CN113053518A - Intelligent monitoring system and method for physical signs of cardiology department patients - Google Patents
Intelligent monitoring system and method for physical signs of cardiology department patients Download PDFInfo
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Abstract
The invention provides an intelligent monitoring system and method for physical signs of a patient in the department of cardiology, which comprises the following steps: obtaining a first time period; obtaining first historical condition information of a first patient within a first time period; obtaining first examination item information of a first patient; obtaining first classification rule information; classifying the first inspection item according to the first classification rule information to obtain second inspection item information and third inspection item information; obtaining first checking result information according to the second checking item information; obtaining a first real-time vital sign parameter according to the third examination item information; inputting first historical illness state information, first examination result information and first real-time vital sign parameters into a monitoring and early warning model as input information; obtaining a first output result of the monitoring and early warning model; obtaining a predetermined alarm strategy; according to the preset alarm strategy, the first output result is transmitted to the supervision nurse of the first patient, and the technical effect of improving the monitoring effect on the cardiology department patient is achieved.
Description
Technical Field
The invention relates to the technical field of patient monitoring, in particular to an intelligent monitoring system and method for physical signs of a patient in the department of cardiology.
Background
The cardiology department, i.e. the cardiovascular department, is a clinical department set by major internal medicine departments of all levels of hospitals for diagnosis and treatment of cardiovascular diseases, and the treated diseases include angina pectoris, hypertension, sudden death, arrhythmia, heart failure, premature beat, arrhythmia, myocardial infarction, cardiomyopathy, myocarditis, acute myocardial infarction and other cardiovascular diseases. With the continuous development of society and the continuous progress of science and technology, the pace of life of people is accelerated, the pressure of life is higher and higher, people have irregular diet and work and rest, and the like, so that the disease of the department of cardiology is ascends year by year, and the people with the disease are gradually younger.
However, the applicant of the present invention finds that the prior art has at least the following technical problems:
in the monitoring process of a patient in the cardiology department, the problems that the data monitoring effect is not ideal, and the identification and prediction of diseases are difficult to accurately realize by medical care personnel, so that the fatality rate of the patient is high exist in the prior art.
Disclosure of Invention
The embodiment of the invention provides an intelligent monitoring system and method for physical signs of a cardiology department patient, which solve the technical problems that in the monitoring process of the cardiology department patient in the prior art, the data monitoring effect is not ideal, and the medical staff is difficult to accurately help to realize the identification and prediction of diseases, so that the fatality rate of the patient is high, and achieve the technical effects of improving the monitoring effect of the cardiology department patient, accurately helping the medical staff to realize the identification and prediction of the diseases and improving the survival probability of the patient.
In view of the above problems, the embodiments of the present application are proposed to provide an intelligent monitoring system and method for physical signs of a cardiology patient.
In a first aspect, the present invention provides an intelligent monitoring system for physical signs of a cardiological patient, wherein the system comprises: a first obtaining unit configured to obtain a first time period; a second obtaining unit, configured to obtain first historical condition information of a first patient within the first time period; a third obtaining unit for obtaining first examination item information of the first patient; a fourth obtaining unit configured to obtain first classification rule information; a fifth obtaining unit, configured to obtain second inspection item information and third inspection item information after classifying the first inspection item according to the first classification rule information; a sixth obtaining unit configured to obtain first examination result information of the first patient based on the second examination item information; a seventh obtaining unit, configured to obtain a first real-time vital sign parameter of the first patient according to the third examination item information; the first input unit is used for inputting the first historical illness state information, the first examination result information and the first real-time vital sign parameters into the monitoring and early warning model as input information; an eighth obtaining unit, configured to obtain a first output result of the monitoring and early warning model, where the first output result includes first disease condition prediction result information and first risk early warning level information of the first patient; a ninth obtaining unit, configured to obtain a predetermined alarm policy; a first sending unit, configured to transmit the first output result to a supervising nurse of the first patient according to the predetermined alarm policy.
In a second aspect, the invention provides a method for intelligently monitoring the signs of a cardiological patient, wherein the method comprises the following steps: obtaining a first time period; obtaining first historical condition information of a first patient within the first time period; obtaining first examination item information of the first patient; obtaining first classification rule information; according to the first classification rule information, after the first inspection item is classified, second inspection item information and third inspection item information are obtained; obtaining first examination result information of the first patient according to the second examination item information; obtaining a first real-time vital sign parameter of the first patient according to the third examination item information; inputting the first historical illness state information, the first examination result information and the first real-time vital sign parameters into a monitoring and early warning model as input information; obtaining a first output result of the monitoring and early warning model, wherein the first output result comprises first disease condition prediction result information and first risk early warning grade information of the first patient; obtaining a predetermined alarm strategy; transmitting the first output result to a supervising nurse of the first patient according to the predetermined alarm policy.
