CN117854731A - Prediction method and system for delayed wound healing influence factors after bromhidrosis operation - Google Patents
Prediction method and system for delayed wound healing influence factors after bromhidrosis operation Download PDFInfo
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Abstract
The invention belongs to the technical field of smart medical treatment, and relates to a prediction method and a prediction system for a delayed wound healing influence factor after an bromhidrosis operation, wherein parameters of the bromhidrosis operation patient related to id of the bromhidrosis operation patient are obtained; loading a patient bromhidrosis reaction level matched with patient exercise behaviors and bromhidrosis reaction level id matched with patient exercise behaviors id; transmitting the intensity of bromhidrosis in the bromhidrosis reaction level of the patient to a first wound healing influencing factor set which sets the healing progress of the wound in the nursing end based on the exercise behavior of the patient; and obtaining a prediction result obtained by predicting the wound healing progress in the first wound healing influence factor set by adopting the bromhidrosis intensity and performing patient prediction matched with a patient after the bromhidrosis operation to be predicted. The invention has the beneficial effects that a prediction model of scientific aging is established, an early warning effect is realized on the bromhidrosis patient, the risk of the patient is realized, the prognosis is improved, the delayed healing risk of the incision after the bromhidrosis operation is better quantitatively evaluated, and the individual nursing scheme is timely given.
Description
Technical Field
The invention belongs to the technical field of smart medical treatment, and particularly relates to a prediction method and a prediction system for delayed wound healing influence factors after bromhidrosis operation.
Background
Axillary malodour (Axillaryosmidosis), commonly known as bromhidrosis, also known as underarm hyperhidrosis, is a common surgical disorder, manifests as an increase in underarm perspiration and is controlled by the surrounding environment and its own body temperature regulation. The disease is usually found in puberty, female patients are more than male patients, and symptoms are aggravated in summer or after activity, and the symptoms are gradually relieved or healed after the elderly. However, the armpit odor gives off a bad smell throughout the whole body, so that the armpit odor is separated from the public and is suspected, and great psychological trouble is often caused to the patient.
Therefore, for patients suffering from bromhidrosis, risk factors of non-healing or delayed healing of the postoperative incision are found early, and early intervention or prevention of the occurrence of non-healing or delayed healing of the incision is of positive significance in improving the postoperative prognosis of the bromhidrosis patients.
Disclosure of Invention
According to a first aspect of the present invention, the present invention claims a method for predicting factors affecting delayed healing of a wound after an bromhidrosis operation, comprising:
acquiring a patient after the bromhidrosis operation to be predicted, and acquiring parameters of the patient after the bromhidrosis operation, which are related to the patient id after the bromhidrosis operation, based on the patient id after the bromhidrosis operation of the patient after the bromhidrosis operation to be predicted, wherein the parameters of the patient after the bromhidrosis operation comprise a patient movement behavior id and an bromhidrosis reaction grade id;
Based on the patient exercise activity id, loading patient exercise activity matched with the patient exercise activity id;
loading a patient's underarm odor reactive level matching the underarm odor reactive level id based on the underarm odor reactive level id;
transmitting the bromhidrosis intensity in the bromhidrosis reaction level of the patient to a first wound healing influencing factor set setting the wound healing progress in a nursing end based on the patient exercise behavior;
and obtaining a prediction result obtained by predicting the wound healing progress in the first wound healing influence factor set by adopting the bromhidrosis intensity and performing patient prediction matched with the patient after the bromhidrosis operation to be predicted.
Further, the transmitting the underarm odor intensity in the underarm odor reaction level of the patient to a first wound healing influence factor set setting a wound healing progress in a care end based on the patient exercise behavior includes:
based on personal attribute factors of the patient after the bromhidrosis operation to be predicted, deciding patient prediction influence factors matched with the personal attribute factors of the patient after the bromhidrosis operation;
deciding a second set of wound healing influencing factors from each of the set of reference wound healing influencing factors based on patient sample conditions of the set of reference wound healing influencing factors for which the patient prediction influencing factors are set;
Selecting patient prediction influence factors in the second wound healing influence factor set, and constructing a prediction simulation model matched with the prediction factors of the patient after the bromhidrosis operation to be predicted based on the patient movement behaviors;
and selecting patient prediction influence factors in the second wound healing influence factor set to send the bromhidrosis intensity in the bromhidrosis reaction level of the patient to the first wound healing influence factor set under the prediction simulation model.
Further, the selecting the patient prediction influence factor in the second wound healing influence factor set is based on the patient movement behavior, and the constructing a prediction simulation model matched with the prediction factor of the patient after the bromhidrosis operation to be predicted includes:
acquiring configuration parameters from the patient exercise behavior;
selecting patient prediction influence factors in the second wound healing influence factor set, and constructing a prediction simulation model matched with the prediction factors of the patient after the bromhidrosis operation to be predicted by combining the configuration parameters;
the determining a second set of wound healing influencing factors from each of the set of reference wound healing influencing factors based on patient sample conditions of the set of reference wound healing influencing factors for which the patient prediction influencing factors are set, comprising:
Deciding a third set of wound healing influencing factors from each of the set of reference wound healing influencing factors based on patient sample conditions of a set of reference wound healing influencing factors for which the patient prediction influencing factors are set, wherein the patient sample conditions of the third set of wound healing influencing factors are balanced by a genus;
the second set of wound healing influencing factors is determined from each of the third set of wound healing influencing factors based on the predictive factors.
Further, the determining a second set of wound healing influencing factors from each of the reference set of wound healing influencing factors based on patient sample conditions of the reference set of wound healing influencing factors for which the patient prediction influencing factors are set, comprises:
deciding a third set of wound healing influencing factors from each of the set of reference wound healing influencing factors based on patient sample conditions of a set of reference wound healing influencing factors for which the patient prediction influencing factors are set, wherein the patient sample conditions of the third set of wound healing influencing factors are balanced by a genus;
from each of the third set of wound healing influencing factors, a second set of wound healing influencing factors selected from the patient after the axillary malodour operation has been predicted is determined.
