, wherein,

A first obtaining module 21, configured to obtain current monitoring data corresponding to each monitoring item of the target patient;

The second obtaining module 22 is configured to obtain an adaptation range corresponding to each monitoring item of the target patient, where the adaptation range corresponding to each monitoring item is obtained by adjusting a reference range corresponding to each monitoring item based on the obtained target disease type, and the target disease type is a disease type corresponding to the target patient;

The judging module 23 is configured to judge whether the current monitoring data corresponding to each monitoring item respectively has an abnormality based on the adaptation range corresponding to each monitoring item respectively;

the first output module 24 is configured to output a first alarm instruction based on current monitoring data of the abnormality when the abnormality exists.

In one possible implementation manner of the embodiment of the present application, the apparatus 20 further includes: an adjustment module, wherein,

The adjusting module is specifically used for adjusting the reference range corresponding to each monitoring item respectively to obtain the mode of the adaptive range corresponding to each monitoring item respectively, wherein the mode is as follows:

acquiring age data corresponding to a target patient;

Determining second adjustment values corresponding to the monitoring items respectively based on the target disease type and a second preset relationship, wherein the second preset relationship is used for representing the corresponding relationship between the preset disease type and the second preset adjustment values corresponding to the monitoring items respectively;

Another possible implementation manner of the embodiment of the present application, the apparatus 20 further includes: the system comprises a second acquisition module, a first determination module and an estimation module, wherein,

The second acquisition module is used for acquiring a relation curve corresponding to the target monitoring item, wherein the relation curve is used for representing the relation between the historical monitoring data and time;

In another possible implementation manner of the embodiment of the present application, when the prediction module predicts the change trend of the target monitoring item based on the current target monitoring data and the target monitoring data corresponding to each target history time, the prediction module is specifically configured to:

Based on the adjacent monitoring data and the current monitoring data, determining a current stable value, and inputting the current stable value into a trained stable value prediction model to obtain a future stable value of the target monitoring item, wherein the trained stable value prediction model is obtained by training based on the historical stable values respectively corresponding to each preset historical time period;

In another possible implementation manner of the embodiment of the present application, the first output module 24 is specifically configured to, when outputting the first alarm instruction based on the current monitoring data with the abnormality:

determining an anomaly value based on the anomaly difference value, the anomaly duration and a preset weight;

Another possible implementation manner of the embodiment of the present application, the current monitoring data includes: current carbon dioxide concentration and current blood oxygen concentration;

the apparatus 20 further comprises: a second determination module and a second output module, wherein,

A second determination module for determining a composite lung index based on the current carbon dioxide concentration and the current blood oxygen concentration;

And the second output module is used for outputting a second alarm instruction when the comprehensive lung index is smaller than the preset index.

In another possible implementation manner of the embodiment of the present application, the second determining module is specifically configured to, when determining the comprehensive lung index based on the current carbon dioxide concentration and the current blood oxygen concentration:

Determining a current carbon dioxide difference value based on the current carbon dioxide concentration and an adaptation range of the carbon dioxide term;

If the current carbon dioxide difference value is smaller than or equal to a second preset difference value, determining a normal blood oxygen concentration range corresponding to the current carbon dioxide concentration based on a preset blood oxygen concentration range, the carbon dioxide concentration and a second corresponding relation between the preset carbon dioxide concentration range and the preset blood oxygen concentration range;

A current blood oxygen concentration difference is determined based on the current blood oxygen concentration and the normal blood oxygen concentration range, and a composite lung index is determined based on the current blood oxygen concentration difference.

It will be clear to those skilled in the art that, for convenience and brevity of description, a specific working process of the apparatus for assisting monitoring described above may refer to a corresponding process in the foregoing method embodiment, which is not described herein again.

The embodiment of the application provides a terminal device, as shown in fig. 3, a terminal device 30 shown in fig. 3 includes: a processor 301 and a memory 303. Wherein the processor 301 is coupled to the memory 303, such as via a bus 302. Optionally, the terminal device 30 may also include a transceiver 304. It should be noted that, in practical applications, the transceiver 304 is not limited to one, and the structure of the terminal device 30 is not limited to the embodiment of the present application.

The Processor 301 may be a CPU (Central Processing Unit ), general purpose Processor, DSP (DIGITAL SIGNAL Processor, data signal Processor), ASIC (Application SPECIFIC INTEGRATED Circuit), FPGA (Field Programmable GATE ARRAY ) or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various exemplary logic blocks, modules and circuits described in connection with this disclosure. Processor 301 may also be a combination that implements computing functionality, e.g., comprising one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

Bus 302 may include a path to transfer information between the components. Bus 302 may be a PCI (PERIPHERAL COMPONENT INTERCONNECT, peripheral component interconnect standard) bus or an EISA (Extended Industry Standard Architecture ) bus, or the like. Bus 302 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in fig. 3, but not only one bus or type of bus.

The Memory 303 may be, but is not limited to, a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory ) or other type of dynamic storage device that can store information and instructions, an EEPROM (ELECTRICALLY ERASABLE PROGRAMMABLE READ ONLY MEMORY ), a CD-ROM (Compact Disc Read Only Memory, compact disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.

