CN117198533A - Perioperative patient anesthesia risk assessment and early warning system based on big data analysis - Google Patents
Perioperative patient anesthesia risk assessment and early warning system based on big data analysis Download PDFInfo
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- 206010002091 Anaesthesia Diseases 0.000 title claims abstract description 45
- 230000037005 anaesthesia Effects 0.000 title claims abstract description 45
- 238000012502 risk assessment Methods 0.000 title claims abstract description 27
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- 238000007781 pre-processing Methods 0.000 claims abstract description 13
- 238000012549 training Methods 0.000 claims abstract description 10
- 238000010801 machine learning Methods 0.000 claims abstract description 9
- 238000005457 optimization Methods 0.000 claims abstract description 7
- 238000000034 method Methods 0.000 claims description 21
- 230000002159 abnormal effect Effects 0.000 claims description 12
- 238000003556 assay Methods 0.000 claims description 12
- 239000008280 blood Substances 0.000 claims description 12
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- DDRJAANPRJIHGJ-UHFFFAOYSA-N creatinine Chemical compound CN1CC(=O)NC1=N DDRJAANPRJIHGJ-UHFFFAOYSA-N 0.000 claims description 12
- 238000001356 surgical procedure Methods 0.000 claims description 7
- 230000000007 visual effect Effects 0.000 claims description 7
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 claims description 6
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- DGAQECJNVWCQMB-PUAWFVPOSA-M Ilexoside XXIX Chemical compound C[C@@H]1CC[C@@]2(CC[C@@]3(C(=CC[C@H]4[C@]3(CC[C@@H]5[C@@]4(CC[C@@H](C5(C)C)OS(=O)(=O)[O-])C)C)[C@@H]2[C@]1(C)O)C)C(=O)O[C@H]6[C@@H]([C@H]([C@@H]([C@H](O6)CO)O)O)O.[Na+] DGAQECJNVWCQMB-PUAWFVPOSA-M 0.000 claims description 6
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 claims description 6
- PNNCWTXUWKENPE-UHFFFAOYSA-N [N].NC(N)=O Chemical compound [N].NC(N)=O PNNCWTXUWKENPE-UHFFFAOYSA-N 0.000 claims description 6
- 230000036772 blood pressure Effects 0.000 claims description 6
- 230000036760 body temperature Effects 0.000 claims description 6
- 229940109239 creatinine Drugs 0.000 claims description 6
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- 238000005534 hematocrit Methods 0.000 claims description 6
- 210000000056 organ Anatomy 0.000 claims description 6
- 229910052700 potassium Inorganic materials 0.000 claims description 6
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- 230000003449 preventive effect Effects 0.000 description 1
Abstract
The invention provides a perioperative patient anesthesia risk assessment and early warning system based on big data analysis, and belongs to the technical field of medical risk assessment. According to the invention, by utilizing a machine learning technology, through collecting the original data of a patient, preprocessing the original data, performing model training and optimization, and finally visually displaying the result of model fitting, personalized and targeted anesthesia risk assessment can be realized, the anesthesia operation risk of the patient can be predicted more accurately, the doctor can be helped to formulate and optimize an operation scheme, the potential risk in the perioperative period is reduced, and the operation safety and medical quality are improved.
Description
Technical Field
The invention belongs to the field of perioperative anesthesia risk assessment, and particularly relates to an perioperative patient anesthesia risk assessment and early warning system based on big data analysis.
Background
With the development of modern medicine, the number of operations in hospitals is greatly increased, and more hospitals pay important attention to the safety in operating rooms. Anesthesia is a key link in the operation process, and risk assessment and safety management of anesthesia are important concerns in the current medical treatment process. Although anesthesiology has evolved over a century, various adverse anesthetic events still occur. The main reason for this is that the use of narcotics has a certain complexity and the medical procedure itself has a certain risk factor. At present, risk assessment is carried out on patients before operation, early warning is carried out on complications of diseases and the risk of anesthesia, the risks can be effectively prevented and avoided, and the method is an important means for guaranteeing the safety of anesthesia.