In a third aspect, the present invention provides an intelligent monitoring system for physical signs of a cardiological patient, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the system of the first aspect.
One or more technical solutions in the embodiments of the present application have at least one or more of the following technical effects:
the embodiment of the invention provides an intelligent monitoring system and method for physical signs of a patient in the department of cardiology, wherein the method comprises the following steps: obtaining a first time period; obtaining first historical condition information of a first patient within the first time period; obtaining first examination item information of the first patient; obtaining first classification rule information; according to the first classification rule information, after the first inspection item is classified, second inspection item information and third inspection item information are obtained; obtaining first examination result information of the first patient according to the second examination item information; obtaining a first real-time vital sign parameter of the first patient according to the third examination item information; inputting the first historical illness state information, the first examination result information and the first real-time vital sign parameters into a monitoring and early warning model as input information; obtaining a first output result of the monitoring and early warning model, wherein the first output result comprises first disease condition prediction result information and first risk early warning grade information of the first patient; obtaining a predetermined alarm strategy; according to predetermined alarm strategy will first output result transmits for first patient's supervision nurse to having solved among the prior art to the monitoring process of intracardiac branch of academic or vocational study patient, it is unsatisfactory to have data monitoring effect, being difficult to accurate help medical personnel and realizing the discernment and the prediction of disease, leading to the high technical problem of patient's fatality rate, reached and improved the monitoring effect to intracardiac branch of academic or vocational study patient, accurate help medical personnel realizes the discernment and the prediction of disease, improve patient survival probability's technical effect.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
FIG. 1 is a schematic flow chart of a method for intelligently monitoring the signs of a cardiology patient in an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an intelligent monitoring system for signs of a cardiology patient according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of another exemplary electronic device in an embodiment of the present invention.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a fifth obtaining unit 15, a sixth obtaining unit 16, a seventh obtaining unit 17, a first input unit 18, an eighth obtaining unit 19, a ninth obtaining unit 20, a first transmitting unit 21, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 305.
Detailed Description
The embodiment of the invention provides an intelligent monitoring system and method for physical signs of a cardiology department patient, which are used for solving the technical problems that in the monitoring process of the cardiology department patient in the prior art, the data monitoring effect is not ideal, and the medical staff is difficult to accurately help to realize the identification and prediction of diseases, so that the fatality rate of the patient is high, thereby achieving the technical effects of improving the monitoring effect of the cardiology department patient, accurately helping the medical staff to realize the identification and prediction of the diseases and improving the survival probability of the patient.
Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Summary of the application
The cardiology department, i.e. the cardiovascular department, is a clinical department set by major internal medicine departments of all levels of hospitals for diagnosis and treatment of cardiovascular diseases, and the treated diseases include angina pectoris, hypertension, sudden death, arrhythmia, heart failure, premature beat, arrhythmia, myocardial infarction, cardiomyopathy, myocarditis, acute myocardial infarction and other cardiovascular diseases. With the continuous development of society and the continuous progress of science and technology, the pace of life of people is accelerated, the pressure of life is higher and higher, people have irregular diet and work and rest, and the like, so that the disease of the department of cardiology is ascends year by year, and the people with the disease are gradually younger. However, in the monitoring process of the patients in the cardiology department, the problems that the data monitoring effect is not ideal, and the identification and prediction of the diseases are difficult to accurately help medical care personnel are caused, so that the fatality rate of the patients is high.
In order to solve the technical problems, the technical scheme provided by the invention has the following general idea:
the embodiment of the application provides an intelligent monitoring system and method for physical signs of a cardiology department patient, wherein the method comprises the following steps: obtaining a first time period; obtaining first historical condition information of a first patient within the first time period; obtaining first examination item information of the first patient; obtaining first classification rule information; according to the first classification rule information, after the first inspection item is classified, second inspection item information and third inspection item information are obtained; obtaining first examination result information of the first patient according to the second examination item information; obtaining a first real-time vital sign parameter of the first patient according to the third examination item information; inputting the first historical illness state information, the first examination result information and the first real-time vital sign parameters into a monitoring and early warning model as input information; obtaining a first output result of the monitoring and early warning model, wherein the first output result comprises first disease condition prediction result information and first risk early warning grade information of the first patient; obtaining a predetermined alarm strategy; transmitting the first output result to a supervising nurse of the first patient according to the predetermined alarm policy.
After the fundamental principle of the present application is introduced, the technical solutions of the present invention are described in detail with reference to the accompanying drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.
