CN113974900A - Laboratory anesthesia information processing system and method based on big data - Google Patents
Laboratory anesthesia information processing system and method based on big data Download PDFInfo
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Abstract
The invention belongs to the technical field of anesthesia information processing, and discloses a laboratory anesthesia information processing system and method based on big data, which comprises the steps of collecting mouse electrocardiogram, blood pressure data, oxyhemoglobin saturation data and respiratory frequency data; setting an anesthesia mode, an anesthesia medicine concentration and an anesthesia speed autonomously; and calculating the dosage of the anesthetic; supplying oxygen to the mouse; altering the temperature of the mouse; anaesthetizing the mice; adjusting the concentration of the anesthetic drug and the anesthetic rate; judging the anesthesia state of the mouse; constructing an anesthesia evaluation decision tree; calculating the time of anesthesia; recording the acquired mouse related data in real time; meanwhile, the anesthesia information is processed through a big data processing program. The invention can carry out various anesthesia experiments, set parameters in the relevant anesthesia process, acquire different mouse data, further carry out comparative analysis on the mouse data and carry out the research of the anesthesia experiments.
Description
Technical Field
The invention belongs to the technical field of anesthesia information processing, and particularly relates to a laboratory anesthesia information processing system and method based on big data.
Background
Anesthesia is a surgical treatment that is performed by using a drug or other methods to make the mouse lose the sensation temporarily in whole or in part so as to achieve the purpose of no pain. Anesthesiology (anesthesiology) is a science that applies basic theory, clinical knowledge and technology about anesthesia to eliminate operation pain of mice, ensure safety of mice and create good conditions for operations. Nowadays, anesthesiology has become a special independent subject in clinical medicine, mainly including clinical anesthesiology, emergency resuscitation medicine, critical care treatment, pain diagnosis and treatment, and other related medicine and mechanism research, and is a comprehensive subject for researching anesthesia, analgesia, emergency resuscitation and critical medicine. Wherein clinical anesthesia is a major component of modern anesthesiology. However, the existing laboratory anesthesia information processing system and method based on big data has large errors in manual calculation of the anesthesia dosage; at the same time, the evaluation of anesthesia is inefficient.
In summary, the problems of the prior art are as follows: in the prior art, no intelligent management control system or software aiming at the anesthesia experiment exists, so that the targeted anesthesia experiment setting cannot be realized, and the comparative analysis of different anesthesia experiments cannot be carried out; the existing laboratory anesthesia information processing system and method based on big data has large error of manual calculation of the anesthesia dosage; at the same time, the evaluation of anesthesia is inefficient.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a laboratory anesthesia information processing system and method based on big data.
The invention is realized in this way, a laboratory anesthesia information processing system based on big data, the laboratory anesthesia information processing system based on big data includes:
the electrocardiogram acquisition module is connected with the central control module and is used for acquiring mouse electrocardiogram data through medical equipment;
the blood pressure acquisition module is connected with the central control module and is used for acquiring blood pressure data of the mice through a blood pressure meter;
the blood oxygen saturation acquisition module is connected with the central control module and is used for detecting the blood oxygen saturation data of the mouse through the blood oxygen detector;
the respiratory parameter acquisition module is connected with the central control module and is used for monitoring the respiratory frequency of the mouse by using a respiratory monitor;
the central control module is connected with the electrocardiogram acquisition module, the blood pressure acquisition module, the oxyhemoglobin saturation acquisition module, the respiratory parameter acquisition module, the anesthesia selection module, the calculation module, the oxygen supply module, the temperature regulation module, the anesthesia adjustment module, the anesthesia state judgment module, the anesthesia evaluation construction module, the time calculation module, the recording module, the big data processing module, the comparison analysis module and the display module, and is used for controlling the normal work of each module through a host;
the anesthesia selection module is connected with the central control module and comprises an anesthesia mode selection unit, an anesthesia medicine selection unit, a medicine concentration selection unit and an anesthesia speed regulation unit; for selecting an anesthetic drug or an anesthetic mode;
the calculation module is connected with the central control module and is used for calculating the dosage of the anesthetic drugs based on the selected anesthetic drugs and the mode through a calculation program;
the oxygen supply module is connected with the central control module and is used for supplying oxygen to the mouse through oxygen supply equipment;
the temperature control module is connected with the central control module and is used for changing the temperature of the mouse by using temperature control equipment;
the anesthesia module is connected with the central control module and used for anesthetizing the mouse through the anesthesia machine;
the anesthesia adjusting module is connected with the central control module and is used for adjusting the concentration of the anesthetic drug and the anesthesia rate;
the anesthesia state judgment module is connected with the central control module and used for judging the anesthesia state of the mouse through a judgment program;
the anesthesia evaluation construction module is connected with the central control module and used for constructing an anesthesia evaluation decision tree through a construction program;
the time calculation module is connected with the central control module and used for calculating the anesthetized time;
the recording module is connected with the central control module and is used for recording the acquired mouse-related electrocardiogram, blood pressure, blood oxygen saturation, respiratory rate, anesthesia state and other related data in real time;
the big data processing module is connected with the central control module and used for processing the anesthesia information through a big data processing program and displaying the anesthesia information in a chart or other forms;
the comparison analysis module is connected with the central control module and is used for performing comparison analysis on the acquired data and other standard data or data before and after adjustment;
and the display module is connected with the central control module and is used for displaying the mouse electrocardiogram, the blood pressure, the blood oxygen saturation, the respiratory rate, the anesthesia state and other related data in a table form through the display.