Further, the obtaining a prediction result obtained by predicting the wound healing progress in the first wound healing influencing factor set by using the bromhidrosis intensity and the patient matched with the patient after the bromhidrosis operation to be predicted includes:
inputting a predicted factor weight distribution sent by the first wound healing influence factor set through the second wound healing influence factor set, wherein the predicted factor weight distribution is generated by the fact that the wound healing progress in the first wound healing influence factor set is matched with the bromhidrosis intensity;
in the process that the wound healing progress in the first wound healing influence factor set is matched with the bromhidrosis intensity, factor weight distribution correction is carried out on the first wound healing influence factor set so as to obtain a correction result;
and taking the prediction factor weight distribution and the correction result as the prediction result.
Further, the wound healing progress in the first wound healing influencing factor set is used for performing fuzzy matching treatment on the bromhidrosis intensity, the prediction factor is the prediction behavior of fuzzy matching, and the method further comprises:
selecting patient predictive influencing factors in the second wound healing influencing factor set to execute the patient movement behavior so as to decide a first difference between the fuzzy matching predictive factor weight distribution and the fuzzy matching expected result marked on the bromhidrosis intensity;
Based on the first distinction, deciding a first prediction index corresponding to the fuzzy matched prediction behavior;
analyzing the fuzzy matching correction result to decide that the wound healing progress in the first wound healing influence factor set matches the factor set to which the bromhidrosis intensity belongs;
based on a factor set of the wound healing progress in the first wound healing influence factor set, deciding a second prediction index corresponding to the fuzzy matching prediction behavior;
and based on the first prediction index and the second prediction index, deciding the behavior of the nursing end in fuzzy matching.
Further, the determining, based on the first prediction index and the second prediction index, the behavior of the care terminal in the fuzzy matching includes:
deciding the first occurrence time stamp, duration and intensity change of the bromhidrosis intensity; wherein the first occurrence time stamp is a time period between a first time stamp and a second time stamp, the time period is a time period between the first time stamp and a third time stamp, and the intensity change is a time period between a fourth time stamp and the third time stamp; the first timestamp is a timestamp of a first bromhidrosis reaction in the bromhidrosis intensity sent by a patient prediction influence factor in the second wound healing influence factor set, the second timestamp is a timestamp of a first bromhidrosis reaction in the fuzzy matching prediction factor weight distribution input by the second wound healing influence factor set, the third timestamp is a timestamp of a last bromhidrosis reaction in the fuzzy matching prediction factor weight distribution input by the second wound healing influence factor set, and the fourth timestamp is a timestamp of a last bromhidrosis reaction in the bromhidrosis intensity sent by a patient prediction influence factor in the second wound healing influence factor set;
Deciding a third prediction index corresponding to the fuzzy matching prediction behavior based on at least one of the first occurrence time stamp, the duration and the intensity change of the bromhidrosis intensity;
and deciding the behavior of the nursing end in fuzzy matching based on the first prediction index, the second prediction index and the third prediction index.
Further, the wound healing progress in the first wound healing influencing factor set is used for performing accurate matching treatment on the bromhidrosis intensity, the prediction factor is the accurately matched prediction behavior, and the method further comprises:
selecting patient predicted influencing factors in the second set of wound healing influencing factors to perform the patient locomotor activity to determine a second difference between the precisely matched predicted factor weight assignment and the precisely matched expected outcome noted on the bromhidrosis intensity;
analyzing the correction result of the accurate matching to determine that the wound healing progress in the first wound healing influence factor set matches the first function selected by the bromhidrosis intensity;
deciding a third distinction between said first function and said precisely matched second function noted in said bromhidrosis intensity;
Based on the second distinction and/or the third distinction, deciding on the behavior of the care end at the exact match.
Further, the predictor is a drug allergy predictor, the method further comprising:
analyzing the correction result to determine whether factor weight distribution abnormality occurs in the process of matching the bromhidrosis intensity on the wound healing progress in the first wound healing influence factor set;
abnormal weight distribution of factors occurs in the wound healing progress matched with the first wound healing influence factor set, and the nursing end is decided not to pass through medicine allergy prediction;
the wound healing progress matched with the first wound healing influence factor set is free from factor weight distribution abnormality, and the nursing end is decided to predict through drug allergy;
and correspondingly storing the prediction result and the patient after the bromhidrosis operation to be predicted.
According to a second aspect of the present invention, the present invention claims a prediction system for factors affecting delayed healing of a wound after an bromhidrosis operation, comprising:
one or more processors;
and the memory is stored with one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors realize the prediction method for the delayed wound healing influence factors after the bromhidrosis operation.
The invention belongs to the technical field of smart medical treatment, and relates to a prediction method and a prediction system for a delayed wound healing influence factor after an bromhidrosis operation, wherein parameters of the bromhidrosis operation patient related to id of the bromhidrosis operation patient are obtained; loading a patient bromhidrosis reaction level matched with patient exercise behaviors and bromhidrosis reaction level id matched with patient exercise behaviors id; transmitting the intensity of bromhidrosis in the bromhidrosis reaction level of the patient to a first wound healing influencing factor set which sets the healing progress of the wound in the nursing end based on the exercise behavior of the patient; and obtaining a prediction result obtained by predicting the wound healing progress in the first wound healing influence factor set by adopting the bromhidrosis intensity and performing patient prediction matched with a patient after the bromhidrosis operation to be predicted. The invention has the beneficial effects that a prediction model of scientific aging is established, an early warning effect is realized on the bromhidrosis patient, the risk of the patient is realized, the prognosis is improved, the delayed healing risk of the incision after the bromhidrosis operation is better quantitatively evaluated, and the individual nursing scheme is timely given.
