CN115394386A - Automatic acquisition method and system for clinical test data - Google Patents
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Abstract
The invention discloses an automatic acquisition method and system of clinical test data, which belong to the clinical field and are used for solving the problems that the clinical test data are generally acquired manually and are not judged in combination with the real-time situation of a clinical tester.
Description
Technical Field
The invention belongs to the clinical field, relates to a test data acquisition technology, and particularly relates to an automatic acquisition method and system of clinical test data.
Background
Clinical trials refer to any systematic study of drugs in humans (patients or healthy volunteers) to confirm or reveal the effects, adverse effects and/or absorption, distribution, metabolism and excretion of the test drugs in order to determine the efficacy and safety of the test drugs. Clinical trials are generally divided into phase I, II, III, IV and EAP clinical trials.
In the prior art, clinical test data are usually acquired manually during clinical test, the acquisition of the clinical test data is prone to deviation, and meanwhile, the clinical test result is not judged in combination with the real-time situation of a clinical tester.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an automatic acquisition method and system for clinical test data.
The technical problem to be solved by the invention is as follows:
the automatic collection of clinical trial data and the real-time situation of clinical testers are combined to realize the intelligent judgment of clinical trial results.
The purpose of the invention can be realized by the following technical scheme:
an automatic acquisition system of clinical test data comprises a user terminal, a data acquisition module, a characteristic analysis module, a grade setting module, an effect comparison module, a grade determination module, a clinical test module and a server, wherein the user terminal is used for clinical test personnel to input clinical test data of a clinical test body and send the clinical test data to the server; the data acquisition module is used for acquiring pre-test sign data of a clinical test body and sending the pre-test sign data to the server, and the server sends the pre-test sign data to the feature analysis module; the characteristic analysis module is used for carrying out characteristic analysis on the clinical test body before the test to obtain a characteristic deviation value of the clinical test body and feeding the characteristic deviation value back to the server, and the server sends the characteristic deviation value of the clinical test body to the grade setting module; the characteristic analysis module also sends the age set corresponding to the clinical trial body to a server, and the server sends the age set corresponding to the clinical trial body to the clinical trial module;
the grade setting module is used for setting the grade of the characteristic deviation value of the clinical test body before clinical test, the grade of the characteristic deviation value of the clinical test body before test is obtained and fed back to the server, and the server sends the grade of the characteristic deviation value of the clinical test body before test to the effect comparison module;
the clinical trial module sets corresponding trial doses for different clinical trial bodies according to the age set; the data acquisition module is used for acquiring post-test physical sign data of a clinical test body and sending the post-test physical sign data to the server, and the server sends the post-test physical sign data to the grade determination module; the grade determining module is used for determining the grade of the physical characteristics of the clinical trial body after the trial, obtaining the characteristic deviation grade after the trial of the clinical trial body and feeding the characteristic deviation grade back to the server, and the server sends the characteristic deviation grade after the trial of the clinical trial body to the effect comparing module;
the effect comparison module compares characteristic deviation grades before and after a clinical test body test to obtain a test micro-effect signal, a test effective signal or a test invalid signal, and feeds the test micro-effect signal, the test effective signal or the test invalid signal back to the server, the server sends the test micro-effect signal, the test effective signal or the test invalid signal to the user terminal, and clinical testers at the user terminal judge test results of the clinical test according to the test micro-effect signal, the test effective signal or the test invalid signal.
Further, the clinical trial data are the number, age and trial dose per clinical trial;
the pre-test characteristic data are the body weight, waist circumference and leg circumference before the clinical test body test;
the post-test body characteristics data are body weight, waist circumference and leg circumference after the clinical test body test.
Further, the analysis process of the feature analysis module is specifically as follows:
acquiring the age of a clinical test subject, dividing the clinical test subject into corresponding age sets according to the age, and setting a corresponding age coefficient for the clinical test subject by combining the age sets;
wherein the age sets include a first age set, a second age set, and a third age set;
then obtaining the weight value, the leg circumference value and the waist circumference value of the clinical test subjects in different age groups and the standard physical sign data corresponding to the clinical test subjects in different age groups to obtain a standard weight value, a standard leg circumference value and a standard waist circumference value;
and calculating the characteristic deviation value of the clinical test body before test.
