Intelligent early warning system for postoperative complications of anesthetized patients

Abstract

The application relates to the technical field of anesthesia post-complication monitoring, in particular to an intelligent early warning system for post-anesthesia complications of surgical patients. The system obtains the intraoperative risk degree according to the size and change of the intraoperative body index data of the patient in each first preset time period, the past medical history score and the allergy history score of the patient, and then judges the intraoperative anesthesia complication patients and the intraoperative normal patients in real time; the postoperative risk degree is obtained according to the size of the postoperative body index data of the intraoperative normal patient in each second preset time period, the consciousness state score, the nervous system score, the wound recovery score and the wakefulness state of the intraoperative normal patient, and the intraoperative risk degree of the intraoperative normal patient, and then the postoperative anesthesia complication patients and the postoperative normal patients are judged in real time. The intraoperative risk degree and the postoperative risk degree are accurately obtained in real time, so that the post-anesthesia complications of the patient can be accurately detected in time, and measures can be taken in time, and the harm of the post-anesthesia complications is effectively reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of post-anesthesia complication monitoring, in particular to an intelligent early warning system for post-anesthesia complications of surgical patients. BACKGROUND

[0002] Surgical anesthesia is an important part of modern surgery, which is directly related to the life safety of patients and the efficacy of surgery. However, while anesthetics inhibit the consciousness and pain perception of patients, they also inhibit the respiratory, circulatory, and nervous systems to varying degrees, leading to a variety of postoperative complications such as respiratory depression, hypotension, bradycardia, delayed awakening, and postoperative cognitive dysfunction. These complications not only interfere with the normal physiological functions of patients, prolong the recovery period, and increase medical costs, but in severe cases, they can even endanger life.

[0003] Currently, the monitoring and early warning of post-anesthesia complications of surgical patients in clinical practice mainly rely on the manual judgment of medical staff through regular rounds, checking of monitoring device data, and personal clinical experience. This method is highly dependent on subjective experience and lacks objective quantitative standards. At the same time, the information integration capability is limited, which can easily form an "information island". Moreover, it does not take into account that the occurrence and development of post-anesthesia complications is a dynamic process, and the early physiological changes are often subtle and fleeting. The existing method is difficult to capture these subtle abnormal trends in a timely manner and provide prospective early warning. Often, anesthesia complications are not discovered until they have shown obvious clinical symptoms, missing the best intervention and prevention window. Therefore, it is difficult to accurately detect post-anesthesia complications of surgical patients in a timely manner using the existing method. SUMMARY

[0004] In order to solve the technical problem that the existing method is difficult to accurately detect post-anesthesia complications of surgical patients in a timely manner, the purpose of the present application is to provide an intelligent early warning system for post-anesthesia complications of surgical patients, and the technical solution adopted is as follows:

[0005] The present application provides an intelligent early warning system for post-anesthesia complications of surgical patients, which comprises:

[0006] A data acquisition module is used to acquire the patient's past medical history score and allergy history score, real-time acquire various intraoperative physical index data of the patient during surgery, and real-time acquire various postoperative physical index data, consciousness state score, nervous system score, and wound recovery score of the patient after surgery;

[0007] The intraoperative anesthesia complication analysis module is used for dividing a first preset time period in real time during a surgery process, obtaining an intraoperative risk degree of each patient in each first preset time period according to the size and change of each kind of intraoperative physical index data of each patient in each first preset time period, and the past medical history score and the allergy history score of each patient; and judging the intraoperative anesthesia complication patient and the intraoperative normal patient in real time based on the intraoperative risk degree.

[0008] The postoperative anesthesia complication analysis module is used for dividing a second preset time period in real time after the surgery, obtaining a postoperative risk degree of each intraoperative normal patient in each second preset time period according to the size of each kind of postoperative physical index data, the consciousness state score, the nervous system score, the wound recovery score and the wake state of each intraoperative normal patient in each second preset time period, and the size and change of the intraoperative risk degree of each intraoperative normal patient; and judging the postoperative anesthesia complication patient and the postoperative normal patient in real time based on the postoperative risk degree.

[0009] Further, the method for obtaining the intraoperative risk degree is as follows:

[0010] For any patient and any first preset time period of the patient, the abnormal degree of the physical index of the patient in the first preset time period is obtained according to the size and change of each kind of intraoperative physical index data of the patient in the first preset time period;

[0011] The addition result of the past medical history score and the allergy history score of the patient is taken as the basic score of the patient;

[0012] The addition result of the abnormal degree of the physical index and the basic score is normalized, and the normalized result is taken as the intraoperative risk degree of the patient in the first preset time period.

[0013] Further, the method for obtaining the abnormal degree of the physical index is as follows:

[0014] For any kind of intraoperative physical index data of the patient, the mean value of the difference of the intraoperative physical index data of the patient at all adjacent time points in the first preset time period is obtained as the change degree of the intraoperative physical index data of the patient in the first preset time period;

[0015] The normal range of the intraoperative physical index data is taken as a target range, and the intraoperative physical index data of the patient not in the target range in the first preset time period is taken as a first abnormal index data;

[0016] The minimum value of each first abnormal index data exceeding the target range is taken as an abnormal analysis value;

[0017] normalizing the product of the number of the first abnormal index data, the mean of the abnormal analysis value and the change degree, as the local abnormal degree of the kind of intraoperative physical index data of the patient in the first preset time period;

[0018] normalizing the product of the number of the first abnormal index data, the mean of the abnormal analysis value and the change degree, as the local abnormal degree of the kind of intraoperative physical index data of the patient in the first preset time period;

[0019] Further, the method for judging the intraoperative anesthesia complication patient and the intraoperative normal patient in real time based on the intraoperative risk degree is:

[0020] For any patient, the intraoperative risk degree of each first preset time period of the patient is acquired in real time, and when the intraoperative risk degree of the patient is greater than a preset intraoperative risk degree threshold, the patient is judged as an intraoperative anesthesia complication patient;

[0021] When the intraoperative risk degree of the patient is less than or equal to the preset intraoperative risk degree threshold, the patient is judged as an intraoperative normal patient.