Drawings
FIG. 1 is a workflow diagram of a method of predicting factors affecting delayed healing of a wound after an bromhidrosis surgery as claimed in an embodiment of the present invention;
FIG. 2 is a second workflow diagram of a method of predicting factors affecting delayed healing of a wound after an bromhidrosis surgery as claimed in an embodiment of the invention;
FIG. 3 is a third workflow diagram of a method of predicting factors affecting delayed healing of a wound after an bromhidrosis surgery as claimed in an embodiment of the invention;
FIG. 4 is a fourth working flow chart of a method for predicting factors affecting delayed healing of a wound after an bromhidrosis operation according to an embodiment of the invention;
FIG. 5 is a system architecture diagram of a predictive system for delayed wound healing after bromhidrosis as claimed in an embodiment of the invention;
fig. 6 is a block diagram of a prediction system for delayed wound healing influencing factors after bromhidrosis surgery according to an embodiment of the present invention.
Detailed Description
According to a first embodiment of the present invention, referring to fig. 1, the present invention claims a prediction method for a delayed wound healing influence factor after bromhidrosis operation, comprising:
acquiring a patient after the bromhidrosis operation to be predicted, and acquiring parameters of the bromhidrosis operation, which are related to the patient id after the bromhidrosis operation, based on the patient id after the bromhidrosis operation of the patient after the bromhidrosis operation to be predicted, wherein the parameters of the patient after the bromhidrosis operation comprise a patient movement behavior id and an bromhidrosis reaction grade id;
based on the patient exercise activity id, loading patient exercise activity matched with the patient exercise activity id;
Loading the patient bromhidrosis reaction grade matched with the bromhidrosis reaction grade id based on the bromhidrosis reaction grade id;
transmitting the intensity of bromhidrosis in the bromhidrosis reaction level of the patient to a first wound healing influencing factor set which sets the healing progress of the wound in the nursing end based on the exercise behavior of the patient;
and obtaining a prediction result obtained by predicting the wound healing progress in the first wound healing influence factor set by adopting the bromhidrosis intensity and performing patient prediction matched with a patient after the bromhidrosis operation to be predicted.
In this example, the present example was intended to be incorporated in patients treated with bromhidrosis surgery as a subject, and the incorporated cases were divided into a set-up group and a validation group according to a certain ratio (7:3). The method comprises the steps of screening out high-risk factors of delayed healing of the incision after the bromhidrosis operation through single-factor and multi-factor regression analysis in a modeling queue, establishing an alignment prediction model through the screened risk factors, and realizing individual prediction of the delayed healing of the incision after the bromhidrosis operation by using the alignment model.
Further, referring to fig. 2, based on the patient's motor activities, transmitting the intensity of bromhidrosis in the bromhidrosis reaction level of the patient to a first wound healing influencing factor set that sets the progress of wound healing in the nursing terminal, comprising:
based on personal attribute factors of the patient after the bromhidrosis operation to be predicted, deciding patient prediction influence factors matched with the personal attribute factors of the patient after the bromhidrosis operation;
deciding a second set of wound healing influencing factors from each set of reference wound healing influencing factors based on patient sample conditions of the set of reference wound healing influencing factors for which patient prediction influencing factors are set;
selecting patient prediction influence factors in the second wound healing influence factor set, and constructing a prediction simulation model matched with the prediction factors of the patient after the bromhidrosis operation to be predicted based on the patient movement behaviors;
and under the predictive simulation model, selecting patient predictive influencing factors in the second wound healing influencing factor set, and sending the bromhidrosis intensity in the bromhidrosis reaction level of the patient to the first wound healing influencing factor set.
In the embodiment, the prediction simulation model is constructed by constructing a nomogram prediction model, so that theoretical basis is provided for making simple, economical and effective nursing and prevention strategies and measures, the delayed healing risk of the incision after the bromhidrosis operation can be predicted and prevented, the medical burden can be reduced, and the prognosis of the bromhidrosis patient after the operation can be improved.
A Nomogram (Nomogram) is also called as a Nomogram, which utilizes multi-factor regression analysis in statistics to calculate a plurality of observation indexes, then defines corresponding scores for each value level of a variable according to the contribution degree of each observation index expressed in calculation results to a prediction result, then draws the corresponding scores by using line segments with scales, scores the corresponding positions of the line segments according to the variable values, and adds the scores to obtain a total score, thereby achieving the visualization of a prediction model. The nomogram model converts a complex regression equation into a popular and easily understood graph, so that the clinical application of the prediction model is more direct and objective, and a clinician can conveniently evaluate the patient before operation. In recent years, cox proportional hazards models and Logistic regression models have been applied to pathological stage and disease progression prediction of malignant tumors. Therefore, the nomogram can help doctors judge the risk of delayed healing of the incision after the bromhidrosis operation, and help the doctors to make individual clinical decisions on patients, so that the treatment and prevention can be better carried out in an early stage.
Personal attribute factors of patients after bromhidrosis surgery at least include: sex, age, body Mass Index (BMI), smoking history, drinking history, disease history (such as hypertension, diabetes, coronary heart disease, etc.), and bromhidrosis grading
The first set of wound healing influencing factors includes at least a surgical condition: surgical mode, surgical month (11-4 months and 5-10 months), surgical time (h), blood loss during surgery and use condition of antibacterial drugs;
the second set of wound healing influencing factors includes at least post-operative observations of patient samples from the same region: the outpatient service treatment is carried out on the 2 nd, 4 th, 6 th, 8 th, 10 th, 12 th and 14 th days after operation, and the healing condition is observed, including the blood circulation of the skin flaps at the two sides, the healing time, the presence or absence of subcutaneous ecchymosis, hematoma, incision cleavage, infection, epidermis shedding and necrosis;
the third set of wound healing influencing factors includes at least post-operative observations of patient samples from different generants: the outpatient service treatment is carried out on the 2 nd, 4 th, 6 th, 8 th, 10 th, 12 th and 14 th days after operation, and the healing condition is observed, including the blood circulation of the skin flaps at the two sides, the healing time, and the presence or absence of subcutaneous ecchymosis, hematoma, incision cleavage, infection, epidermis shedding and necrosis.