Further, the anesthesia selection module comprises:
the device comprises an anesthesia mode selection unit, an anesthesia medicine selection unit, a medicine concentration selection unit and an anesthesia speed regulation unit;
an anesthesia mode selection unit: for autonomously selecting an anesthesia mode;
an anesthetic selection unit: for selecting an anesthetic drug;
drug concentration selection unit: for setting the concentration based on the selected anesthetic drug;
an anesthesia speed adjusting unit: for setting the speed of anesthesia.
Another object of the present invention is to provide a big-data-based laboratory anesthesia information processing method applied to the big-data-based laboratory anesthesia information processing system, the big-data-based laboratory anesthesia information processing method comprising the steps of:
step one, acquiring electrocardiogram data of a mouse through medical equipment; collecting blood pressure data of the mouse through a blood pressure meter; detecting blood oxygen saturation data of the mouse through a blood oxygen detector; monitoring the respiratory frequency of the mouse by using a respiratory monitor;
automatically setting an anesthesia mode, an anesthesia medicine concentration and an anesthesia speed; calculating the dosage of the anesthetic based on the selected anesthetic and the mode through a calculation program;
thirdly, supplying oxygen to the mouse through oxygen supply equipment; changing the temperature of the mouse by using a temperature control device; anaesthetizing the mouse by an anaesthesia machine;
regulating the concentration and the anesthesia rate of the anesthetic; judging the anesthesia state of the mouse through a judgment program; constructing an anesthesia evaluation decision tree through a construction program;
step five, calculating the anesthesia time; recording the acquired relevant electrocardiogram, blood pressure, blood oxygen saturation, respiratory frequency, anesthesia state and other relevant data of the mouse in real time; meanwhile, the anesthesia information is processed through a big data processing program and displayed in a chart or other forms;
comparing and analyzing the acquired data with other standard data or data before and after adjustment; the electrocardiogram of the mouse, the blood pressure, the blood oxygen saturation, the respiratory rate, the anesthesia state, the result of the comparative analysis and other relevant data are displayed in a tabular form through a display.
Further, in step two, the calculation method is as follows:
a. measuring by a pressure gauge to obtain the concentration of anesthetic gas, measuring by a flowmeter to obtain the flow of the mixed gas, and measuring to obtain time;
b. calculating the dosage of the gaseous anesthetic according to the concentration of the anesthetic gas, the flow rate of the mixed gas and the time;
c. and according to the type of the anesthetic gas, the controller calculates the liquid anesthetic dosage corresponding to the gas anesthetic dosage according to a gas molar volume calculation method.
Further, in step four, the anesthesia assessment construction method comprises the following steps:
(1) acquiring a training sample of an anesthesia evaluation decision tree through a construction program, and determining a branch variable of the anesthesia evaluation decision tree according to an information gain rate of the training sample;
(2) obtaining a verification sample of the anesthesia evaluation decision tree, and performing post pruning on the branch variables according to the verification sample to obtain a final anesthesia evaluation decision tree;
(3) the final anesthesia evaluation decision tree is used to output an anesthesia level output variable.
Further, the obtaining of the training sample of the anesthesia evaluation decision tree includes:
and extracting 70% of the data in the big data of the anesthesia evaluation as a training sample of the anesthesia evaluation decision tree.