Example one
Fig. 1 is a schematic flow chart of an intelligent monitoring method for physical signs of a cardiology department patient according to an embodiment of the present invention. As shown in fig. 1, an embodiment of the present invention provides an intelligent monitoring method for physical signs of a cardiology patient, including:
step 100: obtaining a first time period;
step 200: obtaining first historical condition information of a first patient within the first time period;
specifically, the first time period is a preset time period, and may be set according to actual conditions in actual setting. After the first time period is set, first historical illness state information of the first patient in the first time period can be further acquired, the first historical illness state information is related illness state of the patient going to a hospital for a doctor in the first time period, for example, data transmission can be carried out between the current hospital for the doctor and other hospitals, and then historical illness state of the patient can be acquired.
Step 300: obtaining first examination item information of the first patient;
specifically, the first examination item information is specific subject information required to be examined, such as electrocardiogram, blood pressure, heart rate, body temperature, urine routine, coronary angiography, coronary CTA, etc., prescribed by the doctor according to the actual condition of the first patient when the first patient is in the cardiology department of the first hospital.
Step 400: obtaining first classification rule information;
step 500: according to the first classification rule information, after the first inspection item is classified, second inspection item information and third inspection item information are obtained;
specifically, the first classification rule information is a scheme for classifying examination items of a patient that is set in advance, and the second examination item information and the third examination item information may be obtained after the first examination items are classified according to the first classification rule information. For example, the examination items may be classified according to their attributes, for example, some examination items have a short examination time, and may obtain the examination results in time, such as a blood routine and a urine routine, while some examination items have a long duration, and require a certain time to monitor and maintain for a certain time to obtain the final examination results, such as a 24-hour monitoring of the heart rate, blood pressure, and electrocardiogram of the patient.
Step 600: obtaining first examination result information of the first patient according to the second examination item information;
step 700: obtaining a first real-time vital sign parameter of the first patient according to the third examination item information;
specifically, after the classification is completed, first examination result information of the first patient can be obtained for second examination item information, the second examination item is a related examination item with a shorter examination time and a faster result, and then the first examination result of the first patient for the second examination item can be obtained, such as a blood routine, a urine routine, a heart hypercolor and the like; similarly, the first real-time vital sign parameters of the first patient corresponding to the third examination item can be obtained in real time, the third examination item at the moment is a related examination item needing to be monitored dynamically in real time for a certain duration, and then the real-time vital sign parameters of the first patient can be obtained according to the corresponding subject information of the third examination item, for example, the patient needs to be monitored for 24 hours, such as an electrocardiogram and a heart rate, so that the patient's condition can be confirmed more accurately, and in the monitoring process, the real-time parameters of the electrocardiogram and the heart rate of the patient can be obtained in real time.
Step 800: inputting the first historical illness state information, the first examination result information and the first real-time vital sign parameters into a monitoring and early warning model as input information;
step 900: obtaining a first output result of the monitoring and early warning model, wherein the first output result comprises first disease condition prediction result information and first risk early warning grade information of the first patient;
further, the first historical illness state information, the first examination result information and the first real-time vital sign parameter are used as input information and input into the monitoring and early warning model, and step 800 in the embodiment of the present application further includes:
step 810: inputting the first historical illness state information, first examination result information and first real-time vital sign parameters into a monitoring and early warning model as input information, wherein the model is trained by using a plurality of groups of training data, and each group of training data in the plurality of groups of training data comprises: the first historical condition information, the first examination result information, the first real-time vital sign parameters, first identification information identifying first condition prediction result information of the first patient, and second identification information identifying first risk early warning level information;
step 820: obtaining a first output result of the monitoring and early warning model, wherein the first output result comprises first disease prediction result information and first risk early warning grade information of the first patient.
Specifically, after first historical illness state information, first examination result information and first real-time vital sign parameters of a first patient are obtained, the first historical illness state information, the first examination result information and the first real-time vital sign parameters are respectively used as input data and input into a monitoring and early warning model, first illness state prediction result information of the first patient and first risk early warning grade information corresponding to the first illness state prediction result information are obtained through output information of the model, wherein the first illness state prediction result is a possible illness type of the patient analyzed and obtained based on big data, the first historical illness state information, the first examination result information and the first real-time vital sign parameters, the first risk early warning grade information is a risk index of a current state of the patient analyzed and obtained according to the first historical illness state information, the first examination result information and the first real-time vital sign parameters, and then can realize the monitoring to the real-time condition of the patient through this monitoring early warning model, still can realize the prediction and the early warning to the dangerous condition simultaneously. Furthermore, the training model is a neural network model in the machine learning model, and the machine learning model can continuously learn through a large amount of data, further continuously correct the model, and finally obtain satisfactory experience to process other data. The machine model is obtained by training a plurality of groups of training data, and the process of training the neural network model by the training data is essentially the process of supervised learning. The training model in the embodiment of the application is obtained by utilizing machine learning training through a plurality of groups of training data, and each group of training data in the plurality of groups comprises: the first patient condition prediction system comprises first historical condition information, first examination result information, first real-time vital sign parameters, first identification information for identifying first condition prediction result information of a first patient, and second identification information for identifying first risk early warning grade information.