Further, selecting the patient prediction influence factors in the second wound healing influence factor set, based on the patient movement behaviors, and constructing a prediction simulation model matched with the prediction factors of the patient after the underarm odor operation to be predicted, referring to fig. 3, including:
acquiring configuration parameters from the exercise behavior of the patient;
And selecting patient prediction influence factors in the second wound healing influence factor set, and constructing a prediction simulation model matched with the prediction factors of the patient after the bromhidrosis operation to be predicted by combining configuration parameters.
Further, based on patient sample conditions of a reference set of wound healing influencing factors for which patient prediction influencing factors are set, deciding a second set of wound healing influencing factors from each of the reference set of wound healing influencing factors, comprising:
deciding a third set of wound healing influencing factors from each set of reference wound healing influencing factors based on patient sample conditions of the set of reference wound healing influencing factors for which patient prediction influencing factors are set, wherein the patient sample conditions of the third set of wound healing influencing factors are balanced by the genus;
based on the predicted factors, a second set of wound healing influencing factors is determined from each third set of wound healing influencing factors.
Wherein in this embodiment the high risk group is evaluated early on based on the nomographic model, and targeted care measures are further implemented. The specific nursing key points are as follows:
the detailed health education aims at the propaganda and education work of patients on the corresponding diseases before and after the operation by a nurse, and mainly seriously explains the notes after the operation and the action principle of the operation to the patients. The method emphasizes that the local braking measure of the upper arm is carried out within 2 weeks after operation, the abduction, the lifting and the back-and-forth swing of the upper arm are avoided, the operation is carried out after the operation, the rest is needed to reduce the sweating of the armpits, the nurse of the patient carefully carries out the ward inspection system for 1 time every 1 hour, closely observes the general condition of the patient, closely observes the appearance, the local braking, the sweating and other conditions of the local dressing of the upper arm of the patient, and informs the patient of strictly following the related notes after the operation.
And (3) observing and nursing postoperative conditions, namely covering the incision with sterile gauze, pressurizing and binding with cotton pads, fixing and binding with an external bandage in an 8 shape, and carrying out ice compress with a self-made wide cloth fixed ice bag within 48 hours, wherein the ice compress is carried out every 4 hours for 15-20 minutes each time. The postoperative orders the patient to carry out upper arm adduction, then stretch backward, and the nurse helps it to put on loose and hypertrophic sweater to order the patient to put on and take off the clothes all need avoid the abduction and the rising of upper arm. The tightness in the compression and bandaging process is proper, the underarm blood circulation is blocked due to the over-tightness, subcutaneous hematoma can be caused by the over-tightness, and the skin condition, the color and the blood circulation state of the incision part are closely observed within 48 hours after the operation.
Further, the patient after the axillary odor operation to be predicted is associated with the patient after the axillary odor operation to be predicted, and the first wound healing influence factor set is decided from each reference wound healing influence factor set based on the patient sample condition of the reference wound healing influence factor set for setting the patient prediction influence factor, comprising:
deciding a third set of wound healing influencing factors from each set of reference wound healing influencing factors based on patient sample conditions of the set of reference wound healing influencing factors for which patient prediction influencing factors are set, wherein the patient sample conditions of the third set of wound healing influencing factors are balanced by the genus;
From each third set of wound healing influencing factors, a decision has been made to select a second set of wound healing influencing factors for the patient after the axillary malodour operation.
Therein, in this example, data were entered using epidata3.1 two persons and SPSS21.0 statistical analysis was used. The bilateral test level was α=0.05, and p < 0.05 was statistically significant as a difference.
(1) Statistical description: counting data and the like are described by adopting frequency numbers and percentages; the measurement data and the like are described in terms of (mean ± standard deviation).
(2) Single factorStatistical methods include χ2 test, fisher exact probability method, t test and rank sum test according toP< 0.05 criteria, screening for potential predictive variables.
(3) Statistically significant prediction variables are included in a multi-factor Logistic binary regression analysis (forward method) to determine final prediction variables. The determined independent influencing factors are used for constructing an alignment chart model by adopting an R (R3.6.3) software package and an RMS package of an RMS package, so that the influencing factors are visualized. And drawing an ROC curve, calculating an AUC value, and verifying the distinguishing degree of the nomogram prediction model established in the study. And (3) judging the consistency of the model by using a Hosmer-Lemeshow test, drawing a scatter diagram according to an actual observed value and a model predicted value, and fitting a linear trend line to obtain a calibration curve.
Further, obtaining a prediction result obtained by predicting a patient matching a patient to be predicted after an bromhidrosis operation with a wound healing progress in which bromhidrosis intensity is concentrated on a first wound healing influencing factor, includes:
inputting predicted factor weight distribution sent by the first wound healing influence factor set through the second wound healing influence factor set, wherein the predicted factor weight distribution is generated by the fact that the wound healing progress in the first wound healing influence factor set is matched with the bromhidrosis intensity;
in the process that the wound healing progress in the first wound healing influence factor set is matched with the bromhidrosis intensity, factor weight distribution correction is carried out on the first wound healing influence factor set so as to obtain a correction result;
and (5) distributing the weight of the prediction factors and correcting the result to be used as a prediction result.
