Laboratory data and index monitoring analysis method and decision support management system

Abstract

The invention relates to the technical field of experimental data monitoring, and discloses a laboratory data and index monitoring analysis method and a decision support management system, and the system comprises a sample tracking module, an index definition module, a data collection module, a data analysis module, a performance monitoring module, a data storage module, a trend analysis module, and a real-time early warning module. Wherein the data collection module is used for automatically collecting experimental data measured by an experimental instrument at regular time; and the data analysis module is used for carrying out visual analysis on the data collected each time, carrying out correlation analysis on the data collected each time and the data stored in the historical logs, and carrying out regression analysis on the data collected each time and the sample variables. According to the method, early warning is carried out in the synthesis process of the sample by comprehensively evaluating the measurement data of the experimental data, the actual data of the sample and the pre-estimated data of the system, so that the normal development of the sample data collected in real time is ensured, and the pollution of the experimental sample caused by untimely monitoring is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of laboratory data monitoring, and in particular to a laboratory data and index monitoring and analysis method and a decision support management system. Background Art

[0002] In the process of biological, chemical and medical experiments, it is necessary to monitor the data changes of samples in real time to ensure the concentration changes, moisture and temperature status in the sample container, and ensure that the changes of samples are within the predictable range. However, since the staff are not always online, they only pay attention to the culture dishes for one-fifth of the time every day, which makes the solvent in the sample container easily contaminated.

[0003] The existing patent discloses a laboratory sample cultivation data monitoring system and method (publication number CN117057594A), which specifically includes: a cultivation task management module, a cultivation data entry module, a reminder module and a data monitoring module. The cultivation task management module sends a first reminder signal to the reminder module at a preset interval based on the bacterial strain information of each cultivation task. After receiving the first reminder signal, the reminder module reminds the staff to enter the cultivation data of the corresponding cultivation task. In the technology disclosed in this patent, there is a lack of specific quantitative monitoring indicators for monitoring the amount of cell changes in the sample container, which results in inaccurate monitoring and analysis of cells in the culture dish, making it difficult to evaluate the sample performance indicators and whether they exceed the threshold. Summary of the invention

[0004] The main technical problem solved by the present invention is to provide a laboratory data and index monitoring and analysis method and a decision support management system, which solves the problems in the above-mentioned background technology.

[0005] To solve the above technical problems, according to one aspect of the present invention, more specifically, a laboratory data and indicator monitoring and analysis method and a decision support management system, including a sample tracking module, an indicator definition module, a data collection module, a data analysis module, a performance monitoring module, a data storage module, a trend analysis module, and a real-time warning module;

[0006] Wherein, the data collection module is used to automatically collect the experimental data measured by the experimental instrument at a regular time;

[0007] The data analysis module is used to perform visual analysis on the data collected each time, perform correlation analysis with the data stored in the historical log, and perform regression analysis with the sample variables;

[0008] The performance monitoring module is used to monitor the accuracy, precision and sensitivity of the collected experimental data;

[0009] The real-time early warning module issues experimental data early warning to the laboratory administrator based on the feedback from the quality monitoring module.

[0010] Furthermore, the performance monitoring module also includes a quality monitoring module;

[0011] The quality monitoring module is used to perform multiple measurements on the same experimental sample data, calculate the standard deviation and coefficient of variation of the results to evaluate the precision.

[0012] Furthermore, the performance monitoring module determines whether the performance indicators of the experimental data exceed the threshold by measuring the accuracy, precision and sensitivity of the sample experimental data, including:

[0013]

[0014] In the formula, C represents the performance index coefficient of the experimental data collected by the data collection module, h represents the accuracy coefficient of the experimental data detected by the data collection module, g represents the tightness coefficient of the experimental data detected by the data collection module, and k represents the sensitivity coefficient of the experimental data detected by the data collection module;

[0015] When C≤65%, it means that the monitored experimental performance index does not exceed the set threshold;

[0016] When C>65%, it means that the monitored experimental performance index exceeds the set threshold, and at this time it needs to be fed back to the real-time warning module for warning.

[0017] Furthermore, the accuracy of the experimental data is determined by the difference between the experimental measurement value and the true value, and the difference between the experimental measurement result and the predicted value.

[0018]

[0019] Where h represents the accuracy coefficient of the experimental data detected by the data collection module, a represents the difference between the experimental measurement average and the true value, and s represents the difference between the experimental measurement average and the predicted value.

[0020] Furthermore, the precision of the experimental data is determined by the ratio of the error to the true value and the degree of dispersion of the measured value distribution:

[0021]

[0022] Where g represents the tightness coefficient of the experimental data detected by the data collection module, m represents the ratio of the error to the true value, and r represents the degree of dispersion of the measured value distribution.

[0023] Furthermore, the sensitivity of the experimental data is further determined by testing the ratio of the average data detected to the true value and the degree of dispersion of the measured value distribution:

[0024]

[0025] Where k represents the sensitivity coefficient of the experimental data detected by the data collection module, p represents the ratio of the number of measured values ​​to the true values, and r represents the degree of dispersion of the measured value distribution.