The first identification information of the first disease condition prediction result information of the first patient and the second identification information of the first risk early warning grade information are respectively used as supervision data. And inputting the first historical illness state information, the first examination result information and the first real-time vital sign parameters into each group of training data, and determining that the output information of the training model reaches a convergence state. Comparing the first identification information of the first disease condition prediction result information of the first patient and the second identification information of the first risk early warning grade information with the output result of the training model, and when the first identification information of the first disease condition prediction result information of the first patient and the second identification information of the first risk early warning grade information of the first patient are consistent, finishing the supervised learning of the group of data and carrying out the supervised learning of the next group of data; when the first patient condition prediction result information is inconsistent with the first risk early warning grade information, the training model carries out self-correction until the output result of the training model is consistent with the first identification information of the first patient condition prediction result information of the identified first patient and the second identification information of the first risk early warning grade information, the supervised learning of the group is finished, and the supervised learning of the next group of data is carried out; and (4) through supervised learning of a large amount of data, enabling the output result of the machine learning model to reach a convergence state, and finishing the supervised learning. Through the process of supervising and learning the training model, the first identification information of the first patient's condition prediction result information and the second identification information of the first risk early warning grade information which are output by the training model are more accurate, the first patient's condition prediction result information and the first risk early warning grade information which can be accurate are achieved, accurate monitoring of the real-time state of the patient is achieved, the intelligent degree of medical treatment is improved, and the technical effect of preventing the occurrence of accidents of the patient during treatment is achieved.
Step 1000: obtaining a predetermined alarm strategy;
step 1100: transmitting the first output result to a supervising nurse of the first patient according to the predetermined alarm policy.
Specifically, a predetermined alarm strategy is obtained, the predetermined alarm strategy is a preset early warning scheme, and specifically, different alarm emergency degrees can be set in the alarm system according to different disease severity degrees of patients. For example, when a patient has an accident and is life-threatening, a high-level alarm program can be started, such as setting to red, and the call frequency is high, and a plurality of nurses can be called at the same time. Like this, according to predetermined alarm strategy, can transmit first output result for first patient's supervision nurse, then after the nurse received first output result, can be according to the alarm procedure of difference, take different first aid measures etc. thereby solved among the prior art to the monitoring process of heart internal medicine patient, it is unsatisfactory to have data monitoring effect, be difficult to accurate help medical personnel to realize the discernment and the prediction of disease, lead to the high technical problem of patient's fatality rate, the improvement has been reached to the monitoring effect of heart internal medicine patient, accurate help medical personnel realize the discernment and the prediction of disease, improve the technical effect of patient survival probability.
Further, in order to obtain more accurate first risk early warning level information, step 900 in this embodiment of the present application further includes:
step 910: obtaining first clinical performance information based on big data according to the first disease condition prediction result information;
step 920: obtaining first real-time performance information for the first patient;
step 930: obtaining first comparison information according to the first real-time performance information and the first clinical performance information;
step 940: determining whether a first correction instruction is obtained or not according to the first comparison information;
step 950: and correcting the first risk early warning grade information according to the first correction instruction.
Specifically, according to the first disease condition prediction result information, first clinical performance information can be obtained based on big data, the first clinical performance information at the moment is clinical performance characteristics related to the first disease condition, further, current real-time performance information of the first patient can be acquired, and then, after the first real-time performance information and the first clinical performance information are compared and analyzed, first comparison information between the first real-time performance information and the first clinical performance information can be obtained; and then, whether a first correction instruction is obtained or not can be determined according to the first comparison information, that is, when the first real-time performance information is consistent with the first clinical performance information, the first correction instruction does not need to be generated, otherwise, when the difference between the first real-time performance information and the first clinical performance information is large, the first correction instruction needs to be generated, and then under the instruction of the first correction instruction, the first risk early warning grade information of the first patient can be corrected, so that the technical effects of accurately obtaining the first risk early warning grade and timely alarming when a problem occurs are achieved.
Further, determining whether to obtain a first correction instruction according to the first comparison information, in step 940 of this embodiment of the present application, further includes:
step 941: taking the first real-time performance information as an abscissa;
step 942: constructing a two-dimensional rectangular coordinate system by taking the first clinical performance information as a longitudinal coordinate;
step 943: and constructing a logistic regression line in the two-dimensional rectangular coordinate system according to the logistic regression model to obtain a first comparison model, wherein one side of the logistic regression line represents a first output result, the other side of the logistic regression line represents a second output result, the first output result is that a first correction instruction is obtained, and the second output result is that the first correction instruction is not obtained.
