WO2024245224A1 - 基于患者样本进行实时质量控制的方法、系统和构建质控模型的方法 - Google Patents

基于患者样本进行实时质量控制的方法、系统和构建质控模型的方法 Download PDF

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WO2024245224A1
WO2024245224A1 PCT/CN2024/095713 CN2024095713W WO2024245224A1 WO 2024245224 A1 WO2024245224 A1 WO 2024245224A1 CN 2024095713 W CN2024095713 W CN 2024095713W WO 2024245224 A1 WO2024245224 A1 WO 2024245224A1
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Prior art keywords
patient
patient information
control
samples
parameter value
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English (en)
French (fr)
Inventor
郑文波
陈鹏震
叶波
祁欢
刘妍
杨程
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Shenzhen Mindray Bio Medical Electronics Co Ltd
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Shenzhen Mindray Bio Medical Electronics Co Ltd
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Priority to EP24814421.4A priority Critical patent/EP4723126A1/en
Priority to CN202480035688.9A priority patent/CN121359214A/zh
Publication of WO2024245224A1 publication Critical patent/WO2024245224A1/zh
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • the present application relates to the field of quality control of in vitro diagnostic instruments, and in particular to a method and system for performing real-time quality control based on patient samples, and a method for constructing a quality control model for performing real-time quality control based on patient samples.
  • Quality control of medical test laboratories includes three aspects: quality control before analysis, during analysis, and after analysis. Among them, quality control during analysis is the most critical process in the quality management system. Quality control during analysis includes internal quality control (IQC).
  • IQC internal quality control
  • the common implementation of IQC is that the inspectors test the quality control products at a certain frequency, use statistical theory to calculate and evaluate the reliability of the test results, and then observe and eliminate the out-of-control factors in the test.
  • the task of the present application is to provide an improved PBRTQC-based technical solution that can achieve more stable and more sensitive indoor quality control.
  • the first aspect of the present application provides a method for real-time quality control based on patient samples, comprising:
  • the detection instrument performing real-time detection on the current patient sample for the target item, obtaining the actual detection result of the current patient sample detected by the detection instrument for the target item and the patient information, wherein the patient information includes the patient's disease, age, gender and at least one of the ordering department for the target item;
  • the second aspect of the present application provides a computer system for real-time quality control based on patient samples, comprising:
  • a data acquisition module configured to acquire, during the process of the detection instrument performing real-time detection on the current patient sample for the target item, an actual detection result of the current patient sample detected by the detection instrument for the target item and patient information, wherein the patient information includes at least one of the patient's disease, age, gender and the ordering department for the target item;
  • a prediction result acquisition module configured to quantify the patient information of the current patient sample and input the quantified patient information into a pre-built machine learning model to obtain an output result of the machine learning model as a prediction test result for the target item, wherein the machine learning model is trained by actual test results and patient information of multiple historical patient samples for the target item;
  • a monitoring indicator acquisition module is configured to acquire monitoring indicators based on the actual test results of the current patient sample and the predicted test results
  • the quality control module is configured to determine whether the detection instrument is under control based on the monitoring indicators, and if not,.
  • the third aspect of the present application provides a method for constructing a quality control model for real-time quality control based on patient samples, comprising:
  • the training set is used to establish a machine learning model by quantifying the patient information of the patient samples in the training set, and establishing a machine learning model that satisfies the following formula between the quantified patient information and the actual test results in the training set:
  • X t f(x 1t ,x 2t ,...,x nt )+ ⁇ t
  • Xt is the actual test result in the training set
  • f( x1t , x2t , ..., xnt ) is a machine learning model
  • x1t , x2t , ..., xnt represent the quantified patient information
  • ⁇ t represents the residual error of estimating Xt using the machine learning model
  • the quality control model is validated using the test set.
  • a machine learning model trained based on actual test results of historical patient samples and patient information is used to obtain monitoring indicators for quality control of the testing instrument.
  • FIG1 is a schematic flow chart of a method for real-time quality control based on patient samples according to an embodiment of the present application.
  • FIG. 2 is a schematic diagram showing the number of samples required for error detection.
  • FIG3 is a schematic diagram of monitoring indicators obtained according to an embodiment of the present application and monitoring indicators obtained according to the prior art.