Wherein, in this example, the risk of delayed healing of the incision after the bromhidrosis operation is identified and predicted as early as possible, which is helpful for adjusting the early implementation treatment mode and controlling relevant dangerous factors, thereby reducing the incidence rate of delayed healing of the incision after the bromhidrosis operation. At present, the internationally mature disease prediction model is a cardiovascular disease onset risk prediction model and a T2DM risk prediction model, and the research of the disease onset risk prediction model of diseases such as childhood asthma, chronic kidney disease, preeclampsia and the like is gradually standardized and internationalized. While Nomogram (Nomogram) predictive models should be the most widespread among many predictive models and present a good advantage.
Further, referring to fig. 4, the wound healing progress in the first set of wound healing influencing factors is used for performing fuzzy matching treatment on the bromhidrosis intensity, and the prediction factors are prediction behaviors of fuzzy matching, and the method further includes:
selecting patient prediction influence factors in the second wound healing influence factor set to execute patient movement behaviors so as to determine a first difference between the fuzzy matching prediction factor weight distribution and the fuzzy matching expected result marked on the bromhidrosis intensity;
based on the first distinction, deciding a first prediction index corresponding to the fuzzy matched prediction behavior;
analyzing the fuzzy matching correction result to decide a factor set to which the bromhidrosis intensity is attributed when the wound healing progress in the first wound healing influence factor set is matched;
based on a factor set of the wound healing progress in the first wound healing influence factor set, deciding a second prediction index corresponding to the fuzzy matching prediction behavior;
based on the first prediction index and the second prediction index, the behavior of the decision care terminal in fuzzy matching is decided.
Further, based on the first prediction index and the second prediction index, the action of the decision care terminal in fuzzy matching comprises the following steps:
deciding the first occurrence time stamp, duration and intensity change of the bromhidrosis intensity; wherein the first occurrence time stamp is the duration between the first time stamp and the second time stamp, the duration is the duration between the first time stamp and the third time stamp, and the intensity change is the duration between the fourth time stamp and the third time stamp; the first time stamp is the time stamp of the first bromhidrosis reaction in the bromhidrosis intensity sent by the patient prediction influence factors in the second wound healing influence factor set, the second time stamp is the time stamp of the first bromhidrosis reaction in the fuzzy matching prediction factor weight distribution input by the second wound healing influence factor set, the third time stamp is the time stamp of the last bromhidrosis reaction in the fuzzy matching prediction factor weight distribution input by the second wound healing influence factor set, and the fourth time stamp is the time stamp of the last bromhidrosis reaction in the bromhidrosis intensity sent by the patient prediction influence factors in the second wound healing influence factor set;
Based on at least one of the first occurrence time stamp, duration and intensity change of the bromhidrosis intensity, deciding a third prediction index corresponding to the fuzzy matching prediction behavior;
and based on the first prediction index, the second prediction index and the third prediction index, deciding the behavior of the nursing terminal in fuzzy matching.
The first prediction index at least comprises the change condition of the wound of the patient after the bromhidrosis operation;
the second prediction index at least comprises the environmental change condition of the patient after the bromhidrosis operation;
the third prediction index at least comprises the peripheral reaction condition of the patient after the bromhidrosis operation;
in this embodiment, the underarm odor reaction stage includes at least:
level 0: the armpit is rubbed with gauze, and no malodor is emitted under any condition;
stage 1: during strenuous exercise (such as exercise, quick walking, etc.) or heavy physical activity, the gauze rubbed under the armpit gives out slight malodor;
2 stages: the gauze rubbed out from the armpit immediately after daily activities gives out strong malodor, but the malodor cannot be smelled at a distance of 1.5 m;
3 stages: the gauze rubbed out from the armpit still gives out strong malodor when no activity is performed, and the malodor can still be smelled outside 1.5 m.
Further, the wound healing progress in the first wound healing influencing factor set is used for performing accurate matching treatment on the bromhidrosis intensity, and the prediction factor is an accurately matched prediction behavior, and the method further comprises:
Selecting patient prediction influencing factors in a second wound healing influencing factor set to execute patient exercise behaviors so as to decide a second difference between the precisely matched prediction factor weight distribution and the precisely matched expected result marked on the bromhidrosis intensity;
analyzing the accurately matched correction result to determine a first function selected by the bromhidrosis intensity of the wound healing progress matching armpit odor in the first wound healing influence factor set;
deciding a third distinction between the first function and a second function that is precisely matched with the marking on the bromhidrosis intensity;
based on the second distinction and/or the third distinction, the decision care end acts on exact matches.
Further, the predictor is a drug allergy predictor, the method further comprising:
analyzing the correction result to determine whether abnormal factor weight distribution occurs in the process of matching the bromhidrosis intensity on the wound healing progress in the first wound healing influence factor set;
abnormal weight distribution of factors occurs when the weight distribution is matched with the wound healing progress in the first wound healing influence factor set, and the decision care terminal does not pass through medicine allergy prediction;
the wound healing progress matched with the first wound healing influence factor set is free from factor weight distribution abnormality, and the decision care terminal predicts through drug allergy;
And correspondingly storing the prediction result and the patient after the bromhidrosis operation to be predicted.
According to a second embodiment of the present invention, referring to fig. 5, the present invention claims a prediction system for a delayed wound healing influence factor after bromhidrosis surgery, comprising:
one or more processors;
and a memory having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement a method of predicting factors affecting delayed healing of a wound after an bromhidrosis operation.