[0022] Further, the method for acquiring the postoperative risk degree is:

[0023] For any intraoperative normal patient and any second preset time period of the intraoperative normal patient, according to the size of each postoperative physical index data in the second preset time period, the anesthesia complication first risk index of the second preset time period is acquired;

[0024] According to the consciousness state score, the nervous system score, the wound recovery score and the wakefulness state of the intraoperative normal patient in the second preset time period, the anesthesia complication second risk index of the second preset time period is acquired;

[0025] According to the size and change of the intraoperative risk degree of the intraoperative normal patient, the anesthesia reference risk index of the intraoperative normal patient is acquired;

[0026] The anesthesia complication first risk index, the anesthesia complication second risk index and the anesthesia reference risk index are added and normalized, and the result is taken as the postoperative risk degree of the second preset time period.

[0027] Further, the method for acquiring the anesthesia complication first risk index is:

[0028] For any postoperative physical index data of any intraoperative normal patient and any second preset time period, all the postoperative physical index data exceeding the normal range of the postoperative physical index data in the second preset time period are taken as second abnormal index data;

[0029] The time points corresponding to the second abnormal index data are all taken as abnormal time points, and the ratio of the duration corresponding to the abnormal time points to the total duration of the second preset time period is taken as the abnormal proportion of the postoperative physical index data of the type in the second preset time period;

[0030] The product of the mean of the second abnormal index data, the number of the second abnormal index data, and the abnormal proportion is normalized to obtain a risk analysis value of the postoperative physical index data of the type in the second preset time period;

[0031] The sum of the risk analysis values of all postoperative physical index data of the type in the second preset time period is taken as the first risk index of the anesthesia complication of the second preset time period.

[0032] Further, the method for obtaining the second risk index of the anesthesia complication is:

[0033] For any second preset time period of any intraoperative normal patient, when the intraoperative normal patient is awake in the second preset time period, the wakefulness state score of the second preset time period is set to 0;

[0034] When the intraoperative normal patient is not awake in the second preset time period, the wakefulness state score of the second preset time period is set to 1;

[0035] The sum of the mean of the consciousness state score, the mean of the nervous system score, the mean of the wound recovery score, and the wakefulness state score of the intraoperative normal patient in the second preset time period is normalized to obtain the second risk index of the anesthesia complication of the second preset time period.

[0036] Further, the method for obtaining the anesthesia reference risk index is:

[0037] For any intraoperative normal patient, the intraoperative risk degree of the intraoperative normal patient is arranged according to the time sequence of the corresponding first preset time period and fitted into a curve;

[0038] The difference between the maximum intraoperative risk degree and the minimum intraoperative risk degree in the curve is obtained as the intraoperative risk fluctuation degree;

[0039] The curve is divided by the extreme points to obtain local curve segments;

[0040] The last local curve segment of the curve is taken as a target curve segment, the mean of the tangent slopes of all intraoperative risk degrees on the target curve segment is obtained as the overall slope of the target curve;

[0041] The product of the intraoperative risk fluctuation degree, the overall slope, and the mean of all intraoperative risk degrees on the target curve segment is normalized to obtain the anesthesia reference risk index of the intraoperative normal patient.

[0042] Further, the method for judging the postoperative anesthesia complication patient and the postoperative normal patient in real time based on the postoperative risk degree comprises the following steps:

[0043] For any intraoperative normal patient, the postoperative risk degree of the intraoperative normal patient in each first preset time period is acquired in real time, and when the postoperative risk degree of the intraoperative normal patient is greater than a preset postoperative risk degree threshold value, the intraoperative normal patient is judged as a postoperative anesthesia complication patient.

[0044] When the postoperative risk degree of the intraoperative normal patient is less than or equal to the preset postoperative risk degree threshold value, the intraoperative normal patient is judged as a postoperative normal patient.

[0045] Further, the initial time point of the postoperation is the end time point of the surgery.

[0046] The present application has the following beneficial effects:

[0047] The present application first divides a first preset time period in real time during the operation process, which is beneficial to real-time, accurate and efficient analysis of the anesthesia complication risk of each patient during the operation process; then, according to the size and change of each intraoperative physical index data of each patient in each first preset time period, and the past medical history score and allergy history score of each patient, the intraoperative risk degree of each patient in each first preset time period is obtained, which accurately reflects the degree of anesthesia complications of each patient in each first preset time period; then, based on the intraoperative risk degree, the intraoperative anesthesia complication patient and the intraoperative normal patient are accurately judged in real time, so that the patient with anesthesia complications during the operation is detected in time, which is beneficial to taking corresponding measures for the patient with anesthesia complications during the operation, and reducing the harm of anesthesia complications; in order to analyze the anesthesia complications of the postoperative patient, a second preset time period is further divided in real time after the operation, which is beneficial to real-time, accurate and efficient analysis of the anesthesia complication risk of each intraoperative normal patient after the operation; therefore, according to the size of each postoperative physical index data, the consciousness state score, the nervous system score, the wound recovery score and the wakefulness state of each intraoperative normal patient in each second preset time period, and the size and change of the intraoperative risk degree of each intraoperative normal patient, the postoperative risk degree of each intraoperative normal patient in each second preset time period is obtained, which accurately reflects the degree of anesthesia complications of each intraoperative normal patient in each second preset time period; then, based on the postoperative risk degree, the postoperative anesthesia complication patient and the postoperative normal patient are accurately judged in real time, which is beneficial to taking corresponding measures for the postoperative anesthesia complication patient in time, and reducing the harm of anesthesia complications. The present application comprehensively captures various factors affecting anesthesia complications by integrating multi-source data, and predicts the anesthesia complications after the operation based on the historical data during the operation, which can more accurately determine the anesthesia complication risk, thereby significantly improving the accuracy of anesthesia complication prediction, improving the accuracy of real-time and dynamic anesthesia complication risk assessment of the patient, and being beneficial to early warning and intervention, and effectively reducing the anesthesia complication risk. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort.