  • FIG4 is a schematic block diagram of a computer system for performing real-time quality control based on patient samples according to an embodiment of the present application.
  • FIG5 is a schematic flowchart of a method for constructing a quality control model for real-time quality control based on patient samples according to an embodiment of the present application.
  • FIG6 is a schematic diagram of a structure of a neural network model according to an embodiment of the present application.
  • FIG. 7 is a schematic flowchart of a method for optimizing a quality control model according to an embodiment of the present application.
  • FIG8 is a schematic flowchart of another method for optimizing a quality control model according to an embodiment of the present application.
  • FIG. 9 is a schematic flowchart of yet another method for optimizing a quality control model according to an embodiment of the present application.
  • PBRTQC patient-based real-time quality control
  • the advantages of PBRTQC are: 1) It can use the patient sample monitoring system in real time to detect the out-of-control state of the instrument in time; 2) Because of the use of patient samples, there is no matrix effect; 3) PBRTQC is an analytical software that does not require additional quality control products, reagents, and labor costs, and has a low cost.
  • the existing PBRTQC requires a large number of continuous samples to identify errors and system out-of-control states, which cannot meet clinical IQC requirements.
  • the inventors of the present application found that for conventional indicators such as white blood cell count, red blood cell count, hemoglobin content, thyroid stimulating hormone, etc., the distribution and fluctuation of parameter indicators are large due to factors such as the patient's disease, age, gender, etc., resulting in a large number of samples required for the existing PBRTQC method to detect the average error of the system.
  • this application proposes a technical solution for real-time quality monitoring based on patient samples and neural networks, so as to reduce the impact of factors such as the patient's disease, age, gender, etc. on the PBRTQC quality control performance (i.e., improve the quality control stability), and at the same time reduce the number of samples required for error detection compared to the existing PBRTQC quality control (i.e., improve sensitivity).
  • Fig. 1 shows a method 100 for real-time quality control based on patient samples according to an embodiment of the present application.
  • the method 100 includes a data acquisition step S110, a data processing step S120, a monitoring index acquisition step S130 and a quality control step S140.
  • the detection instrument while the detection instrument is performing real-time detection on the target item of the current patient sample, that is, in real time, the actual detection result of the current patient sample obtained by the detection instrument for the target item and the patient information are acquired, wherein the patient information includes the patient's disease, age, gender and at least one of the department to which the sample belongs.
  • the department of the sample refers to the department that issues the order for the target project, that is, the hospital department that requests the use of the detection instrument to detect the current patient sample for the target project.
  • the patient information of the current patient sample is quantified and the quantified patient information is input into a pre-built machine learning model to obtain the output result of the machine learning model as the predicted test result for the target project, wherein the machine learning model is obtained by training the actual test results and patient information for the target project of multiple known patient samples (i.e., historical patient samples).
  • the machine learning model proposed in the embodiment of the present application characterizes the relationship between the patient information and the test results of the target project. In other words, if the patient information is input into the machine learning model, the predicted test result for the target project predicted by the machine learning model will be obtained.
  • the monitoring index acquisition step S130 the monitoring index is acquired based on the actual test result of the current patient sample obtained in step S110 and the predicted test result obtained in step S120.
  • the quality control step S140 it is determined based on the monitoring index whether the detection instrument is under control and/or it is determined based on the monitoring index whether to give an alarm indicating that the detection instrument is out of control. For example, when it is determined based on the monitoring index that the detection instrument is not under control, i.e., out of control, an alarm indicating that the detection instrument is out of control is output.
  • the performance of the quality control method based on PBRTQC of the present application and the performance of the quality control method based on PBRTQC of the prior art are verified by using the number of samples required for error detection NPed under the same false alarm rate.
  • the false alarm rate FAR refers to the number of false alarms of the monitoring index calculated based on the data obtained under the controlled state of the detection instrument (the number exceeding the preset control line)/the number of samples*100%.
  • the number of samples required for error detection NPed refers to the number of samples required from the detection instrument to the monitoring index exceeding the preset control line, as shown in Figure 2.
  • the WBC verification data set, RBC verification data set, HGB verification data set and TSH verification data set obtained under the controlled state of the detection instrument are used respectively.