Further, the determining branch variables of the anesthesia evaluation decision tree according to the information gain rate of the training samples, and accordingly, the information gain rate of the training samples includes:
wherein a is a life feature attribute; the Gain _ ratio is the information Gain rate of the training sample with the selected vital sign attribute a as the split attribute; d is a training sample of the anesthesia evaluation decision tree; gain is the information Gain of selecting the vital sign attribute a as the split attribute; IV is the information entropy of a; ent is the information entropy of D; di is dividing D according to the vital sign attribute a to generate V branch nodes, wherein the ith branch node comprises the number of training samples of an anesthesia evaluation decision tree taking a value as ai in D; pk is the proportion of the kth sample in D; and y is the number of types of samples in D.
Further, the obtaining a verification sample of the anesthesia evaluation decision tree includes:
30% of the large anesthesia evaluation data are extracted as verification samples of the anesthesia evaluation decision tree.
Further, the post-pruning the branch variables according to the verification sample to obtain a final anesthesia evaluation decision tree, including:
(1.1) acquiring the anesthesia evaluation result error of a single node through a positive space distribution table by adopting a confidence interval method, and acquiring the anesthesia evaluation result errors of all child nodes under the father node aiming at the father node of the single node;
(1.2) obtaining weighted values of the anesthesia evaluation result errors of all the child nodes, and if the weighted values are larger than the anesthesia evaluation result error of the father node and the anesthesia evaluation result error of a single node is the minimum value, pruning and removing all the child nodes under the father node.
It is another object of the present invention to provide a computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface to implement the big data based laboratory anesthesia information processing method when executed on an electronic device.
It is another object of the present invention to provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the big-data based laboratory anesthesia information processing method.
The invention has the advantages and positive effects that: the invention can carry out various anesthesia experiments, set parameters in the relevant anesthesia process, acquire different mouse data, further carry out comparative analysis on the mouse data and carry out the research of the anesthesia experiments.
According to the invention, the gas anesthetic dosage in the whole anesthesia process can be accurately calculated through the calculation module, errors caused by manual calculation are avoided, and the accuracy and precision of the calculation result of the liquid anesthetic dosage are effectively improved; meanwhile, the model training method is adopted by the anesthesia evaluation construction module, and the model is subjected to post-pruning by combining a confidence interval method, so that an anesthesia evaluation decision tree model for anesthesia condition evaluation can be obtained, the workload of preoperative anesthesia evaluation is reduced, and the efficiency of preoperative anesthesia evaluation is improved.
Drawings
Fig. 1 is a schematic structural diagram of a laboratory anesthesia information processing system based on big data according to an embodiment of the present invention.
In the figure: 1. an electrocardiogram acquisition module; 2. a blood pressure acquisition module; 3. a blood oxygen saturation collecting module; 4. a respiratory parameter acquisition module; 5. a central control module; 6. an anesthesia selection module; 7. a calculation module; 8. an oxygen supply module; 9. a temperature regulation module; 10. an anesthesia module; 11. an anesthesia adjustment module; 12. an anesthesia state judgment module; 13. an anesthesia evaluation construction module; 14. a time calculation module; 15. a recording module; 16. a big data processing module; 17. a comparison analysis module; 18. and a display module.
Fig. 2 is a schematic structural diagram of an anesthesia selection module according to an embodiment of the present invention.
In the figure: 19. an anesthesia mode selection unit; 20. an anesthetic drug selection unit; 21. a drug concentration selection unit; 22. an anesthesia speed adjusting unit.
Fig. 3 is a flowchart of a laboratory anesthesia information processing method based on big data according to an embodiment of the present invention.
Fig. 4 is a flowchart of a calculation method of a calculation module according to an embodiment of the present invention.
Fig. 5 is a flowchart of a method for constructing an anesthesia evaluation building block according to an embodiment of the present invention.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings.
The structure of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, a big data-based laboratory anesthesia information processing system according to an embodiment of the present invention includes:
and the electrocardiogram acquisition module 1 is connected with the central control module 5 and is used for acquiring mouse electrocardiogram data through medical equipment.
And the blood pressure acquisition module 2 is connected with the central control module 5 and is used for acquiring the blood pressure data of the mice through a blood pressure meter.
And the blood oxygen saturation acquisition module 3 is connected with the central control module 5 and is used for detecting the blood oxygen saturation data of the mouse through the blood oxygen detector.