Further, the setting process of the level setting module is specifically as follows:
if the characteristic deviation value before the clinical trial body is less than the first characteristic deviation threshold value, the characteristic deviation grade before the clinical trial body is a third deviation grade;
if the characteristic deviation value before the clinical test body is tested is greater than or equal to the first characteristic deviation threshold and is smaller than the second characteristic deviation threshold, the characteristic deviation grade before the clinical test body is tested is a second deviation grade;
and if the characteristic deviation value before the clinical test body is tested is larger than or equal to the second characteristic deviation threshold value, the characteristic deviation grade before the clinical test body is tested is the first deviation grade.
Further, the first characteristic deviation threshold and the second characteristic deviation threshold are positive integers with fixed values, and the value of the first characteristic deviation threshold is smaller than that of the second characteristic deviation threshold;
the level of the third deviation level is less than the level of the second deviation level, which is less than the level of the first deviation level.
Further, the working process of the clinical trial module specifically comprises:
if the age set of the clinical trial is the first age set, the trial dose of the clinical trial is the first trial dose;
if the age set of the clinical trial is the second age set, the trial dose of the clinical trial is the third trial dose;
if the age set of the clinical trial is the third age set, the trial dose of the clinical trial is the second trial dose.
Further, the value of the first trial dose is less than the value of the second trial dose, which is less than the value of the third trial dose.
Further, the working process of the level determination module is specifically as follows:
acquiring the weight value, the leg circumference value and the waist circumference value of clinical test subjects of different age sets after the test;
then obtaining expected physical sign data corresponding to clinical test subjects of different age sets to obtain an expected weight value, an expected leg circumference value and an expected waist circumference value;
calculating the characteristic deviation value after the clinical test body is tested;
if the characteristic deviation value after the clinical test body test is smaller than the first characteristic deviation threshold value, the characteristic deviation grade after the clinical test body test is a third deviation grade;
if the characteristic deviation value after the clinical test body test is greater than or equal to the first characteristic deviation threshold value and smaller than the second characteristic deviation threshold value, the characteristic deviation grade after the clinical test body test is a second deviation grade;
and if the characteristic deviation value after the clinical test body test is greater than or equal to the second characteristic deviation threshold value, the characteristic deviation grade after the clinical test body test is the first deviation grade.
Further, the comparison process of the effect comparison module specifically comprises the following steps:
if the characteristic deviation grade after the clinical test body test and the characteristic deviation grade after the clinical test body test are the same, generating a test micro-effect signal;
if the characteristic deviation grade after the clinical test body test is smaller than the characteristic deviation grade after the clinical test body test and is the same characteristic deviation grade, generating a test effective signal;
and if the characteristic deviation grade after the clinical test body test is larger than the characteristic deviation grade after the clinical test body test and is the same characteristic deviation grade, generating a test invalid signal.
An automatic acquisition method of an automatic acquisition system of clinical trial data is characterized by comprising the following steps:
step S101, inputting clinical test data of a clinical test subject by a user terminal, and acquiring pre-test physical sign data of the clinical test subject by a data acquisition module and sending the pre-test physical sign data to a characteristic analysis module;
step S102, the characteristic analysis module performs characteristic analysis on the clinical test body before the test to obtain a characteristic deviation value of the clinical test body and sends the characteristic deviation value to the grade setting module;
step S103, setting the grade of the characteristic deviation value of the clinical trial body before clinical trial by the grade setting module to obtain the grade of the characteristic deviation value of the clinical trial body before the clinical trial and sending the grade of the characteristic deviation value to the effect comparison module;
step S104, sending the age set corresponding to the clinical trial body to a clinical trial module, setting corresponding trial doses for different clinical trial bodies by the clinical trial module according to the age set, and collecting post-trial physical sign data of the clinical trial body by the data collection module and sending the post-trial physical sign data to the grade determination module;
step S105, the grade determining module determines the grade of the physical characteristics of the clinical trial body after the trial, obtains the characteristic deviation grade of the clinical trial body after the trial and sends the characteristic deviation grade to the effect comparing module;
and S106, comparing the characteristic deviation grades before and after the clinical test by the effect comparison module to generate a test micro-effect signal, a test effective signal or a test ineffective signal, and sending the test micro-effect signal, the test effective signal or the test ineffective signal to the user terminal.