  • the PBRTQC-based quality control method of the present application and the PBRTQC-based quality control method of the prior art are simulated by adding errors multiple times, and the average number of samples required for error detection ANPed as shown in Table 1 is obtained.
  • Table 1 the PBRTQC-based quality control method proposed in the present application significantly improves the quality control sensitivity compared with the prior art.
  • the monitoring index NNs-PBRTQC of the embodiment of the present application and the monitoring index PBRTQC of the existing PBRTQC are calculated based on the WBC data set obtained under the controlled state of the detection instrument, as shown in Figure 3.
  • the monitoring index calculated according to the embodiment of the present application is more stable than the monitoring index calculated according to the existing PBRTQC
  • the control line obtained according to the embodiment of the present application is smaller than the control line obtained according to the existing PBRTQC.
  • a detection instrument being controlled means that the detection instrument is in a normal state and no fault occurs; and a detection instrument being out of control means that the detection instrument is in an abnormal state and a fault may occur.
  • the machine learning model may be a neural network model. In other embodiments, the machine learning model may be a machine learning model based on SVM (support vector machine) and/or LDA (support vector machine).
  • the target items can be routine blood test items, such as cell count (such as white blood cell count, platelet count, red blood cell count, etc.) and classification (such as white blood cell classification, etc.), hemoglobin content, etc.; accordingly, the detection instrument is a blood cell analyzer.
  • cell count such as white blood cell count, platelet count, red blood cell count, etc.
  • classification such as white blood cell classification, etc.
  • hemoglobin content etc.
  • the detection instrument is a blood cell analyzer.
  • the target items may be biochemical detection items, such as thyroid function (such as thyroid stimulating hormone, etc.), liver function, kidney function, blood lipids, blood sugar, etc.; accordingly, the detection instrument is a biochemical analyzer.
  • thyroid function such as thyroid stimulating hormone, etc.
  • liver function such as thyroid stimulating hormone, etc.
  • kidney function such as kidney function
  • blood lipids such as blood sugar, etc.
  • the detection instrument is a biochemical analyzer.
  • the patient information may include multiple of the patient's disease, age, gender, and the department to which the sample belongs.
  • the patient information may include the patient's disease, age, gender, and the department to which the sample belongs.
  • the patient information can be obtained from the laboratory (inspection department) information system of the hospital where the detection instrument is located. System acquisition.
  • the step S130 of acquiring monitoring indicators based on the actual detection results and the predicted detection results may include:
  • the difference is input into a calculation model based on a process control SPC algorithm to obtain an output result of the calculation model as the monitoring indicator.
  • the process control SPC algorithm may, for example, include at least one of a floating mean, a floating median, an exponentially weighted moving average, a floating standard deviation, a floating quantile, and a floating number of patients with abnormal values.
  • the step S130 of acquiring the monitoring indicator based on the actual detection result and the predicted detection result may also include acquiring the monitoring indicator based on the ratio of the actual detection result to the predicted detection result.
  • quantifying the patient information of the current patient sample may include: quantifying at least one of the patient information of the current patient sample, especially at least one of the disease, gender, and department to which the sample belongs, into matrices.
  • quantizing at least one type of patient information of the current patient sample into matrices may include: quantizing each type of patient information of the at least one type of patient information of the current patient sample into a one-dimensional matrix having multiple elements, wherein each element of the one-dimensional matrix represents a category, and the patient information belongs to at least one of the categories represented by the element.
  • the disease information when the patient information includes disease information, if the disease can be classified into 33 disease categories, the disease information may be quantified into a one-dimensional matrix having 33 elements, wherein each element represents a disease.
  • the patient information when the patient information includes gender information, since gender is divided into two categories, namely male and female, the gender information can be quantified into a one-dimensional matrix with two elements, wherein one element represents male and the other element represents female.
  • the patient information includes information about the department to which the sample belongs
  • the department to which the sample belongs can be divided into 32 department categories
  • the information about the department to which the sample belongs can be quantified into a one-dimensional matrix with 32 elements, where each element represents a department.