Specifically, the logistic regression model is a machine learning model reflecting the relationship between independent variables and dependent variables, a two-dimensional rectangular coordinate system is constructed by using first real-time performance information as abscissa and first clinical performance information as ordinate, a logistic regression line is obtained based on the logistic regression model through the two-dimensional rectangular coordinate system, one side of the logistic regression line represents a first output result, and the other side of the logistic regression line represents a second output result, for example, when the first clinical performance information is fixed, there is a performance range of the first real-time performance information matching the first clinical performance information, when the current real-time characteristic information is monitored to be within the clinical performance range, the position is on the side of the logistic regression line representing the first output result, which indicates that the first correction instruction does not need to be generated at this time, when the current real-time characteristic information is monitored to be not within the clinical performance range, the position at this time is on the other side of the logistic regression line, representing a second output result. The relation between the first real-time performance information and the first clinical performance information is better reflected through a logistic regression model, and the technical effect of accurately diagnosing whether alarm information correction is needed or not is achieved through the matching degree of the first real-time performance information and the first clinical performance information.
Further, in order to achieve the purpose of correcting the inspection scheme corresponding to the image inspection subject, step 500 in the embodiment of the present application further includes:
step 510: judging whether the second inspection item information comprises the imaging inspection;
step 520: if yes, obtaining an image examination subject corresponding to the imaging examination;
step 530: obtaining second correction information according to the first historical illness state information and the image examination subject information;
step 540: and correcting the inspection scheme corresponding to the image inspection subject according to the second correction information.
Specifically, it is determined whether the second examination item information includes an imaging examination, such as a cardiac B-ultrasound examination, a coronary angiography, a coronary CTA, or the like, if the judgment result includes the imaging examination, the first historical illness state information and the image examination subject information need to be further compared and analyzed, then second correction information is obtained, and further according to the second correction information, the examination protocol corresponding to the imaging examination subject is modified, for example for patients suffering from arrhythmia, if coronary angiography is used, the sharpness of the examination image is affected, and the patient's actual physical health status is now taken into account, the method and the device have the advantages that the actual examination items of the patient are adaptively adjusted, so that the examination items can be timely adjusted according to the actual condition of the patient, and the subsequent accurate diagnosis and treatment of the illness state of the patient can be conveniently realized.
Further, after obtaining the first real-time vital sign parameter of the first patient according to the third examination item information, step 700 in this embodiment of the present application further includes:
step 710: obtaining a first monitoring duration according to the third inspection item information, wherein the first monitoring duration is an item with the longest monitoring time in the third inspection item information;
step 720: obtaining the body temperature change information corresponding to different body parts of the first patient within the first monitoring duration;
step 730: obtaining first body temperature information of the first patient according to the body temperature change information corresponding to different body parts;
step 740: judging whether the first body temperature information exceeds a preset temperature or not;
step 750: if yes, obtaining first reminding information;
step 760: and transmitting the first reminding information and the first output result to a supervision nurse of the first patient together.
Specifically, a first monitoring duration may be determined from the third examination item information, in this embodiment, the first monitoring duration is taken as the item with the longest monitoring time in the third examination item information, so as to obtain various body temperature variation information corresponding to different body parts of the first patient, such as an underarm temperature, an oral cavity temperature, an esophagus temperature, and the like, within the first monitoring duration, and after performing comprehensive comparison and analysis on the obtained various body temperature variation information corresponding to different body parts, the first body temperature information of the first patient may be obtained, and then it is determined whether the first body temperature information exceeds a predetermined temperature, where the predetermined temperature is a temperature representation of a healthy user, and if it is determined that the first patient exceeds a normal temperature, which indicates that the temperature of the patient is too high, at this time, a first reminding information needs to be generated, and then the first reminding information and the first output result are jointly transmitted to a monitoring nurse of the first patient, the aim of reminding a nurse to timely take related protective measures aiming at the phenomenon of the overhigh body temperature of the patient while making a decision based on the first output result is fulfilled.
Further, after obtaining the first examination item information of the first patient, step 300 of the embodiment of the present application further includes:
step 310: obtaining a second time period;
step 320: obtaining first behavior information of the first patient over the second time period, wherein the first behavior information comprises emotional behavior, and/or physiological behavior, and/or motor behavior;
step 330: obtaining third correction information according to the first behavior information;
step 340: and correcting the first checking item information according to the third correction information.