[0049] Figure 1 The structural block diagram of the intelligent early warning system for anesthesia complications of surgical patients provided by an embodiment of the present application;

[0050] Figure 2A flow chart of a method for obtaining an intraoperative risk level according to an embodiment of the present application;

[0051] Figure 3 A flow chart of a method for obtaining a postoperative risk level according to an embodiment of the present application;

[0052] Figure 4 A schematic diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to further clarify the technical means and effects taken by the present application to achieve the predetermined object of the application, the following describes in detail the specific implementation, structure, features and effects of an intelligent postoperative complication warning system for a surgical patient according to the present application in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0055] The specific scheme of the intelligent postoperative complication warning system for a surgical patient according to the present application is described in detail below in combination with the accompanying drawings.

[0056] Embodiment 1:

[0057] The present application proposes an intelligent postoperative complication warning system for a surgical patient, please refer to Figure 1 which shows a structural block diagram of an intelligent postoperative complication warning system for a surgical patient according to an embodiment of the present application. The system comprises a data acquisition module 10, an intraoperative anesthesia complication analysis module 20 and a postoperative anesthesia complication analysis module 30.

[0058] The data acquisition module 10 is used to acquire the patient's past medical history score and allergy history score; to acquire various intraoperative physical index data of the patient in real time during the operation; and to acquire various postoperative physical index data, consciousness state score, nervous system score and wound recovery score of the patient after the operation.

[0059] Specifically, in order to accurately analyze the anesthetic reaction of each patient during or after the operation, so that various complications of the patient after anesthesia can be found in time, and measures can be taken in time to avoid accidents in the patient's body, the electronic medical record of the patient is first integrated to obtain the patient's medical history and allergy history, and then the influence degree of the medical history on the operation anesthesia is scored. When there is no influence degree, the medical history score is set to 0; when the influence degree is mild, the medical history score is set to 1; when the influence degree is mild or above, the medical history score is set to 3. Further, when the patient has no allergy history to anesthetic related drugs, the allergy history score is set to 0; when the patient has an allergy history to anesthetic related drugs, the allergy history score is set to 1. It should be noted that the implementer can set the medical history score and the allergy history score according to the actual situation, which is not limited herein. Further, the medical history score and the allergy history score of each patient are obtained.

[0060] In order to analyze the anesthetic effect of the patient during and after the operation in real time, so that the adverse effects of anesthesia on the patient's body can be found in time, the embodiment further obtains various intraoperative physical index data of the patient during the operation in real time; at the same time, various postoperative physical index data, consciousness state score, nervous system score and wound recovery score of the patient after the operation are obtained in real time. In the embodiment, the intraoperative physical index data includes heart rate, blood pressure and blood oxygen saturation; the postoperative physical index data includes respiratory rate, heart rate and blood pressure; it is known that the consciousness state, nervous system and wound recovery of the patient after the operation can indirectly reflect the anesthetic recovery of the patient after the operation, which is beneficial to further analyze the anesthetic risk of the patient after the operation. Therefore, the consciousness state of the patient is scored by the Glasgow Coma Scale, and the consciousness state score of each patient is obtained in real time; the nervous system of the patient is scored by the muscle strength grading standard, and the nervous system score of each patient is obtained in real time; the Glasgow Coma Scale and the muscle strength grading standard are both known contents, and will not be described herein. The wound recovery of the patient is scored by observing the state of the surgical wound, and the wound recovery score of each patient is obtained in real time, i.e. when the surgical wound has no redness, no bleeding and no exudation, the wound recovery score is set to 1; when the surgical wound has mild redness with small range, no bleeding and no exudation, the wound recovery score is set to 2; when the surgical wound has severe redness with large range, bleeding or exudation, the wound recovery score is set to 3. The implementer can set the consciousness state score, the nervous system score and the wound recovery score according to the actual situation, which is not limited herein. The embodiment sets the time interval between the adjacent two time points for obtaining data to 1 second, and the implementer can set the time interval between the adjacent two time points for obtaining data according to the actual situation, which is not limited herein.

[0061] It should be noted that the initial time after the operation is the end time of the operation, and the anesthesia risk observation time length after the operation is set to 24 hours in this embodiment, and the implementer can set the anesthesia risk observation time length after the operation according to the actual situation, which is not limited here.

[0062] The intraoperative anesthesia complication analysis module 20 is configured to divide a first preset time period in real time during the operation, obtain an intraoperative risk degree of each patient in each first preset time period according to the size and change of each intraoperative physical index data of each patient in each first preset time period, and the past medical history score and the allergy history score of each patient, and judge the intraoperative anesthesia complication patient and the intraoperative normal patient in real time based on the intraoperative risk degree.