  • quantizing each type of patient information in the at least one type of patient information of the current patient sample into a one-dimensional matrix having multiple elements may include: quantizing each type of patient information in the at least one type of patient information of the current patient sample into a one-dimensional matrix having multiple elements composed of 0 and 1, wherein the value of the element representing the category to which the information belongs is 1, and the values of the remaining elements are 0. This simplifies the construction of the machine learning model.
  • the disease information when the patient information includes disease information, if the disease can be divided into 33 disease categories, the disease information can be quantized into a one-dimensional matrix [X1, X2, ..., X33] with 33 elements, where each element X represents a disease, for example, X1 represents hyperthyroidism, X2 represents hypertension, etc. If the disease information of a current patient sample is hyperthyroidism, the disease information of the patient sample is quantized to [1, 0, 0, 0, ..., 0], that is, the value of element X1 is 1, and the values of the remaining elements are 0.
  • the disease information of a current patient sample includes hyperthyroidism and hypertension
  • the disease information of the patient sample is quantized to [1, 1, 0, 0, ..., 0], that is, the values of elements X1 and X2 are 1, and the values of the remaining elements are 0.
  • the gender information when the patient information includes gender information, since gender is divided into two categories, namely male and female, the gender information can be quantized into a one-dimensional matrix [Y1, Y2] with two elements, wherein element Y1 represents male and element Y2 represents female. If the gender information of a patient sample is male, the gender information of the patient sample is quantized to [1,0], that is, the value of element Y1 is 1 and the value of element Y2 is 0.
  • the patient information includes information about the department to which the sample belongs
  • the department to which the sample belongs can be divided into 32 department categories
  • the information about the department to which the sample belongs can be quantized into a one-dimensional matrix [Z1, Z2, ..., Z32] with 32 elements, wherein each element represents a department, for example, Z1 represents the Department of Endocrinology, Z2 represents the Department of Nephrology, etc.
  • Z1 represents the Department of Endocrinology
  • Z2 represents the Department of Nephrology, etc.
  • the information about the department to which the sample of a patient belongs is the Department of Endocrinology
  • the information about the department to which the sample of the patient belongs is quantized into [1, 0, 0, 0, ..., 0], that is, the value of the element Z1 is 1, and the values of the remaining elements are 0.
  • quantizing the patient information of the current patient sample may include: quantizing at least one type of patient information in the patient information of the current patient sample, especially age, as a fixed value, preferably as a fixed integer.
  • age can be quantified as a fixed value from 0 to 150, i.e., if the patient's age is 30, then his age is quantified as 30. In other examples, age can be quantified as a percentage. For example, if the patient's age is 30, then his age is quantified as 30/100.
  • gender can be quantified as a first positive integer or a second positive integer, wherein the first positive integer represents a male and the second positive integer represents a female.
  • first positive integer is 1 and the second positive integer is 2, but the present application is not limited thereto.
  • At least one of the patient information of the current patient sample may be quantified by a table lookup method, thereby simplifying the construction of the machine learning model.
  • different positive integers can be assigned to different diseases, and the assignment of diseases to corresponding positive integers can be pre-stored in the form of a lookup table. Therefore, when quantifying a patient's disease, the positive integer corresponding to the patient's disease is searched through the lookup table.
  • Table 2 shows an example of a lookup table for quantifying diseases.
  • different positive integers may be assigned to different departments, and the assignments of departments and corresponding positive integers may be pre-stored in the form of a lookup table. Therefore, when quantifying the department to which the patient sample belongs, the positive integer corresponding to the department to which the patient sample belongs is searched through the lookup table.
  • Table 3 shows an example of a lookup table for quantifying departments.
  • the average value of the actual test results for the target item of n patient samples (the value of n is, for example, the daily test throughput of the detection instrument) among the multiple known patient samples before a specific moment can also be used as a variable of the machine learning model, so as to reduce, for example, the diurnal effects caused by the calibration of the detection instrument and the diurnal effects caused by reagents.
  • the machine learning model is trained by the actual test results of the target project of multiple known patient samples (i.e., historical patient samples) and patient information.
  • the machine learning model can also further characterize the relationship between the patient information, the mean of the actual test results of the multiple known patient samples, and the test results of the target project. In other words, if the patient information is input into the machine learning model, the predicted test results for the target project predicted by the machine learning model will be obtained.