And the respiratory parameter acquisition module 4 is connected with the central control module 5 and is used for monitoring the respiratory frequency of the mouse by using a respiratory monitor.
The central control module 5 is connected with the electrocardiogram acquisition module 1, the blood pressure acquisition module 2, the oxyhemoglobin saturation acquisition module 3, the respiratory parameter acquisition module 4, the anesthesia selection module 6, the calculation module 7, the oxygen supply module 8, the temperature regulation and control module 9, the anesthesia module 10, the anesthesia adjustment module 11, the anesthesia state judgment module 12, the anesthesia evaluation construction module 13, the time calculation module 14, the recording module 15, the big data processing module 16, the contrastive analysis module 17 and the display module 18, and is used for controlling the normal work of each module through a host.
The anesthesia selection module 6 is connected with the central control module 5 and comprises an anesthesia mode selection unit 19, an anesthesia medicine selection unit 20, a medicine concentration selection unit 21 and an anesthesia speed regulation unit 22; used for selecting anesthetic drugs or anesthetic modes.
And the calculation module 7 is connected with the central control module 5 and is used for calculating the dosage of the anesthetic based on the selected anesthetic and the mode through a calculation program.
And the oxygen supply module 8 is connected with the central control module 5 and is used for supplying oxygen to the mouse through oxygen supply equipment.
And the temperature regulating module 9 is connected with the central control module 5 and is used for changing the temperature of the mouse by using temperature control equipment.
And the anesthesia module 10 is connected with the central control module 5 and is used for anesthetizing the mouse through an anesthesia machine.
And the anesthesia adjusting module 11 is connected with the central control module 5 and is used for adjusting the concentration of the anesthetic drug and the anesthesia rate.
And the anesthesia state judgment module 12 is connected with the central control module 5 and is used for judging the anesthesia state of the mouse through a judgment program.
And the anesthesia evaluation construction module 13 is connected with the central control module 5 and is used for constructing an anesthesia evaluation decision tree through a construction program.
And the time calculating module 14 is connected with the central control module 5 and is used for calculating the anaesthetized time.
And the recording module 15 is connected with the central control module 5 and is used for recording the acquired mouse-related electrocardiogram, blood pressure, blood oxygen saturation, respiratory rate, anesthesia state and other related data in real time.
And the big data processing module 16 is connected with the central control module 5 and is used for processing the anesthesia information through a big data processing program and displaying the anesthesia information in a chart or other forms.
And the comparison analysis module 17 is connected with the central control module 5 and is used for performing comparison analysis on the acquired data and other standard data or data before and after adjustment.
And the display module 18 is connected with the central control module 5 and is used for displaying the mouse electrocardiogram, the blood pressure, the blood oxygen saturation, the respiratory frequency, the anesthesia state and other related data in a table form through a display.
As shown in fig. 2, an anesthesia selection module 6 according to an embodiment of the present invention includes:
an anesthesia mode selection unit 19, an anesthetic drug selection unit 20, a drug concentration selection unit 21, and an anesthesia speed adjustment unit 22.
Anesthesia mode selection unit 19: for autonomous selection of the anesthesia mode.
The anesthetic drug selection unit 20: for selecting anesthetic drugs.
Drug concentration selection unit 21: for concentration setting based on the selected anesthetic drug.
The anesthesia speed adjusting unit 22: for setting the speed of anesthesia.
As shown in fig. 3, the laboratory anesthesia information processing method based on big data according to the embodiment of the present invention includes the following steps:
s101, acquiring electrocardiogram data of the mouse through medical equipment; collecting blood pressure data of the mouse through a blood pressure meter; detecting blood oxygen saturation data of the mouse through a blood oxygen detector; and monitoring the respiratory frequency of the mouse by using a respiratory monitor.
S102, automatically setting an anesthesia mode, an anesthesia medicine concentration and an anesthesia speed; and calculating the dosage of the anesthetic medicine based on the selected anesthetic medicine and the mode through a calculation program.
S103, supplying oxygen to the mouse through an oxygen supply device; changing the temperature of the mouse by using a temperature control device; the mice were anesthetized by an anesthesia machine.
S104, adjusting the concentration and the anesthesia rate of the anesthetic; judging the anesthesia state of the mouse through a judgment program; and constructing an anesthesia evaluation decision tree through a construction program.