According to a third embodiment of the present invention, referring to fig. 6, the present invention claims a prediction system for delayed wound healing influencing factors after bromhidrosis operation, comprising:
the first acquisition module is used for acquiring a patient after the bromhidrosis operation to be predicted and acquiring parameters of the patient after the bromhidrosis operation, which are related to the patient id after the bromhidrosis operation, based on the patient id after the bromhidrosis operation of the patient after the bromhidrosis operation to be predicted, wherein the parameters of the patient after the bromhidrosis operation comprise a patient movement behavior id and an bromhidrosis reaction grade id;
the loading module is used for loading the patient exercise behavior matched with the patient exercise behavior id based on the patient exercise behavior id and loading the patient bromhidrosis reaction grade matched with the bromhidrosis reaction grade id based on the bromhidrosis reaction grade id;
The transmitting module is used for transmitting the bromhidrosis intensity in the bromhidrosis reaction level of the patient to a first wound healing influence factor set for setting the wound healing progress in the nursing end based on the exercise behavior of the patient;
the second obtaining module is used for obtaining a prediction result obtained by predicting the wound healing progress in the first wound healing influence factor set by adopting the bromhidrosis intensity and matching the patient to be predicted with the bromhidrosis postoperative patient.
Further, the sending module includes:
the first decision unit is used for deciding patient prediction influence factors matched with personal attribute factors of the patient after the bromhidrosis operation based on the personal attribute factors of the patient after the bromhidrosis operation to be predicted;
a second decision unit for deciding a second set of wound healing influencing factors from each set of reference wound healing influencing factors based on patient sample conditions of the set of reference wound healing influencing factors for which the patient prediction influencing factors are set;
the construction unit is used for selecting patient prediction influence factors in the second wound healing influence factor set to construct a prediction simulation model matched with the prediction factors of the patient after the bromhidrosis operation to be predicted based on the patient movement behavior;
and the sending unit is used for selecting patient prediction influence factors in the second wound healing influence factor set to send the bromhidrosis intensity in the bromhidrosis reaction level of the patient to the first wound healing influence factor set under the prediction simulation model.
Further, the construction unit is specifically configured to:
acquiring configuration parameters from the exercise behavior of the patient;
and selecting patient prediction influence factors in the second wound healing influence factor set, and constructing a prediction simulation model matched with the prediction factors of the patient after the bromhidrosis operation to be predicted by combining configuration parameters.
Further, the second decision unit is specifically configured to:
deciding a third set of wound healing influencing factors from each set of reference wound healing influencing factors based on patient sample conditions of the set of reference wound healing influencing factors for which patient prediction influencing factors are set, wherein the patient sample conditions of the third set of wound healing influencing factors are balanced by the genus;
based on the predicted factors, a second set of wound healing influencing factors is determined from each third set of wound healing influencing factors.
Further, the patient after the bromhidrosis operation to be predicted is associated with the patient after the bromhidrosis operation to be predicted, and the second decision unit is specifically configured to:
deciding a third set of wound healing influencing factors from each set of reference wound healing influencing factors based on patient sample conditions of the set of reference wound healing influencing factors for which patient prediction influencing factors are set, wherein the patient sample conditions of the third set of wound healing influencing factors are balanced by the genus;
From each third set of wound healing influencing factors, a decision has been made to select a second set of wound healing influencing factors for the patient after the axillary malodour operation.
Further, the second obtaining module is specifically configured to:
inputting predicted factor weight distribution sent by the first wound healing influence factor set through the second wound healing influence factor set, wherein the predicted factor weight distribution is generated by the fact that the wound healing progress in the first wound healing influence factor set is matched with the bromhidrosis intensity;
in the process that the wound healing progress in the first wound healing influence factor set is matched with the bromhidrosis intensity, factor weight distribution correction is carried out on the first wound healing influence factor set so as to obtain a correction result;
and (5) distributing the weight of the prediction factors and correcting the result to be used as a prediction result.
The wound healing progress in the first wound healing influence factor set is used for carrying out fuzzy matching treatment on the bromhidrosis intensity, the prediction factors are the prediction behaviors of fuzzy matching, and the system further comprises:
the first execution module is used for selecting patient prediction influence factors in the second wound healing influence factor set to execute patient movement behaviors so as to determine a first difference between the fuzzy matching prediction factor weight distribution and the fuzzy matching expected result marked on the bromhidrosis intensity;
The first decision module is used for deciding a first prediction index corresponding to the fuzzy matched prediction behavior based on the first distinction;
the first analysis module is used for analyzing the fuzzy matching correction result to determine a factor set to which the bromhidrosis intensity belongs, wherein the bromhidrosis intensity is matched with the wound healing progress in the first wound healing influence factor set;
the second decision module is used for deciding a second prediction index corresponding to the fuzzy matched prediction behavior based on a factor set of the wound healing progress in the first wound healing influence factor set;
and the third decision module is used for deciding the behavior of the nursing terminal in fuzzy matching based on the first prediction index and the second prediction index.
The third decision module is specifically configured to:
deciding the first occurrence time stamp, duration and intensity change of the bromhidrosis intensity; wherein the first occurrence time stamp is the duration between the first time stamp and the second time stamp, the duration is the duration between the first time stamp and the third time stamp, and the intensity change is the duration between the fourth time stamp and the third time stamp; the first time stamp is the time stamp of the first bromhidrosis reaction in the bromhidrosis intensity sent by the patient prediction influence factors in the second wound healing influence factor set, the second time stamp is the time stamp of the first bromhidrosis reaction in the fuzzy matching prediction factor weight distribution input by the second wound healing influence factor set, the third time stamp is the time stamp of the last bromhidrosis reaction in the fuzzy matching prediction factor weight distribution input by the second wound healing influence factor set, and the fourth time stamp is the time stamp of the last bromhidrosis reaction in the bromhidrosis intensity sent by the patient prediction influence factors in the second wound healing influence factor set;
Based on at least one of the first occurrence time stamp, duration and intensity change of the bromhidrosis intensity, deciding a third prediction index corresponding to the fuzzy matching prediction behavior;
and based on the first prediction index, the second prediction index and the third prediction index, deciding the behavior of the nursing terminal in fuzzy matching.