[0063] Specifically, in actual situations, the occurrence time span of the post-anesthesia complications of different patients is uncertain, and the physical state of the patient is in dynamic change during the operation, and some post-anesthesia complications may only show slight changes in the physical index in the early stage and are not obvious, and are easy to be ignored. Therefore, in order to analyze the anesthesia risk of the patient in the operation process in a timely and accurate manner, the first preset time period is divided in real time during the operation in this embodiment, and the length of the first preset time period is set to 5 minutes in this embodiment, and the implementer can set the length of the first preset time period according to the actual situation, which is not limited here. That is, the first preset time period is divided in sequence from the start time of the operation. It should be noted that there is no other time between the adjacent two first preset time periods, that is, the adjacent two first preset time periods are continuous in time sequence and do not overlap.

[0064] In addition, considering that the basic disease information of different patients is different, which makes the post-anesthesia complication risk faced by each patient unique, therefore, in this embodiment, the basic disease information of the patient and the change of each intraoperative physical index data during the operation are combined to capture some potential complication risks of the patient during the operation, so as to timely give an early warning and avoid the post-anesthesia complication risk from causing an accident in the patient's body. Further, the intraoperative risk degree of each patient in each first preset time period is obtained according to the size and change of each intraoperative physical index data of each patient in each first preset time period, and the past medical history score and the allergy history score of each patient. The greater the intraoperative risk degree, the more likely the corresponding patient has a post-anesthesia complication in the corresponding first preset time period. Further, the intraoperative anesthesia complication patient and the intraoperative normal patient are judged in real time based on the intraoperative risk degree in this embodiment.

[0065] Preferably, in an implementable manner of this embodiment, the method for obtaining the intraoperative risk degree is as follows: Figure 2 which shows a method flowchart for obtaining the intraoperative risk degree provided by this embodiment, and the method comprises the following steps:

[0066] Step S201: For any patient and any first preset time period of the patient, according to the size and change of each intraoperative physical index data of the patient in the first preset time period, the abnormal degree of the physical index of the patient in the first preset time period is obtained.

[0067] When the change degree of the intraoperative physical index data of a certain patient in a certain first preset time period is greater, and the more and greater the intraoperative physical index data exceeding the normal range, the greater the risk of anesthesia complications of the patient in the first preset time period. Therefore, according to the size and change of each intraoperative physical index data of the patient in the first preset time period, the embodiment obtains the abnormal degree of the physical index of the patient in the first preset time period. The greater the abnormal degree of the physical index, the greater the risk of anesthesia complications of the patient in the first preset time period.

[0068] In an implementable manner of the embodiment, the method for obtaining the abnormal degree of the physical index is as follows: for any intraoperative physical index data of the patient, the mean value of the absolute value of the difference of the intraoperative physical index data at all adjacent time points in the first preset time period of the patient is obtained as the change degree of the intraoperative physical index data in the first preset time period of the patient. The greater the change degree, the more abnormal the intraoperative physical index data in the first preset time period.

[0069] The normal range of the intraoperative physical index data is taken as the target range, and the intraoperative physical index data of the patient not in the target range in the first preset time period is taken as the first abnormal index data. The minimum value of each first abnormal index data exceeding the target range is taken as the abnormal analysis value. The greater the abnormal analysis value and the more the first abnormal index data, the more abnormal the intraoperative physical index data in the first preset time period.

[0070] In order to accurately represent the abnormality of the intraoperative physical index data in the first preset time period, the embodiment normalizes the product of the number of first abnormal index data, the mean value of abnormal analysis value and the change degree, and takes the result as the local abnormal degree of the intraoperative physical index data of the patient in the first preset time period. The embodiment normalizes the product of the number of first abnormal index data, the mean value of abnormal analysis value and the change degree by the norm normalization function. Thus, the local abnormal degree of each intraoperative physical index data of the patient in the first preset time period is obtained.

[0071] In order to represent the abnormality of the physical index of the patient in the first preset time period as a whole, the embodiment takes the mean value of the local abnormal degree of all intraoperative physical index data of the patient in the first preset time period as the abnormal degree of the physical index of the patient in the first preset time period.

[0072] Step S202: Add the past medical history score and the allergy history score of the patient to obtain a basic score of the patient.

[0073] For any patient, the greater the past medical history score and the allergy history score of the patient, the more likely the patient is to have a risk of anesthesia complications during the operation. Therefore, the embodiment adds the past medical history score and the allergy history score of the patient to obtain a basic score of the patient. The greater the basic score, the more likely the patient is to have a risk of anesthesia complications.

[0074] Step S203: Add the body index abnormality degree and the basic score and normalize the result to obtain an intraoperative risk degree of the patient in the first preset time period.

[0075] It is known that the greater the body index abnormality degree, the more likely the patient is to have a risk of anesthesia complications in the corresponding first preset time period; the greater the basic score, the more likely the patient is to have a risk of anesthesia complications. Therefore, for any patient and any first preset time period of the patient, the embodiment adds the body index abnormality degree of the patient in the first preset time period and the basic score of the patient and normalizes the result to obtain an intraoperative risk degree of the patient in the first preset time period. The embodiment normalizes the addition result of the body index abnormality degree of the patient in the first preset time period and the basic score of the patient by a norm normalization function.

[0076] At this point, the intraoperative risk degree of each patient in each first preset time period is obtained.