Specifically, the second time period is also a preset time period, and may be set according to actual needs, which is not specifically limited in this embodiment, for example, the last 1 hour, two hours, five hours, and the like may be set, so as to obtain first behavior information of the first patient in the second time period, where the first behavior information includes emotional behaviors, such as too much emotional activity or large fluctuation, and/or physiological behaviors, such as too much strength in the toilet, and/or athletic behaviors, such as a severe exercise, and the like, and further, according to the first behavior information of the patient, third correction information may be generated to correct the first examination item information, so as to prevent an unexpected phenomenon from occurring when the patient performs an inappropriate item.
Example two
Based on the same inventive concept as the method for intelligently monitoring the physical signs of the cardiology department patient in the foregoing embodiment, the present invention further provides an intelligent monitoring system for the physical signs of the cardiology department patient, as shown in fig. 2, wherein the system includes:
a first obtaining unit 11, wherein the first obtaining unit 11 is used for obtaining a first time period;
a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain first historical condition information of the first patient in the first time period;
a third obtaining unit 13, wherein the third obtaining unit 13 is configured to obtain first examination item information of the first patient;
a fourth obtaining unit 14, where the fourth obtaining unit 14 is configured to obtain the first classification rule information;
a fifth obtaining unit 15, where the fifth obtaining unit 15 is configured to obtain second inspection item information and third inspection item information after classifying the first inspection item according to the first classification rule information;
a sixth obtaining unit 16, wherein the sixth obtaining unit 16 is configured to obtain first examination result information of the first patient according to the second examination item information;
a seventh obtaining unit 17, wherein the seventh obtaining unit 17 is configured to obtain a first real-time vital sign parameter of the first patient according to the third examination item information;
the first input unit 18 is configured to input the first historical illness state information, the first examination result information, and the first real-time vital sign parameter as input information to the monitoring and early warning model;
an eighth obtaining unit 19, configured to obtain a first output result of the monitoring and early warning model, where the first output result includes first disease condition prediction result information and first risk early warning level information of the first patient;
a ninth obtaining unit 20, said ninth obtaining unit 20 being configured to obtain a predetermined alarm policy;
a first sending unit 21, where the first sending unit 21 is configured to transmit the first output result to a supervising nurse of the first patient according to the predetermined alarm policy.
Further, the system further comprises:
a tenth obtaining unit for obtaining first clinical manifestation information based on big data according to the first disease prediction result information;
an eleventh obtaining unit for obtaining first real-time performance information of the first patient;
a twelfth obtaining unit, configured to obtain first comparison information according to the first real-time performance information and the first clinical performance information;
a first determining unit, configured to determine whether to obtain a first correction instruction according to the first comparison information;
and the first correcting unit is used for correcting the first risk early warning grade information according to the first correcting instruction.
Further, the determining whether to obtain a first correction instruction according to the first comparison information further includes:
the first operation unit is used for taking the first real-time performance information as an abscissa;
a second operation unit for constructing a two-dimensional rectangular coordinate system using the first clinical performance information as a vertical coordinate;
and the third operation unit is used for constructing a logistic regression line in the two-dimensional rectangular coordinate system according to the logistic regression model to obtain a first comparison model, wherein one side of the logistic regression line represents a first output result, the other side of the logistic regression line represents a second output result, the first output result is that the first correction instruction is obtained, and the second output result is that the first correction instruction is not obtained.
Further, the system further comprises:
a first judging unit configured to judge whether the second examination item information includes an imaging examination;
a thirteenth obtaining unit, configured to obtain an image examination subject corresponding to the imaging examination if included;
a fourteenth obtaining unit, configured to obtain second correction information according to the first historical disease information and the image examination subject information;
and the second correction unit is used for correcting the inspection scheme corresponding to the image inspection subject according to the second correction information.
Further, after obtaining the first real-time vital sign parameter of the first patient according to the third examination item information, the system further includes:
a fifteenth obtaining unit, configured to obtain a first monitoring duration according to the third inspection item information, where the first monitoring duration is an item with a longest monitoring time in the third inspection item information;
a sixteenth obtaining unit, configured to obtain body temperature change information corresponding to different body parts of the first patient within the first monitoring duration;
a seventeenth obtaining unit, configured to obtain first body temperature information of the first patient according to the body temperature change information corresponding to the different body parts;
a second determination unit configured to determine whether the first body temperature information exceeds a predetermined temperature;
an eighteenth obtaining unit, configured to obtain the first reminding information if the first reminding information exceeds the first reminding information;
and the second sending unit is used for transmitting the first reminding information and the first output result to the supervision nurse of the first patient together.