The wound healing progress in the first wound healing influencing factor set is used for carrying out accurate matching treatment on the bromhidrosis intensity, and the prediction factors are the prediction behaviors of accurate matching, and the system further comprises:
the second execution module is used for selecting patient prediction influence factors in a second wound healing influence factor set to execute patient movement behaviors so as to determine a second difference between the precisely matched prediction factor weight distribution and the precisely matched expected result marked on the bromhidrosis intensity;
the second analysis module is used for analyzing the accurate matching correction result so as to determine a first function selected by the bromhidrosis intensity of the wound healing progress matching in the first wound healing influence factor set;
a fourth decision module for deciding a third distinction between the first function and a second function of exact match noted on the bromhidrosis intensity;
and the fifth decision module is used for deciding the behavior of the nursing end in accurate matching based on the second distinction and/or the third distinction.
Further, the predictor is a drug allergy predictor, and the system further comprises:
the third analysis module is used for analyzing the correction result to determine whether factor weight distribution abnormality occurs in the process of matching the bromhidrosis intensity on the wound healing progress in the first wound healing influence factor set;
the sixth decision module is used for matching abnormal weight distribution of the wound healing progress generation factors in the first wound healing influence factor set, and the decision care terminal does not pass through medicine allergy prediction;
and the seventh decision module is used for matching the wound healing progress in the first wound healing influence factor set, wherein no factor weight distribution abnormality occurs, and the decision care terminal predicts through drug allergy.
Further, the system further comprises:
and the storage module is used for correspondingly storing the prediction result and the patient after the bromhidrosis operation to be predicted.
Those skilled in the art will appreciate that various modifications and improvements can be made to the disclosure. For example, the various devices or components described above may be implemented in hardware, or may be implemented in software, firmware, or a combination of some or all of the three.
A flowchart is used in this disclosure to describe the steps of a method in accordance with embodiments of the present disclosure. It should be understood that the steps that follow or before do not have to be performed in exact order. Rather, the various steps may be processed in reverse order or simultaneously. Also, other operations may be added to these processes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps of the methods described above may be implemented by a computer program to instruct related hardware, and the program may be stored in a computer readable storage medium, such as a read only memory, a magnetic disk, or an optical disk. Alternatively, all or part of the steps of the above embodiments may be implemented using one or more integrated circuits. Accordingly, each module/unit in the above embodiment may be implemented in the form of hardware, or may be implemented in the form of a software functional module. The present disclosure is not limited to any specific form of combination of hardware and software.
Unless defined otherwise, all terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The foregoing is illustrative of the present disclosure and is not to be construed as limiting thereof. Although a few exemplary embodiments of this disclosure have been described, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without materially departing from the novel teachings and advantages of this disclosure. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the claims. It is to be understood that the foregoing is illustrative of the present disclosure and is not to be construed as limited to the specific embodiments disclosed, and that modifications to the disclosed embodiments, as well as other embodiments, are intended to be included within the scope of the appended claims. The disclosure is defined by the claims and their equivalents.
In the description of the present specification, reference to the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in the embodiments or examples of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
Claims (10)
1. The prediction method for the factors affecting delayed healing of the wound after the bromhidrosis operation is characterized by comprising the following steps of:
acquiring a patient after the bromhidrosis operation to be predicted, and acquiring parameters of the patient after the bromhidrosis operation, which are related to the patient id after the bromhidrosis operation, based on the patient id after the bromhidrosis operation of the patient after the bromhidrosis operation to be predicted, wherein the parameters of the patient after the bromhidrosis operation comprise a patient movement behavior id and an bromhidrosis reaction grade id;
Based on the patient exercise activity id, loading patient exercise activity matched with the patient exercise activity id;
loading a patient's underarm odor reactive level matching the underarm odor reactive level id based on the underarm odor reactive level id;
transmitting the bromhidrosis intensity in the bromhidrosis reaction level of the patient to a first wound healing influencing factor set setting the wound healing progress in a nursing end based on the patient exercise behavior;
and obtaining a prediction result obtained by predicting the wound healing progress in the first wound healing influence factor set by adopting the bromhidrosis intensity and performing patient prediction matched with the patient after the bromhidrosis operation to be predicted.
2. The method of claim 1, wherein said transmitting the intensity of underarm odor in the patient's underarm odor reactive stage to a first set of wound healing influencing factors that set the progress of wound healing in the care terminal based on the patient's motor behavior, comprises:
based on personal attribute factors of the patient after the bromhidrosis operation to be predicted, deciding patient prediction influence factors matched with the personal attribute factors of the patient after the bromhidrosis operation;
deciding a second set of wound healing influencing factors from each of the set of reference wound healing influencing factors based on patient sample conditions of the set of reference wound healing influencing factors for which the patient prediction influencing factors are set;
Selecting patient prediction influence factors in the second wound healing influence factor set, and constructing a prediction simulation model matched with the prediction factors of the patient after the bromhidrosis operation to be predicted based on the patient movement behaviors;
and selecting patient prediction influence factors in the second wound healing influence factor set to send the bromhidrosis intensity in the bromhidrosis reaction level of the patient to the first wound healing influence factor set under the prediction simulation model.
3. The method for predicting the delayed healing influence factors of the tragomaschalia postoperative wound according to claim 2, wherein the selecting the patient prediction influence factors in the second set of wound healing influence factors is based on the patient movement behavior, and constructing a prediction simulation model matched with the prediction factors of the patient to be predicted after the tragomaschalia postoperative wound comprises:
acquiring configuration parameters from the patient exercise behavior;
selecting patient prediction influence factors in the second wound healing influence factor set, and constructing a prediction simulation model matched with the prediction factors of the patient after the bromhidrosis operation to be predicted by combining the configuration parameters;
the determining a second set of wound healing influencing factors from each of the set of reference wound healing influencing factors based on patient sample conditions of the set of reference wound healing influencing factors for which the patient prediction influencing factors are set, comprising:
Deciding a third set of wound healing influencing factors from each of the set of reference wound healing influencing factors based on patient sample conditions of a set of reference wound healing influencing factors for which the patient prediction influencing factors are set, wherein the patient sample conditions of the third set of wound healing influencing factors are balanced by a genus;
the second set of wound healing influencing factors is determined from each of the third set of wound healing influencing factors based on the predictive factors.