[0077] Preferably, in one implementation manner of the embodiment, the method for judging the intraoperative anesthesia complication patient and the intraoperative normal patient in real time based on the intraoperative risk degree is as follows: for any patient, the intraoperative risk degree of the patient in each first preset time period is obtained in real time, and it is known that the greater the intraoperative risk degree, the more likely the patient is to have a risk of anesthesia complications in the corresponding first preset time period. Therefore, the embodiment sets a preset intraoperative risk degree threshold value as 0.6, and the implementer can set the size of the preset intraoperative risk degree threshold value according to the actual situation, which is not limited herein. When the intraoperative risk degree of the patient is greater than the preset intraoperative risk degree threshold value, the patient is judged as an intraoperative anesthesia complication patient, and a warning is issued to remind the medical staff to pay high attention to the anesthesia condition of the patient. When the intraoperative risk degree of the patient is less than or equal to the preset intraoperative risk degree threshold value, the patient is judged as an intraoperative normal patient.

[0078] At this point, the intraoperative anesthesia complication patient and the intraoperative normal patient are accurately judged.

[0079] The postoperative anesthesia complication analysis module 30 is used for dividing a second preset time period in real time after the operation, obtaining the postoperative risk degree of each intraoperative normal patient in each second preset time period according to the size of each postoperative physical index data, the consciousness state score, the nervous system score, the wound recovery score and the wakefulness state of each intraoperative normal patient in each second preset time period, and the size and change of the intraoperative risk degree of each intraoperative normal patient; and judging the postoperative anesthesia complication patient and the postoperative normal patient in real time based on the postoperative risk degree.

[0080] Specifically, it is known that different patients have certain individual differences, and the time of anesthesia complication occurrence will also be different. Some patients may occur during the operation, and some patients may occur after the operation. Therefore, the absence of anesthesia complications during the operation does not mean that anesthesia complications will not occur after the operation. At the same time, the occurrence of some anesthesia complications is a gradual process, and the residual effect of postoperative anesthetic drugs gradually subsides, that is, the body begins to recover from anesthesia and surgical trauma after the operation, and the immune system, endocrine system and the like will undergo a series of adjustments. These changes may lead to the emergence of new anesthesia risk factors, and thus trigger anesthesia complications. Therefore, it is still necessary to monitor the changes of various physical indicators and the overall recovery of intraoperative normal patients after the operation, so as to timely warn anesthesia complications after the operation.

[0081] In order to timely and accurately analyze the anesthesia risk of intraoperative normal patients after the operation, the embodiment first divides a second preset time period in real time after the operation. The embodiment sets the length of the second preset time period as 2 hours, and the implementer can set the length of the second preset time period according to the actual situation, which is not limited here. That is, the second preset time period is divided from the beginning time of the operation. It should be noted that there is no other time between the adjacent two second preset time periods, that is, the adjacent two second preset time periods are continuous in time sequence and do not overlap.

[0082] It is known that the more abnormal the postoperative physical index data of a certain second preset time period of a certain intraoperative normal patient is, the more likely the intraoperative normal patient is to have a anesthesia complication in the second preset time period; at the same time, the greater the consciousness state score, the greater the nervous system score and the greater the wound recovery score of the intraoperative normal patient in the second preset time period, and the intraoperative normal patient is in an unconscious state in the second preset time period, which also indicates that the intraoperative normal patient is more likely to have a anesthesia complication in the second preset time period; in addition, the greater the change of the intraoperative risk degree of the intraoperative normal patient, and the more the change trend of the final intraoperative risk degree presents an upward trend, the more likely the intraoperative normal patient is to have a anesthesia complication risk in the subsequent postoperative stage; then, according to the size of each postoperative physical index data, the consciousness state score, the nervous system score, the wound recovery score and the conscious state of each second preset time period of each intraoperative normal patient, and the size and change of the intraoperative risk degree of each intraoperative normal patient, the embodiment obtains the postoperative risk degree of each second preset time period of each intraoperative normal patient; the greater the postoperative risk degree, the more likely the corresponding intraoperative normal patient is to have a anesthesia complication in the corresponding second preset time period; therefore, the embodiment judges the postoperative anesthesia complication patient and the postoperative normal patient in real time based on the postoperative risk degree.

[0083] Preferably, in one implementation manner of the embodiment, the method for obtaining the postoperative risk degree can refer to Figure 3 which shows a method flowchart for obtaining the postoperative risk degree provided by the embodiment, and the method comprises the following steps:

[0084] Step S301: For any intraoperative normal patient and any second preset time period of the intraoperative normal patient, according to the size of each postoperative physical index data in the second preset time period, the anesthesia complication first risk index of the second preset time period is obtained.

[0085] Wherein, the greater the anesthesia complication first risk index, the more likely the corresponding intraoperative normal patient is to have a anesthesia complication in the corresponding second preset time period.

[0086] In an implementable manner of the embodiment, the method for obtaining the first risk index of anesthesia complications is as follows: for any postoperative physical index data of any intraoperative normal patient and any second preset time period, the postoperative physical index data exceeding the normal range of the postoperative physical index data in the second preset time period are all taken as second abnormal index data; the time corresponding to the second abnormal index data is taken as an abnormal time, and the ratio of the duration corresponding to the abnormal time to the total duration of the second preset time period is taken as an abnormal proportion degree of the postoperative physical index data in the second preset time period; the greater the abnormal proportion degree, the more abnormal the postoperative physical index data in the second preset time period; the greater and more the second abnormal index data, the more abnormal the postoperative physical index data in the second preset time period, and then the embodiment takes the normalized result of the mean of the second abnormal index data, the product of the number of the second abnormal index data and the abnormal proportion degree as the risk analysis value of the postoperative physical index data in the second preset time period; the embodiment normalizes the product of the mean of the second abnormal index data, the number of the second abnormal index data and the abnormal proportion degree by using the norm normalization function; thus, the risk analysis value of each postoperative physical index data in the second preset time period is obtained.