ASA anesthesia risk classification is currently the most commonly used classification scheme, which classifies patients into six classes according to their physical condition and surgical risk prior to anesthesia, and is used to guide the risk of anesthesia surgery in practice. However, the classification criteria are rough in evaluating the condition of the patient, and the risk prediction for the anesthesia operation is not accurate enough, so that a more intelligent method for the anesthesia operation is still needed in the industry to provide effective prediction, and the anesthesia operation scheme is modified and formulated according to the risk rating, so that preventive measures are taken, potential risks in the perioperative period are reduced, and the operation safety and medical quality are improved.
Disclosure of Invention
The invention aims to solve the problems in the prior art, and provides an perioperative patient anesthesia risk assessment and early warning system based on big data analysis, which can accurately assess and predict anesthesia operation risks and reduce potential operation risks.
The invention is realized by the following technical scheme:
an embodiment provides a perioperative patient anesthesia risk assessment and early warning system based on big data analysis, including:
1) A data capture unit: capturing original data of a plurality of patients in a medical database as an initial data set;
2) A data processing unit: performing data preprocessing on the initial data set to obtain a basic data set, wherein the data preprocessing comprises abnormal value removal, missing data estimation, and optimization of classification variables and nominal variables; wherein, for a nominal variable and a classification variable having more than two levels, each level is replaced with a ratio Q calculated by equation (1):
Q＝log[P(X i ＝x i |E＝1)/P(X i ＝x i |E＝0)] (1)
e=0 and e=1 represent both positive and negative results, P (X i =x) |e=e is an estimated value using equation (2),
E j representing the classification variable x i The result at the level j is that,indicating the occurrence of E j =e and->Is a number of times (1);
3) Model prediction unit: training and optimizing a machine learning model using the base dataset, the model being defined as,
here, let Y be a classification variable with levels A, B and C, and with m predictors X (1) 、X (2) 、…、X (m) The method comprises the steps of carrying out a first treatment on the surface of the Beta is the influence factor of each predictor; p (P) A ＝P[Y＝A|X]，P B ＝P[Y＝B|X]，P C ＝P[Y＝C|X]And P is A +P B +P C =1; based on the above formulas (3) and (4), all the possibilities are calculated by the following formulas (5) and (6)It is derived that the method comprises the steps of,
4) And a result display unit: and visually displaying the result output by the model prediction unit, predicting the patient perioperative anesthesia risk probability, and early warning the abnormal result.
Further, the raw data comprises basic body indexes, assay indexes, operation information of a patient and ASA grading given by doctors;
further, the basic body indicators include age, weight, BMI, heart rate, blood pressure, respiration, body temperature;
further, the assay indicator comprises sodium, potassium, creatinine, urea nitrogen, hemoglobin, hematocrit, and blood glucose;
further, the surgical information includes a type of surgery, a surgical grade, an organ status, and a blood loss.
Further, the removing outliers includes removing outliers that are considered unreasonable by medical professionals, and removing observations located at the top and bottom 1% of the continuous variable distribution;
further, the estimating missing data includes estimating missing values using an automatic algorithm.
Further, the visual display includes displaying the risk probabilities on a display screen while recording the risk probabilities in an initial dataset of a medical repository.
An embodiment II provides a perioperative patient anesthesia risk assessment and early warning method based on big data analysis, which comprises the following steps:
1) Capturing original data: capturing original data of a plurality of patients in a medical database as an initial data set;
2) Raw data processing: performing data preprocessing on the initial data set to obtain a basic data set, wherein the data preprocessing comprises abnormal value removal, missing data estimation, and optimization of classification variables and nominal variables; wherein, for a nominal variable and a classification variable having more than two levels, each level is replaced with a ratio Q calculated by equation (1):
Q＝log[P(X i ＝x i |E＝1)/P(X i ＝x i |E＝0)] (1)
e=0 and e=1 represent both positive and negative results, P (X i =x) |e=e is an estimated value using equation (2),
E j representing the classification variable x i The result at the level j is that,indicating the occurrence of E j =e and->Is a number of times (1);
3) Model training: training and optimizing a machine learning model using the base dataset, the model being defined as,
here, let Y be a classification variable with levels A, B and C, and with m predictors X (1) 、X (2) 、…、X (m) The method comprises the steps of carrying out a first treatment on the surface of the Beta is the influence factor of each predictor; p (P) A ＝P[Y＝A|X]，P B ＝P[Y＝B|X]，P C ＝P[Y＝C|X]And P is A +P B +P C =1; based on the above formulas (3) and (4), all the possibilities are calculated from the following formulas (5) and (6),
4) Visual display: and visually displaying the result output by the model prediction unit, predicting the patient perioperative anesthesia risk probability, and early warning the abnormal result.