S105, calculating the anesthesia time; recording the acquired relevant electrocardiogram, blood pressure, blood oxygen saturation, respiratory frequency, anesthesia state and other relevant data of the mouse in real time; meanwhile, the anesthesia information is processed through a big data processing program and displayed in a chart or other forms.
S106, comparing and analyzing the acquired data with other standard data or data before and after adjustment; the electrocardiogram of the mouse, the blood pressure, the blood oxygen saturation, the respiratory rate, the anesthesia state, the result of the comparative analysis and other relevant data are displayed in a tabular form through a display.
The technical solution of the present invention is further illustrated by the following specific examples.
Example 1
The laboratory anesthesia information processing method based on big data provided by the embodiment of the invention is shown in fig. 3, as a preferred embodiment, as shown in fig. 4, the calculation method provided by the embodiment of the invention is as follows:
s201, measuring by a pressure gauge to obtain the concentration of anesthetic gas, measuring by a flowmeter to obtain the flow of mixed gas, and measuring to obtain time;
s202, calculating the dosage of the gaseous anesthetic according to the concentration of the anesthetic gas, the flow rate of the mixed gas and the time;
s203, according to the type of the anesthetic gas, the controller calculates the liquid anesthetic dosage corresponding to the gas anesthetic dosage according to a gas molar volume calculation method.
Example 2
The laboratory anesthesia information processing method based on big data provided by the embodiment of the invention is shown in fig. 3, as a preferred embodiment, as shown in fig. 5, the anesthesia evaluation construction method provided by the embodiment of the invention is as follows:
s301, obtaining a training sample of an anesthesia evaluation decision tree through a construction program, and determining a branch variable of the anesthesia evaluation decision tree according to an information gain rate of the training sample.
S302, obtaining a verification sample of the anesthesia evaluation decision tree, and performing post pruning on the branch variables according to the verification sample to obtain the final anesthesia evaluation decision tree.
And S303, the final anesthesia evaluation decision tree is used for outputting an anesthesia grade output variable.
The training sample for obtaining the anesthesia evaluation decision tree provided by the embodiment of the invention comprises the following steps:
and extracting 70% of the data in the big data of the anesthesia evaluation as a training sample of the anesthesia evaluation decision tree.
In the embodiment of the present invention, the branch variables of the anesthesia evaluation decision tree are determined according to the information gain rate of the training sample, and accordingly, the information gain rate of the training sample includes:
wherein a is a life feature attribute; the Gain _ ratio is the information Gain rate of the training sample with the selected vital sign attribute a as the split attribute; d is a training sample of the anesthesia evaluation decision tree; gain is the information Gain of selecting the vital sign attribute a as the split attribute; IV is the information entropy of a; ent is the information entropy of D; di is dividing D according to the vital sign attribute a to generate V branch nodes, wherein the ith branch node comprises the number of training samples of an anesthesia evaluation decision tree taking a value as ai in D; pk is the proportion of the kth sample in D; and y is the number of types of samples in D.
The verification sample for obtaining the anesthesia evaluation decision tree provided by the embodiment of the invention comprises the following steps:
30% of the large anesthesia evaluation data are extracted as verification samples of the anesthesia evaluation decision tree.
The embodiment of the invention provides a method for performing post pruning on the branch variables according to the verification sample to obtain a final anesthesia evaluation decision tree, which comprises the following steps:
(1.1) acquiring the anesthesia evaluation result error of a single node through a positive space distribution table by adopting a confidence interval method, and acquiring the anesthesia evaluation result errors of all child nodes under the father node aiming at the father node of the single node;
(1.2) obtaining weighted values of the anesthesia evaluation result errors of all the child nodes, and if the weighted values are larger than the anesthesia evaluation result error of the father node and the anesthesia evaluation result error of a single node is the minimum value, pruning and removing all the child nodes under the father node.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications, equivalent changes and modifications made to the above embodiment according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.