[0087] The greater the risk analysis value, the more abnormal the corresponding postoperative physical index data in the second preset time period, in order to represent the abnormality of the postoperative physical index data in the second preset time period as a whole, and then the embodiment takes the sum of the risk analysis values of all postoperative physical index data in the second preset time period as the first risk index of anesthesia complications in the second preset time period.

[0088] Thus, the first risk index of anesthesia complications in each second preset time period of each intraoperative normal patient is obtained.

[0089] Step S302: According to the consciousness state score, the nervous system score, the wound recovery score and the wakefulness state of the intraoperative normal patient in the second preset time period, the second risk index of anesthesia complications in the second preset time period is obtained.

[0090] The greater the second risk index of anesthesia complications, the more likely the anesthesia complications in the corresponding second preset time period of the corresponding intraoperative normal patient.

[0091] In an implementable manner of the embodiment, the method for obtaining the second risk index of anesthesia complication is as follows: for any second preset time period of any intraoperative normal patient, when the intraoperative normal patient is awake in the second preset time period, the score of the wake state of the second preset time period is set to 0; when the intraoperative normal patient is not awake in the second preset time period, the score of the wake state of the second preset time period is set to 1; the implementer can set the score of the wake state according to the actual situation, which is not limited herein. It is known that the greater the consciousness state score, the greater the nervous system score, the greater the wound recovery score and the greater the wake state score of the intraoperative normal patient in the second preset time period, the greater the risk of anesthesia complication of the intraoperative normal patient in the second preset time period; in order to more accurately analyze the anesthesia risk of the intraoperative normal patient in the second preset time period, the result of adding the mean value of the consciousness state score, the mean value of the nervous system score, the mean value of the wound recovery score and the wake state score of the intraoperative normal patient in the second preset time period and then normalizing is taken as the second risk index of anesthesia complication of the second preset time period. In the embodiment, the norm normalization function is used to normalize the related results of the mean value of the consciousness state score, the mean value of the nervous system score, the mean value of the wound recovery score and the wake state score.

[0092] At this point, the second risk index of anesthesia complication of each second preset time period of each intraoperative normal patient is obtained.

[0093] Step S303: According to the size and change of the intraoperative risk degree of the intraoperative normal patient, the anesthesia reference risk index of the intraoperative normal patient is obtained.

[0094] Wherein, the greater the anesthesia reference risk index, the greater the possibility of the intraoperative normal patient to have anesthesia complication after operation.

[0095] In an implementable manner of the embodiment, the method for obtaining the anesthesia reference risk index is as follows: for any intraoperative normal patient, the intraoperative risk degree of the intraoperative normal patient is arranged according to time sequence of the corresponding first preset time period and fitted as a curve; the method for fitting the curve is a known technology and will not be described in detail. The difference between the maximum intraoperative risk degree and the minimum intraoperative risk degree in the curve is taken as the intraoperative risk fluctuation degree; the greater the intraoperative risk fluctuation degree, the more unstable the anesthesia complication risk of the intraoperative normal patient in the surgical process, which indirectly reflects that the anesthesia complication risk of the intraoperative normal patient after surgery is more likely to be greater; further, the curve is divided by the extreme point (i.e., the curve is divided by taking the extreme point as a division point) to obtain a local curve segment; the last local curve segment of the curve is taken as a target curve segment, the average of the tangent slopes of all intraoperative risk degrees on the target curve segment is taken as the overall slope of the target curve; the greater the overall slope, the more gradually the anesthesia risk of the intraoperative normal patient increases in the later stage of surgery, which indirectly indicates that the anesthesia risk of the intraoperative normal patient after surgery is more likely to be greater. The greater the intraoperative risk degree on the target curve segment, the more likely the anesthesia risk of the intraoperative normal patient after surgery is greater; further, the product of the intraoperative risk fluctuation degree, the overall slope and the average of all intraoperative risk degrees on the target curve segment is normalized, and the result is taken as the anesthesia reference risk index of the intraoperative normal patient. The product of the intraoperative risk fluctuation degree, the overall slope and the average of all intraoperative risk degrees on the target curve segment is normalized by the norm normalization function.

[0096] At this point, the anesthesia reference risk index of each intraoperative normal patient is obtained.

[0097] Step S304: the result of adding and normalizing the anesthesia complication first risk index, the anesthesia complication second risk index and the anesthesia reference risk index is taken as the postoperative risk degree of the second preset time period.

[0098] It is known that the greater the anesthesia complication first risk index and the greater the anesthesia complication second risk index, the more likely the anesthesia complication of the corresponding intraoperative normal patient in the corresponding second preset time period; the greater the anesthesia reference risk index, the more likely the anesthesia complication of the corresponding intraoperative normal patient after surgery. In order to accurately characterize the anesthesia complication risk of each intraoperative normal patient in each second preset time period of the intraoperative normal patient, further, for any intraoperative normal patient and any second preset time period of the intraoperative normal patient, the result of adding and normalizing the anesthesia complication first risk index, the anesthesia complication second risk index and the anesthesia reference risk index of the intraoperative normal patient in the second preset time period of the intraoperative normal patient is taken as the postoperative risk degree of the second preset time period of the intraoperative normal patient.

[0099] At this point, the postoperative risk degree of each second preset time period of each intraoperative normal patient is obtained.