Preferably, the raw data includes basic body index of the patient, assay index, operation information, and ASA grade given by doctor;
preferably, the basic body index comprises age, weight, BMI, heart rate, blood pressure, respiration, body temperature;
preferably, the assay indicator comprises sodium, potassium, creatinine, urea nitrogen, hemoglobin, hematocrit, and blood glucose;
preferably, the surgical information includes a type of surgery, a surgical grade, an organ status, and a blood loss.
Preferably, the removing outliers includes removing outliers that are considered unreasonable by the medical expert, and removing observations located at the top and bottom 1% of the continuous variable distribution;
preferably, the estimating missing data includes estimating missing values using an automatic algorithm.
Preferably, the visual display comprises displaying the risk probability on a display screen while recording the risk probability in an initial dataset of a medical repository.
Compared with the prior art, the invention provides the perioperative patient anesthesia risk assessment and early warning system based on big data analysis, and the personalized and targeted anesthesia risk assessment can be realized by utilizing the machine learning technology, so that the anesthesia operation risk of the patient can be predicted more accurately, the doctor can make and optimize an operation scheme, and the operation risk is reduced.
Drawings
FIG. 1 is a schematic diagram of an perioperative patient anesthesia risk assessment and early warning system based on big data analysis.
FIG. 2 is a schematic illustration of a perioperative patient anesthesia risk assessment and early warning method based on big data analysis.
Detailed Description
The invention is described in further detail below with reference to the attached drawing figures:
example 1
As shown in fig. 1, there is provided an perioperative patient anesthesia risk assessment and early warning system based on big data analysis, including:
1) A data capture unit: capturing original data of a plurality of patients in a medical database as an initial data set;
2) A data processing unit: performing data preprocessing on the initial data set to obtain a basic data set, wherein the data preprocessing comprises abnormal value removal, missing data estimation, and optimization of classification variables and nominal variables; wherein, for a nominal variable and a classification variable having more than two levels, each level is replaced with a ratio Q calculated by equation (1):
Q＝log[P(X i ＝x i |E＝1)/P(X i ＝x i |E＝0)] (1)
e=0 and e=1 represent both positive and negative results, P (X i =x) |e=e is an estimated value using equation (2),
E j representing the classification variable x i The result at the level j is that,indicating the occurrence of E j =e and->Is a number of times (1);
3) Model prediction unit: training and optimizing a machine learning model using the base dataset, the model being defined as,
here, let Y be a classification variable with levels A, B and C, and with m predictors X (1) 、X (2) 、…、X (m) The method comprises the steps of carrying out a first treatment on the surface of the Beta is the influence factor of each predictor; p (P) A ＝P[Y＝A|X]，P B ＝P[Y＝B|X]，P C ＝P[Y＝C|X]And P is A +P B +P C =1; based on the above formulas (3) and (4), all the possibilities are calculated from the following formulas (5) and (6),
5) And a result display unit: and visually displaying the result output by the model prediction unit, predicting the patient perioperative anesthesia risk probability, and early warning the abnormal result.
Wherein the original data comprises basic body indexes, assay indexes, operation information and ASA grading given by doctors of a patient; the basic body index comprises age, weight, BMI, heart rate, blood pressure, respiration and body temperature; the assay index comprises sodium, potassium, creatinine, urea nitrogen, hemoglobin, hematocrit and blood glucose; the surgical information includes a type of surgery, a surgical grade, an organ status, and a blood loss.
Removing outliers includes removing outliers that are considered unreasonable by medical professionals, and removing observations located at the top and bottom 1% of the continuous variable distribution; estimating missing data includes estimating missing values using an automatic algorithm.
The visualizing comprises displaying the risk probabilities on a display screen while recording the risk probabilities in an initial dataset of a medical repository.