Further, after obtaining the first examination item information of the first patient, the system further includes:
a nineteenth obtaining unit for obtaining a second time period;
a twentieth obtaining unit for obtaining first behavior information of the first patient over the second time period, wherein the first behavior information comprises emotional behavior, and/or physiological behavior, and/or motor behavior;
a twenty-first obtaining unit, configured to obtain third correction information according to the first behavior information;
a third correction unit configured to correct the first inspection item information based on the third correction information.
Further, the first historical illness state information, the first examination result information and the first real-time vital sign parameter are used as input information and input into a monitoring and early warning model, and the system further comprises:
the second input unit is used for inputting the first historical illness state information, the first examination result information and the first real-time vital sign parameters into a monitoring and early warning model as input information, the model is trained by using multiple groups of training data, and each group of training data in the multiple groups of training data comprises: the first historical condition information, the first examination result information, the first real-time vital sign parameters, first identification information identifying first condition prediction result information of the first patient, and second identification information identifying first risk early warning level information;
a twenty-second obtaining unit, configured to obtain a first output result of the monitoring and early warning model, where the first output result includes first disease condition prediction result information and first risk early warning level information of the first patient.
Various changes and specific examples of the foregoing intelligent monitoring method for physical signs of a cardiology patient in the first embodiment of fig. 1 are also applicable to the intelligent monitoring system for physical signs of a cardiology patient of the present embodiment, and through the foregoing detailed description of the intelligent monitoring method for physical signs of a cardiology patient, those skilled in the art can clearly know the implementation method of the intelligent monitoring system for physical signs of a cardiology patient in the present embodiment, so for the sake of brevity of the description, detailed descriptions thereof are omitted here.
EXAMPLE III
Based on the same inventive concept as the method for intelligently monitoring the physical signs of the cardiological patient in the foregoing embodiment, the present invention further provides an exemplary electronic device, as shown in fig. 3, including a memory 304, a processor 302, and a computer program stored in the memory 304 and executable on the processor 302, wherein the processor 302 executes the computer program to implement the steps of any one of the methods for intelligently monitoring the physical signs of the cardiological patient.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium. The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
One or more technical solutions in the embodiments of the present application have at least one or more of the following technical effects:
the embodiment of the invention provides an intelligent monitoring system and method for physical signs of a patient in the department of cardiology, wherein the method comprises the following steps: obtaining a first time period; obtaining first historical condition information of a first patient within the first time period; obtaining first examination item information of the first patient; obtaining first classification rule information; according to the first classification rule information, after the first inspection item is classified, second inspection item information and third inspection item information are obtained; obtaining first examination result information of the first patient according to the second examination item information; obtaining a first real-time vital sign parameter of the first patient according to the third examination item information; inputting the first historical illness state information, the first examination result information and the first real-time vital sign parameters into a monitoring and early warning model as input information; obtaining a first output result of the monitoring and early warning model, wherein the first output result comprises first disease condition prediction result information and first risk early warning grade information of the first patient; obtaining a predetermined alarm strategy; according to predetermined alarm strategy will first output result transmits for first patient's supervision nurse to having solved among the prior art to the monitoring process of intracardiac branch of academic or vocational study patient, it is unsatisfactory to have data monitoring effect, being difficult to accurate help medical personnel and realizing the discernment and the prediction of disease, leading to the high technical problem of patient's fatality rate, reached and improved the monitoring effect to intracardiac branch of academic or vocational study patient, accurate help medical personnel realizes the discernment and the prediction of disease, improve patient survival probability's technical effect.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. An intelligent monitoring system for cardiology patient signs, wherein the system comprises:
a first obtaining unit configured to obtain a first time period;
a second obtaining unit, configured to obtain first historical condition information of a first patient within the first time period;
a third obtaining unit for obtaining first examination item information of the first patient;
a fourth obtaining unit configured to obtain first classification rule information;
a fifth obtaining unit, configured to obtain second inspection item information and third inspection item information after classifying the first inspection item according to the first classification rule information;
a sixth obtaining unit configured to obtain first examination result information of the first patient based on the second examination item information;
a seventh obtaining unit, configured to obtain a first real-time vital sign parameter of the first patient according to the third examination item information;
the first input unit is used for inputting the first historical illness state information, the first examination result information and the first real-time vital sign parameters into the monitoring and early warning model as input information;
an eighth obtaining unit, configured to obtain a first output result of the monitoring and early warning model, where the first output result includes first disease condition prediction result information and first risk early warning level information of the first patient;
a ninth obtaining unit, configured to obtain a predetermined alarm policy;
a first sending unit, configured to transmit the first output result to a supervising nurse of the first patient according to the predetermined alarm policy.