[0100] Preferably, in an implementable manner of the present embodiment, the method for judging the postoperative anesthesia complication patient and the postoperative normal patient in real time based on the postoperative risk degree is that, for any intraoperative normal patient, the postoperative risk degree of each first preset time period of the intraoperative normal patient is obtained in real time, and the greater the postoperative risk degree is, the more likely the corresponding second preset time period of the intraoperative normal patient has anesthesia complications, therefore, the present embodiment sets the preset postoperative risk degree threshold value to 0.6, and the implementer can set the size of the preset postoperative risk degree threshold value according to the actual situation, which is not limited herein. When the postoperative risk degree of the intraoperative normal patient is greater than the preset postoperative risk degree threshold value, the intraoperative normal patient is judged to be a postoperative anesthesia complication patient at this time, and an emergency warning is issued to the medical staff at the same time, so that timely emergency intervention treatment is provided to reduce the risk of anesthesia complications; when the postoperative risk degree of the intraoperative normal patient is less than or equal to the preset postoperative risk degree threshold value, the intraoperative normal patient is judged to be a postoperative normal patient.

[0101] In summary, the present embodiment obtains the intraoperative risk degree according to the size and change of the intraoperative physical index data of the patient in each first preset time period, the past medical history score and the allergy history score of the patient, and then judges the intraoperative anesthesia complication patient and the intraoperative normal patient in real time; the postoperative risk degree is obtained according to the size of the postoperative physical index data of the intraoperative normal patient in each second preset time period, the consciousness state score, the nervous system score, the wound recovery score and the wakefulness state, and the intraoperative risk degree of the intraoperative normal patient, and then the postoperative anesthesia complication patient and the postoperative normal patient are judged in real time. The present application can accurately obtain the intraoperative risk degree and the postoperative risk degree in real time, so that the anesthesia complications of the patient can be accurately detected in time, and measures can be taken in time, thereby effectively reducing the harm of anesthesia complications.

[0102] Embodiment 2:

[0103] The present application also provides an intelligent warning device for anesthesia complications of a surgical patient. The device comprises a memory and a processor, wherein the memory stores executable program code, and the processor is configured to call and execute the executable program code to execute the intelligent warning system for anesthesia complications of a surgical patient provided by the present application. The device can be a chip, a component or a module. The chip can comprise a processor and a memory connected thereto. When the processor calls and executes the instructions, the chip can execute the intelligent warning system for anesthesia complications of a surgical patient provided by the above-mentioned embodiments.

[0104] In addition, the present application also protects a computer device, please refer toFigure 4 The computer device comprises a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402, wherein the processor 402 executes the computer program 403, so that the computer device can execute any one of the aforementioned intelligent warning systems for postoperative complications of surgical patients.

[0105] Embodiment 3

[0106] The application further provides a computer readable storage medium, which stores computer program codes, and when the computer program codes are run on a computer, the computer executes the above-mentioned related method steps to realize the intelligent warning system for postoperative complications of surgical patients provided in the above-mentioned embodiments.

[0107] Embodiment 4

[0108] The application further provides a computer program product, and when the computer program product is run on a computer, the computer executes the above-mentioned related steps to realize the intelligent warning system for postoperative complications of surgical patients provided in the above-mentioned embodiments.

[0109] The device, the computer readable storage medium, the computer program product or the chip provided in the embodiment are used to execute the corresponding method provided above, so the beneficial effects achieved by the device, the computer readable storage medium, the computer program product or the chip can refer to the beneficial effects in the corresponding method provided above, and will not be described here.

[0110] It should be noted that the above-mentioned sequence of the embodiments of the application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0111] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment mainly describes the difference from other embodiments.

Claims

1. An intelligent warning system for post-anesthesia complications in a surgical patient, characterized in that, The system comprises: a data acquisition module, configured to acquire a past medical history score and an allergy history score of a patient, acquire various intraoperative physical index data of the patient in real time during an operation, and acquire various postoperative physical index data, a consciousness state score, a nervous system score, and a wound recovery score of the patient after the operation; an intraoperative anesthesia complication analysis module, configured to divide a first preset time period in real time during the operation, acquire an intraoperative risk degree of each patient for each first preset time period according to the size and change of each kind of intraoperative physical index data of each patient in each first preset time period, and the past medical history score and the allergy history score of each patient, and judge an intraoperative anesthesia complication patient and an intraoperative normal patient in real time based on the intraoperative risk degree; a postoperative anesthesia complication analysis module, configured to divide a second preset time period in real time after the operation, acquire a postoperative risk degree of each intraoperative normal patient for each second preset time period according to the size of each kind of postoperative physical index data, the consciousness state score, the nervous system score, the wound recovery score, and the wakeful state of each intraoperative normal patient in each second preset time period, and the size and change of the intraoperative risk degree of each intraoperative normal patient, and judge a postoperative anesthesia complication patient and a postoperative normal patient in real time based on the postoperative risk degree.

2. An intelligent pre-warning system for post-operative complications in a surgical patient as claimed in claim 1, wherein, The method for acquiring the intraoperative risk degree comprises: for any patient and any first preset time period of the patient, acquiring a physical index abnormality degree of the patient for the first preset time period according to the size and change of each kind of intraoperative physical index data of the patient in the first preset time period; adding the past medical history score and the allergy history score of the patient to obtain a basic score of the patient; adding the physical index abnormality degree and the basic score and performing normalization to obtain the intraoperative risk degree of the patient for the first preset time period.