Compared with the prior art, the invention has the beneficial effects that:
the invention carries out characteristic analysis on a clinical trial body before a trial through a characteristic analysis module to obtain a characteristic deviation value of the clinical trial body, the characteristic deviation value is sent to a grade setting module, the grade setting module sets the grade of the characteristic deviation value of the clinical trial body before the clinical trial body, the grade of the characteristic deviation value of the clinical trial body before the trial is sent to an effect comparison module, meanwhile, an age set corresponding to the clinical trial body is sent to the clinical trial module, the clinical trial module sets corresponding trial dose for different clinical trial bodies according to the age set, the grade determination module determines the grade of the body characteristic of the clinical trial body after the trial, the obtained characteristic deviation grade of the clinical trial body after the trial is sent to the effect comparison module, and the effect comparison module compares the characteristic deviation grades before and after the clinical trial body trial to generate a trial micro-effect signal, a trial effective signal or a trial ineffective signal.
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In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is an overall system block diagram of the present invention;
fig. 2 is a flow chart of the operation of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In an embodiment, please refer to fig. 1, which provides an automatic acquisition system of clinical trial data, including a user terminal, a data acquisition module, a feature analysis module, a grade setting module, an effect comparison module, a grade determination module, a clinical trial module and a server;
in this example, the clinical trial data is specifically a clinical trial of a weight-loss drug;
in specific implementation, the server is connected with a user terminal, and the user terminal is used for clinical trial personnel to input clinical trial data of a clinical trial body and send the clinical trial data to the server;
specifically, the clinical trial data includes the number, age, and trial dose of each clinical trial;
specifically, the clinical trial bodies are various experimental animals which can be used for clinical trials within an allowable range;
the data acquisition module is used for acquiring pre-test sign data of a clinical test body and sending the pre-test sign data to the server, and the server sends the pre-test sign data to the feature analysis module;
specifically, the data acquisition module can be various clinical measurement medical devices, such as a medical weight detector and the like;
specifically, the pre-test characteristic data includes body weight, waist circumference, leg circumference and the like before the clinical test body test;
the characteristic analysis module is used for carrying out characteristic analysis on a clinical trial body before a trial, and the analysis process is as follows:
the method comprises the following steps: labeling clinical trial bodies as u, u =1,2, … …, z, z being a positive integer; acquiring the age of a clinical trial body, and dividing the clinical trial body into corresponding age sets according to the age;
step two: setting a corresponding age coefficient NXu for the clinical trial according to the age set;
the age group comprises a first age group, a second age group and a third age group, and can be divided according to the age of the whole Linchuan test subject;
for example: the first age set is [0,5 ], the age coefficient is X1, if the second age set is [5, 10 ], the age coefficient is X2, if the third age set is [10, 15], the age coefficient is X3, X1 < X2 < X3;
step three: acquiring the weight value, the leg circumference value and the waist circumference value of the clinical test subject at different ages, and respectively marking the weight value, the leg circumference value and the waist circumference as QTZu, QTWu and QYWu;
step four: acquiring standard physical sign data corresponding to clinical test subjects of different age sets to obtain a standard body weight value BTZu, a standard leg circumference value BTWu and a standard waist circumference value BYWu;
step five: by the formula QTPu = (| BTZu-QTZu |. Times a1+ | BTWu-QTWu |. Times a2+ | BYWu-QYWu |. Times a 2) NXu Calculating to obtain a characteristic deviation value QTPu before the clinical trial; in the formula, a1, a2 and a3 are all weight coefficients with fixed numerical values, and the values of a1, a2 and a3 are all larger than zero;
the characteristic analysis module feeds back the characteristic deviation value TPu of the clinical trial body to the server, and the server sends the characteristic deviation value TPu of the clinical trial body to the grade setting module;
the characteristic analysis module also sends the age set corresponding to the clinical trial body to a server, and the server sends the age set corresponding to the clinical trial body to the clinical trial module;
the grade setting module is used for setting the grade of the characteristic deviation value of the clinical trial body before clinical trial, and the setting process is as follows:
step S1: if the characteristic deviation value before the clinical trial body is less than the first characteristic deviation threshold value, the characteristic deviation grade before the clinical trial body is a third deviation grade;