  • step S120 that is, obtaining monitoring indicators based on the actual detection results and the predicted detection results may include:
  • the monitoring index is obtained based on the actual detection result and the predicted detection result after data processing.
  • the actual detection result may not be normalized, thereby simplifying data processing.
  • step S140 when one of the monitoring indicators exceeds a preset control line, it can be determined that the detection instrument is out of control and an alarm prompt can be output.
  • the detection instrument in step S140, may be judged to be out of control and an alarm may be output only when M consecutive monitoring indicators (obtained in chronological order) exceed a preset control line, where M is a natural number greater than 1. Compared with the case where an alarm is issued when one monitoring indicator exceeds a preset control line, the false alarm rate can be reduced.
  • the monitoring indicators of a first number M of continuous current patient samples can be acquired in chronological order, and whether the detection instrument is under control can be determined based on the monitoring indicators of the first number M of continuous current patient samples; the detection instrument is determined to be out of control and an alarm prompt indicating that the detection instrument is out of control is output when at least a second number N of current patient samples among the monitoring indicators of the first number M of continuous current patients (obtained in chronological order) exceed a preset control line, wherein the first number M and the second number N are both natural numbers greater than 1 and the first number M is greater than the second number N.
  • the first number M and the second number N are both natural numbers greater than 1 and the first number M is greater than the second number N.
  • step S140 when the detection instrument is judged to be out of control based on the monitoring index, the quality control product for the target project can be automatically retrieved to the detection instrument for detection to obtain the detection result of the quality control product; and whether the detection instrument is out of control is determined based on the detection result of the quality control product. That is, when the detection instrument is judged to be out of control based on the detection result of the actual patient sample, the quality control product is further used to test the detection instrument to determine whether the detection instrument is really out of control.
  • the parameters of the computational model may be optimized in the following manner:
  • the actual test results of the plurality of historical patient samples and the patient information are used to calculate the monitoring index under the SPC parameter value, and the upper and lower control lines are obtained based on the monitoring index under the SPC parameter value and the false alarm rate,
  • a simulated data set containing multiple simulated instrument out-of-control or a real data set containing multiple real instrument out-of-control is used to calculate the monitoring index of the presence of instrument out-of-control under the SPC parameter value, and the monitoring index is compared with the upper and lower control lines to calculate the average number of samples required from the start of the simulated instrument out-of-control or the real instrument out-of-control to the actual monitoring of the simulated instrument out-of-control or the real instrument out-of-control, that is, the average number of samples required for error detection,
  • the simulated data set is obtained by adding errors to the actual test results of the multiple historical patient samples at different time points for multiple times, and the real data set is obtained from the actual test results and patient information of the multiple historical patient samples or by obtaining the actual test results and patient information of multiple other historical patient samples for the target item, and
  • the SPC parameter value with the smallest number of samples required for average error detection and the upper and lower control lines corresponding to the SPC parameter value are selected to construct the optimized calculation model.
  • the parameters of the truncation process and the parameters of the calculation model may be optimized in the following manner:
  • the upper and lower control lines and the number of samples required for the average error detection at the false alarm rate are obtained in the following manner:
  • the actual test results of the plurality of historical patient samples and the patient information are used to calculate the monitoring index under the parameter value combination, and the upper and lower control lines are obtained based on the monitoring index under the parameter value combination and the false alarm rate,
  • a simulated data set containing multiple simulated instrument out-of-control or a real data set containing multiple real instrument out-of-control is used to calculate the monitoring index of the presence of instrument out-of-control under the parameter value combination, and the monitoring index is compared with the upper and lower control lines to calculate the average number of samples required from the start of the simulated instrument out-of-control or the real instrument out-of-control to the actual monitoring of the simulated instrument out-of-control or the real instrument out-of-control, that is, the average number of samples required for error detection,
  • the simulated data set is obtained by adding errors to the actual test results of the multiple historical patient samples at different time points for multiple times, and the real data set is obtained from the actual test results and patient information of the multiple historical patient samples or by obtaining the actual test results and patient information of multiple other historical patient samples for the target item;
  • the parameter value combination with the minimum number of samples required for average error detection and the upper and lower control lines corresponding to the parameter value combination are selected to construct the optimized truncation process and the calculation model.