The memory 303 is used for storing application program codes for executing the inventive arrangements and is controlled to be executed by the processor 301. The processor 301 is configured to execute the application code stored in the memory 303 to implement what is shown in the foregoing method embodiments.

Wherein the terminal device includes, but is not limited to: mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), car terminals (e.g., car navigation terminals), and the like, and stationary terminals such as digital TVs, monitor devices, desktop computers, and the like. The terminal device shown in fig. 3 is only an example, and should not impose any limitation on the functions and the scope of use of the embodiment of the present application.

Embodiments of the present application provide a computer-readable storage medium having a computer program stored thereon, which when run on a computer, causes the computer to perform the corresponding method embodiments described above. Compared with the related art, in the embodiment of the application, by acquiring the current monitoring data respectively corresponding to each monitoring item of the target patient, normal parameter ranges corresponding to different disease types may be different, and acquiring the adaptive ranges respectively corresponding to each monitoring item of the target patient, wherein the adaptive ranges respectively corresponding to each monitoring item are obtained by adjusting the reference ranges respectively corresponding to each monitoring item based on the acquired target disease types, and judging whether the current monitoring data respectively corresponding to each monitoring item is abnormal or not based on the adaptive ranges respectively corresponding to each monitoring item, namely whether the current monitoring data is in the adaptive range respectively corresponding to each monitoring item, and outputting a first alarm instruction based on the current monitoring data with abnormality when the abnormality exists. In other words, in the embodiment of the application, the adaptive range of the target patient is obtained by adjusting the reference ranges corresponding to the monitoring items respectively according to the type of the target disease, and comparing the current monitoring data with the adaptive range of the monitoring items, so as to accurately judge whether the current monitoring data has abnormal data or not, and improve the accuracy of monitoring the patient.

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.

The foregoing is only a partial embodiment of the present application, and it should be noted that it will be apparent to those skilled in the art that modifications and adaptations can be made without departing from the principles of the present application, and such modifications and adaptations should and are intended to be comprehended within the scope of the present application.

Claims

1. A method of assisted monitoring, comprising:

acquiring current monitoring data corresponding to each monitoring item of a target patient; the current monitoring data includes: current carbon dioxide concentration and current blood oxygen concentration;

if the abnormality exists, outputting a first alarm instruction based on current monitoring data with the abnormality;

The step of obtaining the current monitoring data corresponding to each monitoring item of the target patient comprises the following steps:

Estimating the change trend of a target monitoring item based on current target monitoring data and target monitoring data corresponding to each target historical moment, wherein the current target monitoring data is the current monitoring data corresponding to the target monitoring item;

The predicting the change trend of the target monitoring item based on the current target monitoring data and the target monitoring data corresponding to each target historical moment respectively comprises the following steps:

Determining a change trend of the target monitoring item based on the future stable value;

the method further comprises the steps of:

Outputting a second alarm instruction if the comprehensive lung index is smaller than a preset index;

the determining a composite lung index based on the current carbon dioxide concentration and the current blood oxygen concentration comprises:

Determining a current carbon dioxide difference value based on the current carbon dioxide concentration and an adaptation range corresponding to a carbon dioxide item, wherein the current carbon dioxide difference value is a difference value between the current carbon dioxide concentration and the upper limit or the lower limit of the adaptation range corresponding to the carbon dioxide;

2. The method according to claim 1, wherein the step of adjusting the reference ranges corresponding to the respective monitoring items based on the obtained target disease type to obtain the adaptive ranges corresponding to the respective monitoring items includes:

acquiring age data corresponding to a target patient;

3. The method of claim 1, wherein outputting a first alert instruction based on current monitored data for the presence of an anomaly comprises:

4. An apparatus for assisting in monitoring, comprising:

The first acquisition module is used for acquiring current monitoring data corresponding to each monitoring item of the target patient; the current monitoring data includes: current carbon dioxide concentration and current blood oxygen concentration;

The first output module is used for outputting a first alarm instruction based on current monitoring data with abnormality when the abnormality exists;

the estimating module is used for acquiring a relation curve corresponding to the target monitoring item, wherein the relation curve is used for representing the relation between the historical monitoring data and time;

the estimating module is specifically configured to determine a history stability value corresponding to each preset history time period based on target monitoring data corresponding to each target history time respectively;

the historical stable value is obtained by determining the difference value of the target monitoring data and the average value of the target monitoring data in each preset time period and taking the average value of the sum of absolute values of the difference values corresponding to the target monitoring data as the historical stable value;

A second output module for determining a composite lung index based on the current carbon dioxide concentration and the current blood oxygen concentration;

The second output module is specifically configured to determine a current carbon dioxide difference value based on the current carbon dioxide concentration and an adaptation range corresponding to a carbon dioxide item, where the current carbon dioxide difference value is a difference value between the current carbon dioxide concentration and an upper limit or a lower limit of the adaptation range corresponding to carbon dioxide;

5. A terminal device, comprising:

One or more processors;

A memory;

One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to: a method of performing assisted monitoring according to any one of claims 1 to 3.

6. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a method of assisted monitoring according to any of claims 1-3.