step S2: if the characteristic deviation value before the clinical test body is tested is greater than or equal to the first characteristic deviation threshold and is smaller than the second characteristic deviation threshold, the characteristic deviation grade before the clinical test body is tested is a second deviation grade;
and step S3: if the characteristic deviation value before the clinical test body is tested is greater than or equal to the second characteristic deviation threshold value, the characteristic deviation grade before the clinical test body is tested is a first deviation grade;
the first characteristic deviation threshold and the second characteristic deviation threshold are positive integers with fixed numerical values, and the value of the first characteristic deviation threshold is smaller than that of the second characteristic deviation threshold;
as can be seen from the above, the level of the third deviation level is less than the level of the second deviation level, which is less than the level of the first deviation level;
the grade setting module feeds the grade of the characteristic deviation value of the clinical test subject before the test back to the server, and the server sends the grade of the characteristic deviation value of the clinical test subject before the test to the effect comparison module;
the clinical trial module sets up corresponding experimental dose for different clinical trial bodies according to age set, specifically does:
if the age set of the clinical trial is the first age set, the trial dose of the clinical trial is the first trial dose;
if the age set of the clinical trial is the second age set, the trial dose of the clinical trial is the third trial dose;
if the age set of the clinical trial is the third age set, the trial dose of the clinical trial is the second trial dose;
wherein the value of the first test dose is less than the value of the second test dose, which is less than the value of the third test dose;
different clinical testers perform clinical tests according to test doses, the data acquisition module is used for acquiring post-test physical sign data of the clinical testers and sending the post-test physical sign data to the server, and the server sends the post-test physical sign data to the grade determination module;
specifically, the physical characteristics data after the test are the body weight, waist circumference, leg circumference and the like after the clinical test;
the grade determining module is used for determining the grade of the physical characteristics of the clinical trial body after the trial, and the working process is as follows:
step P1: acquiring weight values, leg circumference values and waist circumference values after clinical test of different age sets, and respectively marking the weight values, the leg circumference values and the waist circumferences as HTZu, HTWu and HYWu;
step P2: obtaining expected physical sign data corresponding to clinical test subjects of different age sets to obtain an expected weight value YTZu, an expected leg circumference value YTWu and an expected waist circumference value YYWu;
step P3: by the formula HTPu = (| YTZu-HTZu |. Times b1+ | YTWu-HTWu |. Times b2+ | YYYWu-HYWu |. Times b 3) NXu Calculating to obtain a characteristic deviation value HTPu after the clinical trial body is tested; in the formula, b1, b2 and b3 are all weight coefficients with fixed numerical values, and the values of b1, b2 and b3 are all larger than zero;
and step P4: if the characteristic deviation value after the clinical test body test is smaller than the first characteristic deviation threshold value, the characteristic deviation grade after the clinical test body test is a third deviation grade;
step P5: if the characteristic deviation value after the clinical test body test is greater than or equal to the first characteristic deviation threshold value and smaller than the second characteristic deviation threshold value, the characteristic deviation grade after the clinical test body test is a second deviation grade;
step P6: if the characteristic deviation value after the clinical test body test is greater than or equal to the second characteristic deviation threshold value, the characteristic deviation grade after the clinical test body test is a first deviation grade;
the grade determining module feeds back the characteristic deviation grade after the clinical test body test to the server, and the server sends the characteristic deviation grade after the clinical test body test to the effect comparison module;
the effect comparison module compares the characteristic deviation grades before and after the clinical trial, and the comparison process specifically comprises the following steps:
if the characteristic deviation grade after the clinical test body test and the characteristic deviation grade after the clinical test body test are the same, generating a test micro-effect signal;
if the characteristic deviation grade after the clinical test body test is smaller than the characteristic deviation grade after the clinical test body test and is the same characteristic deviation grade, generating a test effective signal;
if the characteristic deviation grade after the clinical test body test is larger than the characteristic deviation grade after the clinical test body test and is the same characteristic deviation grade, generating a test invalid signal;
the effect comparison module feeds back a test micro-effect signal, a test effective signal or a test ineffective signal to the server, the server sends the test micro-effect signal, the test effective signal or the test ineffective signal to the user terminal, and clinical testers at the user terminal judge test results of clinical tests according to the test micro-effect signal, the test effective signal or the test ineffective signal;