  • Fig. 4 is a schematic block diagram of a computer system 200 for real-time quality control based on patient samples according to an embodiment of the present application.
  • the computer system 200 includes a data acquisition module 210, a prediction result acquisition module 220, a monitoring indicator acquisition module 230 and a quality control module 240.
  • the data acquisition module 210 is configured to acquire the actual test results and patient information of the current patient sample obtained by the testing instrument for the target item in real time during the process of the testing instrument performing real-time testing on the target item of the current patient sample, i.e., in real time, wherein the patient information includes the patient's disease, age, gender and at least one of the department to which the sample belongs.
  • the prediction result acquisition module 220 is configured to quantify the patient information of the current patient sample and input the quantified patient information into a pre-built machine learning model to obtain the output result of the machine learning model as the predicted detection result for the target item, wherein the machine learning model is trained through the actual detection results of the target item and patient information of multiple known patient samples.
  • the monitoring indicator acquisition module 230 is configured to acquire monitoring indicators based on the actual test results of the current patient sample and the predicted test results.
  • the quality control module 240 is configured to determine whether the detection instrument is under control based on the monitoring indicators and/or determine whether to give an alarm prompt indicating that the detection instrument is out of control based on the monitoring indicators. When the detection instrument is determined to be out of control based on the monitoring indicators, the quality control module 240 outputs an alarm prompt indicating that the detection instrument is out of control.
  • the computer system 200 proposed in the embodiment of the present application may be implemented in the form of hardware, such as a processor, or in the form of software, such as software that can be installed and executed on a computer. This application does not make any specific limitation on this.
  • the machine learning model may be a neural network model. In other embodiments, the machine learning model may be a machine learning model based on SVM (support vector machine) and/or LDA (support vector machine).
  • the target items can be routine blood test items, such as cell count (such as white blood cell count, platelet count, red blood cell count, etc.) and classification (such as white blood cell classification, etc.), hemoglobin content, etc.; accordingly, the detection instrument is a blood cell analyzer.
  • cell count such as white blood cell count, platelet count, red blood cell count, etc.
  • classification such as white blood cell classification, etc.
  • hemoglobin content etc.
  • the detection instrument is a blood cell analyzer.
  • the target items may be biochemical detection items, such as thyroid function (such as thyroid stimulating hormone, etc.), liver function, kidney function, blood lipids, blood sugar, etc.; accordingly, the detection instrument is a biochemical analyzer.
  • thyroid function such as thyroid stimulating hormone, etc.
  • liver function such as thyroid stimulating hormone, etc.
  • kidney function such as kidney function
  • blood lipids such as blood sugar, etc.
  • the detection instrument is a biochemical analyzer.
  • the patient information may include multiple of the patient's disease, age, gender, and the department to which the sample belongs.
  • the patient information may include the patient's disease, age, gender, and the department to which the sample belongs.
  • the patient information can be obtained from the laboratory (testing department) information system of the hospital where the testing instrument is located.
  • the monitoring indicator acquisition module 230 may be further configured as follows:
  • the difference is input into a calculation model based on a process control SPC algorithm to obtain an output result of the calculation model as the monitoring indicator.
  • the process control SPC algorithm may, for example, include at least one of a floating mean, a floating median, an exponentially weighted moving average, a floating standard deviation, a floating quantile, and a floating number of patients with abnormal values.
  • the prediction result acquisition module 220 can be further configured to quantify at least one of the patient information of the current patient sample, especially at least one of the disease, gender, and department to which the sample belongs, into a matrix.
  • the prediction result acquisition module 220 can be further configured to obtain the at least Each type of patient information is quantified into a one-dimensional matrix with multiple elements, wherein each element of the one-dimensional matrix represents a category, and the patient information belongs to at least one of the categories represented by the element.
  • the prediction result acquisition module 220 can be further configured to quantify each type of patient information among the at least one type of patient information of the current patient sample into a one-dimensional matrix consisting of 0 and 1 having multiple elements, wherein the value of the element representing the category to which the patient information belongs is 1, and the values of the remaining elements are 0.