[0026] Furthermore, the sample tracking module is used to regularly check the deviation between the analysis results and the expected values ​​of the quality control samples with known concentrations added during routine analysis.

[0027] Furthermore, the indicator definition module is used to quantitatively evaluate the performance of samples and experimental results.

[0028] Furthermore, the data storage module is used to store the collected experimental data, as well as data analysis results and warning status information.

[0029] Beneficial effects:

[0030] 1. The present invention provides early warning during the sample synthesis process through comprehensive evaluation of the measured data of the experimental data, the actual data of the sample and the system estimated data, thereby ensuring that the sample data collected in real time can develop normally and avoiding the contamination of the experimental samples due to untimely monitoring.

[0031] 2. The present invention detects the accuracy, precision and sensitivity of sample experimental data through the performance monitoring module, and measures the same experimental sample data multiple times through the quality monitoring module, which can improve the accuracy of the system's measurement of experimental samples.

[0032] 3. The present invention can set different thresholds for different experimental samples by changing the warning threshold. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0034] In order to make the technical solution of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] Example 1

[0036] like Figure 1As shown, according to one aspect of the present invention, a laboratory data and indicator monitoring and analysis method and a decision support management system are provided, including a sample tracking module, an indicator definition module, a data collection module, a data analysis module, a performance monitoring module, a data storage module, a trend analysis module, and a real-time warning module;

[0037] Among them, the data collection module is used to automatically collect the experimental data measured by the experimental instrument at a fixed time; the data analysis module is used to visualize the data collected each time, perform correlation analysis with the data stored in the historical log, and regression analysis with the sample variables; the performance monitoring module is used to monitor the accuracy, precision and sensitivity of the collected experimental data. Through the comprehensive evaluation of the measured data of the experimental data, the actual data of the sample and the estimated data of the system, the real-time collection and detection of the sample data in the automatic cultivation process of the sample can ensure the normal development of the sample, thereby avoiding the contamination of the experimental sample due to untimely monitoring.

[0038] Example 2

[0039] like Figure 1 As shown, the accuracy of the experimental data is determined by the difference between the experimental measurement value and the true value, and the difference between the experimental measurement result and the predicted value.

[0040]

[0041] In the formula, h represents the accuracy coefficient of the experimental data detected by the data collection module, a represents the difference between the experimental measurement average and the true value, and s represents the difference between the average value of the experimental measurement result and the predicted value. (The true value or actual concentration x0 is the sample concentration actually measured by the staff in the laboratory every day, and the sample concentration needs to be entered into the data collection module by the staff)

[0042] Among them, the system measurement average value of the experimental data is taken (mg / mL), the estimated average value of the data analysis module is (mg / ml), and the actual sample concentration in this sample is x0=30 (mg / ml), then:

[0043] The difference between the experimental measurement average and the true value is a = -9

[0044] The difference between the average value of the experimental measurement results and the predicted value is s = -14

[0045] Then we have:

[0046]

[0047] From the above calculation, it can be known that the accuracy coefficient of the experimental data detected by the data collection module is h=0.8.

[0048] In this embodiment, the precision of the experimental data is determined by the ratio of the error to the true value and the degree of dispersion of the measured value distribution, which is:

[0049]

[0050] Where g represents the tightness coefficient of the experimental data detected by the data collection module, m represents the ratio of the error to the true value, and r represents the degree of dispersion of the measured value distribution.

[0051] Then, the ratio of error to true value is m = 0.3 (the ratio of error to true value = the number of samples with difference s in all samples ÷ the number of samples with actual sample concentration x0 in all samples), and the discrete degree of the measured value distribution is r = 3.2 ( Where n represents the number of all experimental samples, x i represents each measurement value, represents the average value of the measurements).

[0052] Then we have:

[0053]

[0054] From the above calculation, it can be known that the compactness coefficient of the experimental data detected by the data collection module is g=0.36.

[0055] In this embodiment, the sensitivity of the experimental data is further determined by testing the ratio of the average data detected to the true value and the degree of dispersion of the measured value distribution.

[0056]

[0057] Where k represents the sensitivity coefficient of the experimental data detected by the data collection module, p represents the ratio of the number of measured values ​​to the true values, and r represents the degree of dispersion of the measured value distribution.

[0058] The ratio of the number of measured values ​​to the true values ​​is p=0.25 (the ratio of the number of measured values ​​to the true values=the number of measured values ​​that are the same as the true values÷the total sample size).

[0059] The discrete degree of the measured value distribution is r = 3.2, then:

[0060]

[0061] From the above calculation, it can be known that the sensitivity coefficient of the experimental data detected by the data collection module is k=0.54.