in another embodiment, please refer to fig. 2, which shows a method for automatically collecting clinical trial data, which includes the following steps:
step S101, a clinical trial worker inputs clinical trial data of a clinical trial body through a user terminal and sends the clinical trial data to a server, a data acquisition module acquires pre-trial sign data of the clinical trial body and sends the pre-trial sign data to the server, and the server sends the pre-trial sign data to a feature analysis module;
step S102, performing characteristic analysis on the clinical test body before the test through a characteristic analysis module, marking the clinical test body as u, obtaining the age of the clinical test body, dividing the clinical test body into corresponding age sets according to the age, setting a corresponding age coefficient NXu for the clinical test body according to the age sets, then obtaining the weight value, the leg circumference value and the waist circumference value of the clinical test body in different age sets, marking the weight value, the leg circumference value and the waist circumference as QTZu, QTWu and QYWu respectively, finally obtaining the standard physical sign data corresponding to the clinical test body in different age sets, obtaining the standard weight value BTZu, the standard leg circumference value BTWu and the standard waist circumference value BYWu, and obtaining the standard weight value BTZu, the standard leg circumference value BTWu and the standard waist circumference value BYWu through a formula QTPu = (| BTZu-QTZu | a1 | BTWu-Wu | a2 | a + | BYWu-a | A2 | BYWu | a2 | A2 |) NXu Calculating to obtain a characteristic deviation value QTPu before the clinical trial, feeding back the characteristic deviation value TPu of the clinical trial to a server by a characteristic analysis module, and sending the characteristic deviation value TPu of the clinical trial to a level setting module by the server;
step S103, a clinical test coefficient of the clinical test subject before clinical test is set through a grade setting module, if a characteristic deviation value of the clinical test subject before test is smaller than a first characteristic deviation threshold value, the characteristic deviation grade of the clinical test subject before test is a third deviation grade, if the characteristic deviation value of the clinical test subject before test is larger than or equal to the first characteristic deviation threshold value and smaller than a second characteristic deviation threshold value, the characteristic deviation grade of the clinical test subject before test is a second deviation grade, if the characteristic deviation value of the clinical test subject before test is larger than or equal to the second characteristic deviation threshold value, the characteristic deviation grade of the clinical test subject before test is a first deviation grade, the grade setting module feeds the characteristic deviation value grade of the clinical test subject before test back to a server, the server sends the characteristic deviation value grade of the clinical test subject before test to an effect comparison module, meanwhile, the characteristic analysis module also sends an age set corresponding to the clinical test subject to the server, and the server sends the age set corresponding to the clinical test subject;
step S104, the clinical trial module sets corresponding trial doses for different clinical trial bodies according to the age sets, if the age set of the clinical trial bodies is a first age set, the trial dose of the clinical trial bodies is a first trial dose, if the age set of the clinical trial bodies is a second age set, the trial dose of the clinical trial bodies is a third trial dose, if the age set of the clinical trial bodies is a third age set, the trial dose of the clinical trial bodies is a second trial dose, the different clinical trial bodies perform clinical trials according to the trial doses, the data acquisition module acquires post-trial physical sign data of the clinical trial bodies and sends the post-trial physical sign data to the server, and the server sends the post-trial physical sign data to the grade determination module;
step S105, determining the grade of the physical characteristics of the clinical trial body after the test through a grade determining module, obtaining the weight value, the leg circumference value and the waist circumference value of the clinical trial body after the test of different age sets, respectively marking the weight value, the leg circumference value and the waist circumference as HTZu, HTWu and HYWu, obtaining expected physical sign data corresponding to the clinical trial body of different age sets, obtaining an expected weight value YTZu, an expected leg circumference value YTWu and an expected waist circumference value YYYWu, and obtaining the expected body weight value YTZu, the expected leg circumference value YTWu and the expected waist circumference value YYYWu through a formula HTPu = (| YTZu-HTZu |. Xb 1+ | YTWu-HTWu |. Xb 2+ | YWu-HYWu | xb 3) NXu Calculating to obtain a characteristic deviation value HTPu after the clinical test body test, wherein if the characteristic deviation value after the clinical test body test is smaller than a first characteristic deviation threshold value, the characteristic deviation grade after the clinical test body test is a third deviation grade, if the characteristic deviation value after the clinical test body test is greater than or equal to the first characteristic deviation threshold value and smaller than a second characteristic deviation threshold value, the characteristic deviation grade after the clinical test body test is a second deviation grade, if the characteristic deviation value after the clinical test body test is greater than or equal to the second characteristic deviation threshold value, the characteristic deviation grade after the clinical test body test is a first deviation grade, a grade determining module feeds the characteristic deviation grade after the clinical test body test back to a server, and the server sends the characteristic deviation grade after the clinical test body test to an effect comparison module;