  • the prediction result acquisition module 220 can be further configured to quantify at least one type of patient information in the patient information of the current patient sample, especially age, as a fixed value, preferably as a fixed integer.
  • the prediction result acquisition module 220 may be further configured to quantify at least one piece of patient information in the patient information of the current patient sample by a table lookup method, thereby simplifying the construction of the machine learning model.
  • the monitoring indicator acquisition module 230 may be further configured as follows:
  • the monitoring index is obtained based on the actual detection result and the predicted detection result after data processing.
  • the actual detection result may not be normalized, thereby simplifying data processing.
  • the quality control module 240 can be further configured to determine that the detection instrument is out of control and output an alarm prompt when one of the monitoring indicators exceeds a preset control line.
  • the quality control module 240 may be further configured to determine that the detection instrument is out of control and output an alarm prompt only when M consecutive monitoring indicators (obtained in chronological order) exceed the preset control line, where M is a natural number greater than 1. Compared with the case where an alarm is issued when one monitoring indicator exceeds the preset control line, the false alarm rate can be reduced.
  • the quality control module 240 can be further configured to obtain monitoring indicators of a first number M of continuous current patient samples in chronological order, and determine whether the detection instrument is under control based on the monitoring indicators of the first number M of continuous current patient samples; the detection instrument is judged to be out of control and an alarm prompt indicating that the detection instrument is out of control is output when at least a second number N of monitoring indicators of the current patient samples of the first number M of continuous current patient samples (obtained in chronological order) exceeds a preset control line, wherein the first number M and the second number N are both natural numbers greater than 1 and the first number M is greater than the second number N.
  • the false alarm rate can be reduced, and the number of samples required for error detection is kept as small as possible.
  • the quality control module 240 can be further configured to automatically retrieve quality control products for the target project to the testing instrument for testing to obtain the test results of the quality control products when the testing instrument is judged to be out of control based on the monitoring indicators; and judge whether the testing instrument is out of control based on the test results of the quality control products.
  • the monitoring indicator acquisition module can be optimized in the following ways:
  • the actual test results of the plurality of historical patient samples and the patient information are used to calculate the monitoring index under the SPC parameter value, and the upper and lower control lines are obtained based on the monitoring index under the SPC parameter value and the false alarm rate,
  • a simulated data set containing multiple simulated instrument out-of-control or a real data set containing multiple real instrument out-of-control is used to calculate the monitoring index of the existence of instrument out-of-control under the SPC parameter value, and the monitoring index is compared with the upper and lower control lines to calculate the average number of samples required from the start of the simulated instrument out-of-control or the real instrument out-of-control to the actual monitoring of the simulated instrument out-of-control or the real instrument out-of-control, that is, the average number of samples required for error detection,
  • the simulated data set is obtained by adding errors to the actual test results of the multiple historical patient samples at different time points for multiple times, and the real data set is obtained from the actual test results and patient information of the multiple historical patient samples or by obtaining the actual test results and patient information of multiple other historical patient samples for the target item, and
  • the SPC parameter value with the smallest number of samples required for average error detection and the upper and lower control lines corresponding to the SPC parameter value are selected to construct the optimized calculation model.
  • the monitoring indicator acquisition module may be optimized in the following ways:
  • the upper and lower control lines and the number of samples required for the average error detection at the false alarm rate are obtained in the following manner:
  • the actual test results of the plurality of historical patient samples and the patient information are used to calculate the monitoring index under the parameter value combination, and the upper and lower control lines are obtained based on the monitoring index under the parameter value combination and the false alarm rate,
  • a simulated data set containing multiple simulated instrument out-of-control or a real data set containing multiple real instrument out-of-control is used to calculate the monitoring index of the presence of instrument out-of-control under the parameter value combination, and the monitoring index is compared with the upper and lower control lines to calculate the average number of samples required from the start of the simulated instrument out-of-control or the real instrument out-of-control to the actual monitoring of the simulated instrument out-of-control or the real instrument out-of-control, that is, the average number of samples required for error detection,
  • the simulated data set is obtained by adding errors to the actual test results of the multiple historical patient samples at different time points for multiple times, and the real data set is obtained from the actual test results and patient information of the multiple historical patient samples or by obtaining the actual test results and patient information of multiple other historical patient samples for the target item;
  • the parameter value combination with the minimum number of samples required for average error detection and the upper and lower control lines corresponding to the parameter value combination are selected to construct the optimized truncation process and the calculation model.