4. The method of claim 2, wherein said determining a second set of wound healing influencing factors from each of said sets of reference wound healing influencing factors based on patient sample conditions for which said patient predicted influencing factors are set of reference wound healing influencing factors comprises:
deciding a third set of wound healing influencing factors from each of the set of reference wound healing influencing factors based on patient sample conditions of a set of reference wound healing influencing factors for which the patient prediction influencing factors are set, wherein the patient sample conditions of the third set of wound healing influencing factors are balanced by a genus;
from each of the third set of wound healing influencing factors, a second set of wound healing influencing factors selected from the patient after the axillary malodour operation has been predicted is determined.
5. The method according to claim 2, wherein the obtaining a prediction result obtained by predicting a patient matched with the patient to be predicted for the wound healing progress in the first wound healing influence factor set using the bromhidrosis intensity, comprises:
inputting a predicted factor weight distribution sent by the first wound healing influence factor set through the second wound healing influence factor set, wherein the predicted factor weight distribution is generated by the fact that the wound healing progress in the first wound healing influence factor set is matched with the bromhidrosis intensity;
in the process that the wound healing progress in the first wound healing influence factor set is matched with the bromhidrosis intensity, factor weight distribution correction is carried out on the first wound healing influence factor set so as to obtain a correction result;
and taking the prediction factor weight distribution and the correction result as the prediction result.
6. The method of claim 5, wherein the progress of wound healing in the first set of wound healing influencing factors is used to perform fuzzy matching on the bromhidrosis intensity, the predicted factors being predicted actions of the fuzzy matching, the method further comprising:
Selecting patient predictive influencing factors in the second wound healing influencing factor set to execute the patient movement behavior so as to decide a first difference between the fuzzy matching predictive factor weight distribution and the fuzzy matching expected result marked on the bromhidrosis intensity;
based on the first distinction, deciding a first prediction index corresponding to the fuzzy matched prediction behavior;
analyzing the fuzzy matching correction result to decide that the wound healing progress in the first wound healing influence factor set matches the factor set to which the bromhidrosis intensity belongs;
based on a factor set of the wound healing progress in the first wound healing influence factor set, deciding a second prediction index corresponding to the fuzzy matching prediction behavior;
and based on the first prediction index and the second prediction index, deciding the behavior of the nursing end in fuzzy matching.
7. The method for predicting factors involved in delayed healing of a wound after bromhidrosis surgery according to claim 6, wherein the deciding the behavior of the care terminal in the fuzzy matching based on the first predictor and the second predictor comprises:
Deciding the first occurrence time stamp, duration and intensity change of the bromhidrosis intensity; wherein the first occurrence time stamp is a time period between a first time stamp and a second time stamp, the time period is a time period between the first time stamp and a third time stamp, and the intensity change is a time period between a fourth time stamp and the third time stamp; the first timestamp is a timestamp of a first bromhidrosis reaction in the bromhidrosis intensity sent by a patient prediction influence factor in the second wound healing influence factor set, the second timestamp is a timestamp of a first bromhidrosis reaction in the fuzzy matching prediction factor weight distribution input by the second wound healing influence factor set, the third timestamp is a timestamp of a last bromhidrosis reaction in the fuzzy matching prediction factor weight distribution input by the second wound healing influence factor set, and the fourth timestamp is a timestamp of a last bromhidrosis reaction in the bromhidrosis intensity sent by a patient prediction influence factor in the second wound healing influence factor set;
deciding a third prediction index corresponding to the fuzzy matching prediction behavior based on at least one of the first occurrence time stamp, the duration and the intensity change of the bromhidrosis intensity;
And deciding the behavior of the nursing end in fuzzy matching based on the first prediction index, the second prediction index and the third prediction index.
8. The method of claim 6, wherein the progress of wound healing in the first set of wound healing influencing factors is used to perform an exact match treatment on the intensity of bromhidrosis, the predictor being the exact matched predicted behavior, the method further comprising:
selecting patient predicted influencing factors in the second set of wound healing influencing factors to perform the patient locomotor activity to determine a second difference between the precisely matched predicted factor weight assignment and the precisely matched expected outcome noted on the bromhidrosis intensity;
analyzing the correction result of the accurate matching to determine that the wound healing progress in the first wound healing influence factor set matches the first function selected by the bromhidrosis intensity;
deciding a third distinction between said first function and said precisely matched second function noted in said bromhidrosis intensity;
based on the second distinction and/or the third distinction, deciding on the behavior of the care end at the exact match.
9. The method of claim 6, wherein the predictive factor is a drug allergy prediction, the method further comprising:
analyzing the correction result to determine whether factor weight distribution abnormality occurs in the process of matching the bromhidrosis intensity on the wound healing progress in the first wound healing influence factor set;
abnormal weight distribution of factors occurs in the wound healing progress matched with the first wound healing influence factor set, and the nursing end is decided not to pass through medicine allergy prediction;
the wound healing progress matched with the first wound healing influence factor set is free from factor weight distribution abnormality, and the nursing end is decided to predict through drug allergy;
and correspondingly storing the prediction result and the patient after the bromhidrosis operation to be predicted.
10. Predicting system to wound delayed healing influence factor after bromhidrosis operation, characterized by comprising:
one or more processors;
a memory having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement the method of predicting factors affecting delayed healing of a wound after an bromhidrosis surgery according to any one of claims 1 to 9.
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