step S106, the effect comparison module compares characteristic deviation grades before and after the clinical test body test, if the characteristic deviation grade after the clinical test body test and the characteristic deviation grade after the clinical test body test are the same characteristic deviation grade, a test micro-effect signal is generated, if the characteristic deviation grade after the clinical test body test is smaller than the characteristic deviation grade after the clinical test body test and is the same characteristic deviation grade, a test effective signal is generated, if the characteristic deviation grade after the clinical test body test is larger than the characteristic deviation grade after the clinical test body test and is the same characteristic deviation grade, a test invalid signal is generated, the effect comparison module feeds the test micro-effect signal, the test effective signal or the test invalid signal back to the server, the server sends the test micro-effect signal, the test effective signal or the test invalid signal to the user terminal, and clinical testers at the user terminal judge the test result of the clinical test according to the test micro-effect signal, the test effective signal or the test invalid signal.
The above formulas are all dimensionless values and calculated, the formula is a formula for obtaining the latest real situation by collecting a large amount of data and performing software simulation, the preset parameters in the formula are set by the technical personnel in the field according to the actual situation, the weight coefficient and the scale coefficient are specific values obtained by quantifying each parameter, so that the subsequent comparison is convenient, and the proportional relation between the parameters and the quantified values can be obtained as long as the proportional relation between the parameters and the quantified values is not influenced.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (10)
1. An automatic acquisition system of clinical test data is characterized by comprising a user terminal, a data acquisition module, a feature analysis module, a grade setting module, an effect comparison module, a grade determination module, a clinical test module and a server, wherein the user terminal is used for clinical test personnel to input clinical test data of a clinical test body and send the clinical test data to the server; the data acquisition module is used for acquiring pre-test sign data of a clinical test body and sending the pre-test sign data to the server, and the server sends the pre-test sign data to the feature analysis module; the characteristic analysis module is used for carrying out characteristic analysis on the clinical trial body before the trial to obtain a characteristic deviation value of the clinical trial body and feeding the characteristic deviation value back to the server, and the server sends the characteristic deviation value of the clinical trial body to the grade setting module; the characteristic analysis module also sends the age set corresponding to the clinical trial body to the server, and the server sends the age set corresponding to the clinical trial body to the clinical trial module;
the level setting module is used for setting the grade of the characteristic deviation value of the clinical test subject before clinical test, the grade of the obtained characteristic deviation value of the clinical test subject before clinical test is fed back to the server, and the server sends the grade of the characteristic deviation value of the clinical test subject before clinical test to the effect comparison module;
the clinical trial module sets corresponding trial doses for different clinical trial bodies according to the age set; the data acquisition module is used for acquiring post-test physical sign data of a clinical test body and sending the post-test physical sign data to the server, and the server sends the post-test physical sign data to the grade determination module; the grade determination module is used for determining the grade of the physical characteristics of the clinical test body after the test, the obtained characteristic deviation grade after the test of the clinical test body is fed back to the server, and the server sends the characteristic deviation grade after the test of the clinical test body to the effect comparison module;
the effect comparison module compares characteristic deviation grades before and after a clinical test body test to obtain a test micro-effect signal, a test effective signal or a test invalid signal, and feeds the test micro-effect signal, the test effective signal or the test invalid signal back to the server, the server sends the test micro-effect signal, the test effective signal or the test invalid signal to the user terminal, and clinical testers at the user terminal judge test results of the clinical test according to the test micro-effect signal, the test effective signal or the test invalid signal.