2. The system of claim 1, wherein the system further comprises:
a tenth obtaining unit for obtaining first clinical manifestation information based on big data according to the first disease prediction result information;
an eleventh obtaining unit for obtaining first real-time performance information of the first patient;
a twelfth obtaining unit, configured to obtain first comparison information according to the first real-time performance information and the first clinical performance information;
a first determining unit, configured to determine whether to obtain a first correction instruction according to the first comparison information;
and the first correcting unit is used for correcting the first risk early warning grade information according to the first correcting instruction.
3. The system of claim 1, wherein the determining whether to obtain a first corrective instruction based on the first comparison information further comprises:
the first operation unit is used for taking the first real-time performance information as an abscissa;
a second operation unit for constructing a two-dimensional rectangular coordinate system using the first clinical performance information as a vertical coordinate;
and the third operation unit is used for constructing a logistic regression line in the two-dimensional rectangular coordinate system according to the logistic regression model to obtain a first comparison model, wherein one side of the logistic regression line represents a first output result, the other side of the logistic regression line represents a second output result, the first output result is that the first correction instruction is obtained, and the second output result is that the first correction instruction is not obtained.
4. The system of claim 1, wherein the system further comprises:
a first judging unit configured to judge whether the second examination item information includes an imaging examination;
a thirteenth obtaining unit, configured to obtain an image examination subject corresponding to the imaging examination if included;
a fourteenth obtaining unit, configured to obtain second correction information according to the first historical disease information and the image examination subject information;
and the second correction unit is used for correcting the inspection scheme corresponding to the image inspection subject according to the second correction information.
5. The system of claim 1, wherein after obtaining the first real-time vital sign parameters of the first patient from the third examination item information, the system further comprises:
a fifteenth obtaining unit, configured to obtain a first monitoring duration according to the third inspection item information, where the first monitoring duration is an item with a longest monitoring time in the third inspection item information;
a sixteenth obtaining unit, configured to obtain body temperature change information corresponding to different body parts of the first patient within the first monitoring duration;
a seventeenth obtaining unit, configured to obtain first body temperature information of the first patient according to the body temperature change information corresponding to the different body parts;
a second determination unit configured to determine whether the first body temperature information exceeds a predetermined temperature;
an eighteenth obtaining unit, configured to obtain the first reminding information if the first reminding information exceeds the first reminding information;
and the second sending unit is used for transmitting the first reminding information and the first output result to the supervision nurse of the first patient together.
6. The system of claim 1, wherein after the obtaining the first examination item information for the first patient, the system further comprises:
a nineteenth obtaining unit for obtaining a second time period;
a twentieth obtaining unit for obtaining first behavior information of the first patient over the second time period, wherein the first behavior information comprises emotional behavior, and/or physiological behavior, and/or motor behavior;
a twenty-first obtaining unit, configured to obtain third correction information according to the first behavior information;
a third correction unit configured to correct the first inspection item information based on the third correction information.
7. The system of claim 1, wherein the first historical condition information, first examination result information, and first real-time vital sign parameters are input as input information to a monitoring and pre-warning model, the system further comprising:
the second input unit is used for inputting the first historical illness state information, the first examination result information and the first real-time vital sign parameters into a monitoring and early warning model as input information, the model is trained by using multiple groups of training data, and each group of training data in the multiple groups of training data comprises: the first historical condition information, the first examination result information, the first real-time vital sign parameters, first identification information identifying first condition prediction result information of the first patient, and second identification information identifying first risk early warning level information;
a twenty-second obtaining unit, configured to obtain a first output result of the monitoring and early warning model, where the first output result includes first disease condition prediction result information and first risk early warning level information of the first patient.
8. A method of intelligent monitoring of cardiology patient signs, wherein the method comprises:
obtaining a first time period;
obtaining first historical condition information of a first patient within the first time period;
obtaining first examination item information of the first patient;
obtaining first classification rule information;
according to the first classification rule information, after the first inspection item is classified, second inspection item information and third inspection item information are obtained;
obtaining first examination result information of the first patient according to the second examination item information;
obtaining a first real-time vital sign parameter of the first patient according to the third examination item information;
inputting the first historical illness state information, the first examination result information and the first real-time vital sign parameters into a monitoring and early warning model as input information;
obtaining a first output result of the monitoring and early warning model, wherein the first output result comprises first disease condition prediction result information and first risk early warning grade information of the first patient;
obtaining a predetermined alarm strategy;
transmitting the first output result to a supervising nurse of the first patient according to the predetermined alarm policy.
9. An intelligent monitoring system for signs of a cardiological patient comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the system of any one of claims 1-7 when executing the program.
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| CN202110346535.4A CN113053518A (en) | 2021-03-31 | 2021-03-31 | Intelligent monitoring system and method for physical signs of cardiology department patients |
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