Claims (10)
1. A big data based laboratory anesthesia information processing system, characterized in that, the big data based laboratory anesthesia information processing system comprises:
the electrocardiogram acquisition module is connected with the central control module and is used for acquiring mouse electrocardiogram data through medical equipment;
the blood pressure acquisition module is connected with the central control module and is used for acquiring blood pressure data of the mice through a blood pressure meter;
the blood oxygen saturation acquisition module is connected with the central control module and is used for detecting the blood oxygen saturation data of the mouse through the blood oxygen detector;
the respiratory parameter acquisition module is connected with the central control module and is used for monitoring the respiratory frequency of the mouse by using a respiratory monitor;
the central control module is connected with the electrocardiogram acquisition module, the blood pressure acquisition module, the oxyhemoglobin saturation acquisition module, the respiratory parameter acquisition module, the anesthesia selection module, the calculation module, the oxygen supply module, the temperature regulation module, the anesthesia adjustment module, the anesthesia state judgment module, the anesthesia evaluation construction module, the time calculation module, the recording module, the big data processing module, the comparison analysis module and the display module, and is used for controlling the normal work of each module through a host;
the anesthesia selection module is connected with the central control module and comprises an anesthesia mode selection unit, an anesthesia medicine selection unit, a medicine concentration selection unit and an anesthesia speed regulation unit; for selecting an anesthetic drug or an anesthetic mode;
the calculation module is connected with the central control module and is used for calculating the dosage of the anesthetic drugs based on the selected anesthetic drugs and the mode through a calculation program;
the oxygen supply module is connected with the central control module and is used for supplying oxygen to the mouse through oxygen supply equipment;
the temperature control module is connected with the central control module and is used for changing the temperature of the mouse by using temperature control equipment;
the anesthesia module is connected with the central control module and used for anesthetizing the mouse through the anesthesia machine;
the anesthesia adjusting module is connected with the central control module and is used for adjusting the concentration of the anesthetic drug and the anesthesia rate;
the anesthesia state judgment module is connected with the central control module and used for judging the anesthesia state of the mouse through a judgment program;
the anesthesia evaluation construction module is connected with the central control module and used for constructing an anesthesia evaluation decision tree through a construction program;
the time calculation module is connected with the central control module and used for calculating the anesthetized time;
the recording module is connected with the central control module and is used for recording the acquired mouse-related electrocardiogram, blood pressure, blood oxygen saturation, respiratory rate, anesthesia state and other related data in real time;
the big data processing module is connected with the central control module and used for processing the anesthesia information through a big data processing program and displaying the anesthesia information in a chart or other forms;
the comparison analysis module is connected with the central control module and is used for performing comparison analysis on the acquired data and other standard data or data before and after adjustment;
and the display module is connected with the central control module and is used for displaying the mouse electrocardiogram, the blood pressure, the blood oxygen saturation, the respiratory rate, the anesthesia state and other related data in a table form through the display.
2. The big data-based laboratory anesthesia information processing system of claim 1, wherein the anesthesia selection module comprises:
the device comprises an anesthesia mode selection unit, an anesthesia medicine selection unit, a medicine concentration selection unit and an anesthesia speed regulation unit;
an anesthesia mode selection unit: for autonomously selecting an anesthesia mode;
an anesthetic selection unit: for selecting an anesthetic drug;
drug concentration selection unit: for setting the concentration based on the selected anesthetic drug;
an anesthesia speed adjusting unit: for setting the speed of anesthesia.
3. The big-data-based laboratory anesthesia information processing method applied to the big-data-based laboratory anesthesia information processing system is characterized by comprising the following steps of:
step one, acquiring electrocardiogram data of a mouse through medical equipment; collecting blood pressure data of the mouse through a blood pressure meter; detecting blood oxygen saturation data of the mouse through a blood oxygen detector; monitoring the respiratory frequency of the mouse by using a respiratory monitor;
automatically setting an anesthesia mode, an anesthesia medicine concentration and an anesthesia speed; calculating the dosage of the anesthetic based on the selected anesthetic and the mode through a calculation program;
thirdly, supplying oxygen to the mouse through oxygen supply equipment; changing the temperature of the mouse by using a temperature control device; anaesthetizing the mouse by an anaesthesia machine;
regulating the concentration and the anesthesia rate of the anesthetic; judging the anesthesia state of the mouse through a judgment program; constructing an anesthesia evaluation decision tree through a construction program;
step five, calculating the anesthesia time; recording the acquired relevant electrocardiogram, blood pressure, blood oxygen saturation, respiratory frequency, anesthesia state and other relevant data of the mouse in real time; meanwhile, the anesthesia information is processed through a big data processing program and displayed in a chart or other forms;
comparing and analyzing the acquired data with other standard data or data before and after adjustment; the electrocardiogram of the mouse, the blood pressure, the blood oxygen saturation, the respiratory rate, the anesthesia state, the result of the comparative analysis and other relevant data are displayed in a tabular form through a display.