Example two
As shown in fig. 2, a method for evaluating and pre-warning anesthesia risk of a perioperative patient based on big data analysis is provided, which comprises the following steps:
1) Capturing original data: capturing original data of a plurality of patients in a medical database as an initial data set;
2) Raw data processing: performing data preprocessing on the initial data set to obtain a basic data set, wherein the data preprocessing comprises abnormal value removal, missing data estimation, and optimization of classification variables and nominal variables; wherein, for a nominal variable and a classification variable having more than two levels, each level is replaced with a ratio Q calculated by equation (1):
Q＝log[P(X i ＝x i |E＝1)/P(X i ＝x i |E＝0)] (1)
e=0 and e=1 represent both positive and negative results, P (X i =x) |e=e is an estimated value using equation (2),
E j representing the classification variable x i The result at the level j is that,indicating the occurrence of E j =e and->Is a number of times (1);
3) Model training: training and optimizing a machine learning model using the base dataset, the model being defined as,
here, let Y be a classification variable with levels A, B and C, and with m predictors X (1) 、X (2) 、…、X (m) The method comprises the steps of carrying out a first treatment on the surface of the Beta is the influence factor of each predictor; p (P) A ＝P[Y＝A|X]，P B ＝P[Y＝B|X]，P C ＝P[Y＝C|X]And P is A +P B +P C =1; based on the above formulas (3) and (4), all the possibilities are calculated from the following formulas (5) and (6),
4) Visual display: and visually displaying the result output by the model prediction unit, predicting the patient perioperative anesthesia risk probability, and early warning the abnormal result.
Wherein the original data comprises basic body indexes, assay indexes, operation information and ASA grading given by doctors of a patient; the basic body index comprises age, weight, BMI, heart rate, blood pressure, respiration and body temperature; the assay index comprises sodium, potassium, creatinine, urea nitrogen, hemoglobin, hematocrit and blood glucose; the surgical information includes a type of surgery, a surgical grade, an organ status, and a blood loss.
Removing outliers includes removing outliers that are considered unreasonable by medical professionals, and removing observations located at the top and bottom 1% of the continuous variable distribution; estimating missing data includes estimating missing values using an automatic algorithm.
The visualizing comprises displaying the risk probabilities on a display screen while recording the risk probabilities in an initial dataset of a medical repository.
Compared with the prior art, the invention provides the perioperative patient anesthesia risk assessment and early warning system based on big data analysis, and the personalized and targeted anesthesia risk assessment can be realized by utilizing the machine learning technology, so that the anesthesia operation risk of the patient can be predicted more accurately, the doctor can make and optimize an operation scheme, and the operation risk is reduced.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "connected," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the description of the present invention, unless otherwise indicated, the terms "upper," "lower," "left," "right," "inner," "outer," and the like are used for convenience in describing the present invention and simplifying the description based on the orientation or positional relationship shown in the drawings, and do not denote or imply that the devices or elements in question must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Finally, it should be noted that the above-mentioned technical solution is only one embodiment of the present invention, and various modifications and variations can be easily made by those skilled in the art based on the application methods and principles disclosed in the present invention, and are not limited to the methods described in the above-mentioned specific embodiments of the present invention, therefore, the foregoing description is only preferred, and not meant to be limiting.
Claims (8)
1. An perioperative patient anesthesia risk assessment and early warning system based on big data analysis, comprising:
1) A data capture unit: capturing original data of a plurality of patients in a medical database as an initial data set;
2) A data processing unit: performing data preprocessing on the initial data set to obtain a basic data set, wherein the data preprocessing comprises abnormal value removal, missing data estimation, and optimization of classification variables and nominal variables; wherein, for a nominal variable and a classification variable having more than two levels, each level is replaced with a ratio Q calculated by equation (1):
Q＝log[P(X i ＝x i |E＝1)/P(X i ＝x i |E＝0)] (1)
e=0 and e=1 represent both positive and negative results, P (X i =x) |e=e is an estimated value using equation (2),
E j representing the classification variable x i The result at the level j is that,indicating the occurrence of E j =e and->Is a number of times (1);
3) Model prediction unit: training and optimizing a machine learning model using the base dataset, the model being defined as,
here, let Y be a classification variable with levels A, B and C, and with m predictors X (1) 、X (2) 、…、X (m) The method comprises the steps of carrying out a first treatment on the surface of the Beta is the influence factor of each predictor; p (P) A ＝P[Y＝A|X]，P B ＝P[Y＝B|X]，P C ＝P[Y＝C|X]And P is A +P B +P C =1; based on the above formulas (3) and (4), all the possibilities are calculated from the following formulas (5) and (6),
4) And a result display unit: and visually displaying the result output by the model prediction unit, predicting the patient perioperative anesthesia risk probability, and early warning the abnormal result.