2. The system of claim 1, wherein the clinical trial data includes the number, age and trial dose of each clinical trial;
pre-test characteristic data are body weight, waist circumference and leg circumference before clinical test body test;
the post-test body characteristics data are body weight, waist circumference and leg circumference after the clinical test body test.
3. The system of claim 1, wherein the analysis process of the feature analysis module is as follows:
acquiring the age of a clinical test subject, dividing the clinical test subject into corresponding age sets according to the age, and setting a corresponding age coefficient for the clinical test subject by combining the age sets;
wherein the age sets include a first age set, a second age set, and a third age set;
then obtaining the weight value, the leg circumference value and the waist circumference value of the clinical test subjects in different age groups and the standard physical sign data corresponding to the clinical test subjects in different age groups to obtain a standard weight value, a standard leg circumference value and a standard waist circumference value;
and calculating the characteristic deviation value of the clinical test body before test.
4. The system of claim 1, wherein the setting process of the level setting module is as follows:
if the characteristic deviation value before the clinical test body is tested is smaller than the first characteristic deviation threshold, the characteristic deviation grade before the clinical test body is tested is a third deviation grade;
if the characteristic deviation value before the clinical test body is tested is greater than or equal to the first characteristic deviation threshold and is smaller than the second characteristic deviation threshold, the characteristic deviation grade before the clinical test body is tested is a second deviation grade;
and if the characteristic deviation value before the clinical test body is tested is larger than or equal to the second characteristic deviation threshold value, the characteristic deviation grade before the clinical test body is tested is the first deviation grade.
5. The system of claim 4, wherein the first characteristic deviation threshold and the second characteristic deviation threshold are positive integers of a fixed value, and the value of the first characteristic deviation threshold is less than the value of the second characteristic deviation threshold;
the level of the third deviation level is less than the level of the second deviation level, which is less than the level of the first deviation level.
6. The system for automatically acquiring clinical trial data according to claim 1, wherein the clinical trial module specifically operates as follows:
if the age set of the clinical trial is the first age set, the trial dose of the clinical trial is the first trial dose;
if the age set of the clinical trial is the second age set, the trial dose of the clinical trial is a third trial dose;
and if the age set of the clinical trial is the third age set, the trial dose of the clinical trial is the second trial dose.
7. An automated clinical trial data acquisition system according to claim 6, wherein the first trial dose has a value less than the second trial dose and the second trial dose has a value less than the third trial dose.
8. The system of claim 1, wherein the level determination module operates as follows:
acquiring the weight value, the leg circumference value and the waist circumference value of clinical test subjects of different age sets after the test;
then obtaining expected physical sign data corresponding to clinical test subjects of different age sets to obtain an expected weight value, an expected leg circumference value and an expected waist circumference value;
calculating the characteristic deviation value after the clinical test body is tested;
if the characteristic deviation value after the clinical test body test is smaller than the first characteristic deviation threshold value, the characteristic deviation grade after the clinical test body test is a third deviation grade;
if the characteristic deviation value after the clinical test body test is greater than or equal to the first characteristic deviation threshold value and smaller than the second characteristic deviation threshold value, the characteristic deviation grade after the clinical test body test is a second deviation grade;
and if the characteristic deviation value after the clinical test body test is greater than or equal to the second characteristic deviation threshold value, the characteristic deviation grade after the clinical test body test is the first deviation grade.