3. An intelligent pre-warning system for post-operative complications in a surgical patient as claimed in claim 2 wherein, The method for acquiring the physical index abnormality degree comprises: for any kind of intraoperative physical index data of the patient, acquiring a mean value of differences of the kind of intraoperative physical index data at all adjacent time points in the first preset time period of the patient as a change degree of the kind of intraoperative physical index data in the first preset time period of the patient; regarding a normal range of the kind of intraoperative physical index data as a target range, and regarding the kind of intraoperative physical index data not in the target range in the first preset time period of the patient as first abnormal index data; regarding a minimum value of each first abnormal index data exceeding the target range as an abnormal analysis value; multiplying a number of first abnormal index data, a mean value of abnormal analysis values, and the change degree to perform normalization to obtain a local abnormality degree of the kind of intraoperative physical index data in the first preset time period of the patient; regarding a mean value of local abnormality degrees of all kinds of intraoperative physical index data in the first preset time period of the patient as the physical index abnormality degree of the patient for the first preset time period.

4. The intelligent pre-warning system for post-operative complications of a surgical patient as claimed in claim 1 wherein, The method for judging the intraoperative anesthesia complication patient and the intraoperative normal patient in real time based on the intraoperative risk degree comprises: For any patient, the intraoperative risk degree of the patient is acquired in real time for each first preset time period, and when the intraoperative risk degree of the patient is greater than a preset intraoperative risk degree threshold, the patient is determined as an intraoperative anesthesia complication patient; When the intraoperative risk degree of the patient is less than or equal to the preset intraoperative risk degree threshold, the patient is determined as an intraoperative normal patient.

5. The intelligent pre-warning system for post-operative complications of a surgical patient as claimed in claim 1 wherein, The postoperative risk degree is acquired by the following method: For any intraoperative normal patient and any second preset time period of the intraoperative normal patient, a first anesthesia complication risk index of the second preset time period is acquired according to the size of each postoperative physical index data in the second preset time period; A second anesthesia complication risk index of the second preset time period is acquired according to the consciousness state score, the nervous system score, the wound recovery score and the wakefulness state of the intraoperative normal patient in the second preset time period; A anesthesia reference risk index of the intraoperative normal patient is acquired according to the size and change of the intraoperative risk degree of the intraoperative normal patient; The sum of the first anesthesia complication risk index, the second anesthesia complication risk index and the anesthesia reference risk index after normalization is taken as the postoperative risk degree of the second preset time period.

6. An intelligent pre-warning system for post-operative complications in a surgical patient as claimed in claim 5 wherein, The first anesthesia complication risk index is acquired by the following method: For any postoperative physical index data and any second preset time period of any intraoperative normal patient, all the postoperative physical index data exceeding the normal range of the postoperative physical index data in the second preset time period are taken as second abnormal index data; The time corresponding to the second abnormal index data is taken as an abnormal time, and the ratio of the duration corresponding to the abnormal time to the total duration of the second preset time period is taken as the abnormal proportion degree of the postoperative physical index data in the second preset time period; The product of the mean value of the second abnormal index data, the number of the second abnormal index data and the abnormal proportion degree is normalized to obtain a risk analysis value of the postoperative physical index data in the second preset time period; The sum of the risk analysis values of all the postoperative physical index data in the second preset time period is taken as the first anesthesia complication risk index of the second preset time period.

7. An intelligent pre-warning system for post-operative complications in a surgical patient as claimed in claim 5 wherein, The second anesthesia complication risk index is acquired by the following method: For any second preset time period of any intraoperative normal patient, when the intraoperative normal patient is in a wakeful state in the second preset time period, the wakefulness state score of the second preset time period is set to 0; When the intraoperative normal patient is in an un-wakeful state in the second preset time period, the wakefulness state score of the second preset time period is set to 1; The sum of the mean value of the consciousness state score, the mean value of the nervous system score, the mean value of the wound recovery score and the wakefulness state score of the intraoperative normal patient in the second preset time period is normalized to obtain the second anesthesia complication risk index of the second preset time period.

8. An intelligent pre-warning system for post-operative complications in a surgical patient as claimed in claim 5 wherein, The anesthesia reference risk index is acquired by the following method: For any intraoperative normal patient, the intraoperative risk degree of the intraoperative normal patient is arranged according to the time sequence of the corresponding first preset time period and fitted into a curve; Obtaining a difference between the maximum intraoperative risk degree and the minimum intraoperative risk degree in the curve as an intraoperative risk fluctuation degree; Dividing the curve by the extreme point to obtain a local curve segment; Taking the last local curve segment of the curve as a target curve segment, obtaining a mean value of the tangent slope of all intraoperative risk degrees on the target curve segment as an overall slope of the target curve; Taking a result of normalizing a product of the intraoperative risk fluctuation degree, the overall slope and the mean value of all intraoperative risk degrees on the target curve segment as an anesthesia reference risk index of the intraoperative normal patient.

9. The intelligent pre-warning system for post-operative complications of anesthetized patients as claimed in claim 1 wherein, The method for judging the postoperative anesthesia complication patient and the postoperative normal patient in real time based on the postoperative risk degree is: For any intraoperative normal patient, the postoperative risk degree of each first preset time period of the intraoperative normal patient is obtained in real time, when the postoperative risk degree of the intraoperative normal patient is greater than a preset postoperative risk degree threshold value, the intraoperative normal patient is judged as a postoperative anesthesia complication patient; When the postoperative risk degree of the intraoperative normal patient is less than or equal to the preset postoperative risk degree threshold value, the intraoperative normal patient is judged as a postoperative normal patient.

10. The intelligent pre-warning system for post-operative complications of a surgical patient as claimed in claim 1 wherein, The initial time of the postoperation is an end time of the operation.

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