2. The perioperative patient anesthesia risk assessment and early warning system based on big data analysis of claim 1, wherein: the original data comprise basic body indexes, assay indexes, operation information and ASA grades given by doctors of the patients;
wherein the basic body index comprises age, weight, BMI, heart rate, blood pressure, respiration, and body temperature;
the assay index comprises sodium, potassium, creatinine, urea nitrogen, hemoglobin, hematocrit and blood glucose;
the surgical information includes a type of surgery, a surgical grade, an organ status, and a blood loss.
3. The perioperative patient anesthesia risk assessment and early warning system based on big data analysis of claim 1, wherein: the outlier removal includes outliers which are considered unreasonable by medical professionals, and observations which are located at the top and bottom 1% of the continuous variable distribution;
the estimating missing data includes estimating missing values using an automatic algorithm.
4. The perioperative patient anesthesia risk assessment and early warning system based on big data analysis of claim 1, wherein: the visual display includes displaying the risk probabilities on a display screen while recording the risk probabilities in an initial dataset of a medical repository.
5. An perioperative patient anesthesia risk assessment and early warning method based on big data analysis comprises the following steps:
1) Capturing original data: capturing original data of a plurality of patients in a medical database as an initial data set;
2) Raw data processing: performing data preprocessing on the initial data set to obtain a basic data set, wherein the data preprocessing comprises abnormal value removal, missing data estimation, and optimization of classification variables and nominal variables; wherein, for a nominal variable and a classification variable having more than two levels, each level is replaced with a ratio Q calculated by equation (1):
Q＝log[P(X i ＝x i |E＝1)/P(X i ＝x i |E＝0)] (1)
e=0 and e=1 represent both positive and negative results, P (X i =x) |e=e is an estimated value using equation (2),
E j representing the classification variable x i The result at the level j is that,indicating the occurrence of E j =e and->Is a number of times (1);
3) Model training: training and optimizing a machine learning model using the base dataset, the model being defined as,
here, let Y be a classification variable with levels A, B and C, and with m predictors X (1) 、X (2) 、…、X (m) The method comprises the steps of carrying out a first treatment on the surface of the Beta is the influence factor of each predictor; p (P) A ＝P[Y＝A|X]，P B ＝P[Y＝B|X]，P C ＝P[Y＝C|X]And P is A +P B +P C =1; based on the above formulas (3) and (4), all the possibilities are calculated from the following formulas (5) and (6),
4) Visual display: and visually displaying the result output by the model prediction unit, predicting the patient perioperative anesthesia risk probability, and early warning the abnormal result.
6. The perioperative patient anesthesia risk assessment and early warning method based on big data analysis of claim 5, wherein: the original data comprise basic body indexes, assay indexes, operation information and ASA grades given by doctors of the patients;
wherein the basic body index comprises age, weight, BMI, heart rate, blood pressure, respiration, and body temperature;
the assay index comprises sodium, potassium, creatinine, urea nitrogen, hemoglobin, hematocrit and blood glucose;
the surgical information includes a type of surgery, a surgical grade, an organ status, and a blood loss.
7. The perioperative patient anesthesia risk assessment and early warning method based on big data analysis of claim 5, wherein: the outlier removal includes outliers which are considered unreasonable by medical professionals, and observations which are located at the top and bottom 1% of the continuous variable distribution;
the estimating missing data includes estimating missing values using an automatic algorithm.
8. The perioperative patient anesthesia risk assessment and early warning method based on big data analysis of claim 5, wherein: the visual display includes displaying the risk probabilities on a display screen while recording the risk probabilities in an initial dataset of a medical repository.
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2023
- 2023-09-11 CN CN202311166252.7A patent/CN117198533A/en active Pending
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