9. The system of claim 1, wherein the effect comparison module performs a comparison process as follows:
if the characteristic deviation grade after the clinical test body test and the characteristic deviation grade after the clinical test body test are the same, generating a test micro-effect signal;
if the characteristic deviation grade after the clinical test body test is smaller than the characteristic deviation grade after the clinical test body test and is the same characteristic deviation grade, generating a test effective signal;
and if the characteristic deviation grade after the clinical test body test is larger than the characteristic deviation grade after the clinical test body test and is the same characteristic deviation grade, generating a test invalid signal.
10. A method for automatic acquisition of clinical trial data according to any of claims 1 to 9, wherein the method for automatic acquisition is specifically as follows:
step S101, inputting clinical test data of a clinical test subject by a user terminal, and acquiring pre-test physical sign data of the clinical test subject by a data acquisition module and sending the pre-test physical sign data to a characteristic analysis module;
step S102, the characteristic analysis module carries out characteristic analysis on a clinical test body before the test to obtain a characteristic deviation value of the clinical test body and sends the characteristic deviation value to the grade setting module;
step S103, setting the grade of the characteristic deviation value of the clinical trial body before clinical trial by the grade setting module to obtain the grade of the characteristic deviation value of the clinical trial body before the clinical trial and sending the grade of the characteristic deviation value to the effect comparison module;
step S104, sending the age set corresponding to the clinical trial body to a clinical trial module, setting corresponding trial doses for different clinical trial bodies by the clinical trial module according to the age set, and collecting post-trial physical sign data of the clinical trial body by the data collection module and sending the post-trial physical sign data to the grade determination module;
step S105, the grade determining module determines the grade of the physical characteristics of the clinical test body after the test, obtains the characteristic deviation grade of the clinical test body after the test and sends the characteristic deviation grade to the effect comparison module;
and S106, comparing the characteristic deviation grades before and after the clinical test by the effect comparison module to generate a test micro-effect signal, a test effective signal or a test ineffective signal, and sending the test micro-effect signal, the test effective signal or the test ineffective signal to the user terminal.
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| CN115312152A (en) * | 2022-09-01 | 2022-11-08 | 北京舒曼德医药科技开发有限公司 | Medicine early warning system based on clinical test platform |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200105380A1 (en) * | 2018-10-02 | 2020-04-02 | Origent Data Sciences, Inc. | Systems and methods for designing clinical trials |
| US20200168304A1 (en) * | 2018-01-18 | 2020-05-28 | MANA Consulting LLC d/b/a MANA RBM | Clinical trial oversight and identification of errors in clinical trial procedure |
| CN112599222A (en) * | 2020-12-28 | 2021-04-02 | 重庆市汇人健康管理有限责任公司 | Diet and exercise guidance method for people with abnormal body weight |
| US20220059197A1 (en) * | 2020-08-19 | 2022-02-24 | Halo Health Systems, Inc | System and method for monitoring compliance and participant safety for clinical trials |
-
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200168304A1 (en) * | 2018-01-18 | 2020-05-28 | MANA Consulting LLC d/b/a MANA RBM | Clinical trial oversight and identification of errors in clinical trial procedure |
| US20200105380A1 (en) * | 2018-10-02 | 2020-04-02 | Origent Data Sciences, Inc. | Systems and methods for designing clinical trials |
| US20220059197A1 (en) * | 2020-08-19 | 2022-02-24 | Halo Health Systems, Inc | System and method for monitoring compliance and participant safety for clinical trials |
| CN112599222A (en) * | 2020-12-28 | 2021-04-02 | 重庆市汇人健康管理有限责任公司 | Diet and exercise guidance method for people with abnormal body weight |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115312152A (en) * | 2022-09-01 | 2022-11-08 | 北京舒曼德医药科技开发有限公司 | Medicine early warning system based on clinical test platform |
| CN115312152B (en) * | 2022-09-01 | 2023-06-20 | 北京舒曼德医药科技开发有限公司 | Medicine early warning system based on clinical test platform |
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