4. The big-data-based laboratory anesthesia information processing method of claim 3, wherein in step two, the calculation method is as follows:
a. measuring by a pressure gauge to obtain the concentration of anesthetic gas, measuring by a flowmeter to obtain the flow of the mixed gas, and measuring to obtain time;
b. calculating the dosage of the gaseous anesthetic according to the concentration of the anesthetic gas, the flow rate of the mixed gas and the time;
c. and according to the type of the anesthetic gas, the controller calculates the liquid anesthetic dosage corresponding to the gas anesthetic dosage according to a gas molar volume calculation method.
5. The laboratory anesthesia information processing method based on big data as claimed in claim 3, wherein in step four, the anesthesia assessment construction method is as follows:
(1) acquiring a training sample of an anesthesia evaluation decision tree through a construction program, and determining a branch variable of the anesthesia evaluation decision tree according to an information gain rate of the training sample;
(2) obtaining a verification sample of the anesthesia evaluation decision tree, and performing post pruning on the branch variables according to the verification sample to obtain a final anesthesia evaluation decision tree;
(3) the final anesthesia evaluation decision tree is used to output an anesthesia level output variable.
6. The big-data-based laboratory anesthesia information processing method of claim 5, wherein the obtaining of training samples of the anesthesia evaluation decision tree comprises:
and extracting 70% of the data in the big data of the anesthesia evaluation as a training sample of the anesthesia evaluation decision tree.
7. The big-data-based laboratory anesthesia information processing method of claim 5, wherein the branch variables of the anesthesia evaluation decision tree are determined according to the information gain rate of the training samples, and accordingly, the information gain rate of the training samples comprises:
wherein a is a life feature attribute; the Gain _ ratio is the information Gain rate of the training sample with the selected vital sign attribute a as the split attribute; d is a training sample of the anesthesia evaluation decision tree; gain is the information Gain of selecting the vital sign attribute a as the split attribute; IV is the information entropy of a; ent is the information entropy of D; di is dividing D according to the vital sign attribute a to generate V branch nodes, wherein the ith branch node comprises the number of training samples of an anesthesia evaluation decision tree taking a value as ai in D; pk is the proportion of the kth sample in D; and y is the number of types of samples in D.
8. The big-data based laboratory anesthesia information processing method of claim 5, wherein the obtaining of the validation sample of the anesthesia evaluation decision tree comprises:
extracting 30% of data in the anesthesia evaluation big data to be used as a verification sample of an anesthesia evaluation decision tree;
and post-pruning the branch variables according to the verification sample to obtain a final anesthesia evaluation decision tree, wherein the post-pruning comprises the following steps:
(1.1) acquiring the anesthesia evaluation result error of a single node through a positive space distribution table by adopting a confidence interval method, and acquiring the anesthesia evaluation result errors of all child nodes under the father node aiming at the father node of the single node;
(1.2) obtaining weighted values of the anesthesia evaluation result errors of all the child nodes, and if the weighted values are larger than the anesthesia evaluation result error of the father node and the anesthesia evaluation result error of a single node is the minimum value, pruning and removing all the child nodes under the father node.
9. A computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface for implementing a big data based laboratory anesthesia information processing method according to any of claims 3-8, when executed on an electronic device.
10. A computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the big data based laboratory anesthesia information processing method of any of claims 3-8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116312958A (en) * | 2023-05-24 | 2023-06-23 | 成都市龙泉驿区中医医院 | Anesthesia risk early warning system, emergency management system and method |
| CN116531628A (en) * | 2023-06-13 | 2023-08-04 | 大庆油田总医院 | Intelligent anesthesia breathing safety monitoring system and method |
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2021
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116312958A (en) * | 2023-05-24 | 2023-06-23 | 成都市龙泉驿区中医医院 | Anesthesia risk early warning system, emergency management system and method |
| CN116312958B (en) * | 2023-05-24 | 2023-09-15 | 成都市龙泉驿区中医医院 | Anesthesia risk early warning system, emergency management system and method |
| CN116531628A (en) * | 2023-06-13 | 2023-08-04 | 大庆油田总医院 | Intelligent anesthesia breathing safety monitoring system and method |
| CN116531628B (en) * | 2023-06-13 | 2023-12-29 | 大庆油田总医院 | Intelligent anesthesia breathing safety monitoring system and method |
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