[0062] In this embodiment, the performance monitoring module also includes a quality monitoring module; the quality monitoring module is used to measure the same experimental sample data multiple times, calculate the standard deviation and coefficient of variation of the results to evaluate the precision. The performance monitoring module determines whether the performance indicators of the experimental data exceed the threshold by measuring the accuracy, precision and sensitivity of the sample experimental data, including:

[0063]

[0064] In the formula, C represents the performance index coefficient of the experimental data collected by the data collection module, h represents the accuracy coefficient of the experimental data detected by the data collection module, g represents the tightness coefficient of the experimental data detected by the data collection module, and k represents the sensitivity coefficient of the experimental data detected by the data collection module;

[0065] When C≤65%, it means that the monitored experimental performance index does not exceed the set threshold;

[0066] When C>65%, it means that the monitored experimental performance index exceeds the set threshold, and at this time it needs to be fed back to the real-time warning module for warning.

[0067] From the above calculation, we can know that the accuracy coefficient of the experimental data detected by the data collection module is h=0.8, the tightness coefficient of the experimental data detected by the data collection module is g=0.36, and the sensitivity coefficient of the experimental data detected by the data collection module is k=0.54. Then:

[0068]

[0069] From the above calculation, it can be known that when the monitored experimental performance index exceeds the set threshold, it needs to be fed back to the real-time warning module for warning.

[0070] Example 3

[0071] like Figure 1 As shown in the figure, the sample tracking module is used to regularly check the deviation between the analysis results and the expected values ​​of the quality control samples with known concentrations added to the routine analysis. The indicator definition module is used to quantitatively evaluate the performance of the samples and experimental results. The data storage module is used to store the collected experimental data, data analysis results and warning status information.

[0072] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A laboratory data and index monitoring and analysis method and a decision support management system, characterized in that: It includes sample tracking module, indicator definition module, data collection module, data analysis module, performance monitoring module, data storage module, trend analysis module and real-time warning module; Wherein, the data collection module is used to automatically collect the experimental data measured by the experimental instrument at a regular time; The data analysis module is used to perform visual analysis on the data collected each time, and to perform correlation analysis on the data stored in the historical logs, as well as regression analysis between sample variables; The performance monitoring module is used to monitor the accuracy, precision and sensitivity of the collected biological experimental data; The real-time early warning module issues experimental data early warning to the laboratory administrator based on the feedback from the quality monitoring module.

2. The laboratory data and indicator monitoring and analysis method and decision support management system according to claim 1, characterized in that: The performance monitoring module also includes a quality monitoring module; The quality monitoring module is used to perform multiple measurements on the same experimental sample data, calculate the standard deviation and coefficient of variation of the results to evaluate the precision.

3. The laboratory data and indicator monitoring and analysis method and decision support management system according to claim 1, characterized in that: The performance monitoring module determines whether the performance indicators of the experimental data exceed the threshold by measuring the accuracy, precision and sensitivity of the sample experimental data. In the formula, C represents the performance index coefficient of the experimental data collected by the data collection module, h represents the accuracy coefficient of the experimental data detected by the data collection module, g represents the tightness coefficient of the experimental data detected by the data collection module, and k represents the sensitivity coefficient of the experimental data detected by the data collection module; When C≤65%, it means that the monitored experimental performance index does not exceed the set threshold; When C>65%, it means that the monitored experimental performance index exceeds the set threshold, and at this time it needs to be fed back to the real-time warning module for warning.

4. The laboratory data and indicator monitoring and analysis method and decision support management system according to claim 3, characterized in that: The accuracy of the experimental data is determined by the difference between the experimental measurement value and the true value, and the difference between the experimental measurement result and the predicted value. Where h represents the accuracy coefficient of the experimental data detected by the data collection module, a represents the difference between the experimental measurement average and the true value, and s represents the difference between the experimental measurement average and the predicted value.

5. The laboratory data and indicator monitoring and analysis method and decision support management system according to claim 4, characterized in that: The precision of experimental data is determined by the ratio of the error to the true value and the degree of dispersion of the measured value distribution: Where g represents the tightness coefficient of the experimental data detected by the data collection module, m represents the ratio of the error to the true value, and r represents the degree of dispersion of the measured value distribution.

6. The laboratory data and indicator monitoring and analysis method and decision support management system according to claim 5, characterized in that: The sensitivity of the experimental data is determined by testing the proportion of the average data detected and the true value, as well as the degree of dispersion of the measured value distribution. Where k represents the sensitivity coefficient of the experimental data detected by the data collection module, p represents the ratio of the number of measured values ​​to the true values, and r represents the degree of dispersion of the measured value distribution.

7. The laboratory data and indicator monitoring and analysis method and decision support management system according to claim 1, characterized in that: The sample tracking module is used to regularly check the deviation between the analysis results and the expected values ​​of the quality control samples with known concentrations added in the routine analysis.

8. The laboratory data and indicator monitoring and analysis method and decision support management system according to claim 1, characterized in that: The indicator definition module is used to quantitatively evaluate the performance of samples and experimental results.

9. The laboratory data and indicator monitoring and analysis method and decision support management system according to claim 1, characterized in that: The data storage module is used to store the collected experimental data, data analysis results and warning status information.

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