  • FIG5 shows a method 300 for constructing a quality control model for real-time quality control based on patient samples according to an embodiment of the present application, comprising the following steps:
  • S310 obtaining data of multiple patient samples and dividing the data into a training set and a test set (for example, the data can be divided into a training set and a test set in chronological order), wherein the data includes the actual test results of the multiple patient samples for the target items and patient information, wherein the actual test results are obtained by the detection instrument detecting the patient samples, and the patient information includes the patient's disease, age, gender, and at least one of the departments to which the samples belong, wherein the data in the training set is obtained when the detection instrument is in a controlled state.
  • the actual test results of the target items and patient information of multiple patient samples, especially all patient samples, detected by the detection instrument in chronological order within a certain period of time are obtained.
  • step S310 the sample ratio of the training set and the test set can be set to 6:4 or 7:3, etc.
  • Xt is the actual test result in the training set
  • f( x1t , x2t , ..., xnt ) is the machine learning model
  • x1t , x2t , ..., xnt represent the quantified patient information
  • ⁇ t represents the residual of estimating Xt using the machine learning model. That is, the machine learning model f is regressed using the historical test results and patient information of the patient controlled by the detection instrument, and ⁇ t is approximately a random distribution with a mean of 0.
  • S340 Use the test set to verify the quality control model so as to verify the performance of the quality control model.
  • the machine learning model f is a nonlinear function of an artificial neural network, it can reasonably estimate the deviations caused by factors such as disease, age, and gender. After eliminating such factors that affect parameter fluctuations, the monitoring indicators obtained by the quality control model constructed based on the machine learning model are stable, which improves the system error detection performance compared with the existing PBRTQC.
  • a loss function min ⁇ t ⁇ 2 can be established based on this principle, and the machine learning model f(x 1t ,x 2t ,...,x nt ) with the loss function minimized is calculated by taking the training samples, i.e., the actual test results of the above-mentioned multiple patient samples and the patient information as inputs.
  • the machine learning model may be a neural network model, wherein the structure of the neural network model is shown in Figure 6.
  • the machine learning model may be a machine learning model based on SVM (support vector machine) and/or LDA (support vector machine).
  • constructing a quality control model for real-time quality control based on patient samples based on the machine learning model may include: constructing the quality control model according to the machine learning model and a computational model based on a process control SPC algorithm, wherein the difference between the actual test result and the test result predicted by the machine learning model f, i.e., the residual ⁇ t, is input into the computational model based on the process control SPC algorithm to obtain the output of the computational model as a monitoring indicator of the quality control model.
  • the monitoring indicators of the quality control model may be calculated from the actual test results and the test results predicted by the machine learning model f in other ways, such as by calculating the ratio.
  • the average value of the actual test results for the target item of n patient samples (the value of n is, for example, the daily test throughput of the detection instrument) in the test set before a specific time can also be used as a variable of the machine learning model to reduce, for example, the diurnal effects caused by the calibration of the detection instrument and the diurnal effects caused by reagents.
  • step S330 the false alarm rate is given and the upper and lower control lines of the quality control model are obtained based on the monitoring index and the false alarm rate. If the measuring instrument is out of control, the residual ⁇ t will produce non-random characteristics. After calculation based on the process control SPC algorithm, the monitoring index will exceed the upper control line or the lower control line, thus generating an alarm, indicating that the measuring instrument is out of control.
  • the process control SPC algorithm may include at least one of a floating mean, a floating median, an exponentially weighted moving average, a floating standard deviation, a floating quantile, and a floating number of outlier patients.
  • the parameters of the calculation model may be optimized in the following manner:
  • Step S331a setting the false alarm rate FAR
  • Step S332a providing a plurality of SPC parameter values of the calculation model
  • Step S333a for each SPC parameter value, the upper and lower control lines and the number of samples required for average error detection under the false alarm rate are obtained in the following manner:
  • the actual test results in the training set and the patient information are used to calculate the monitoring index under the SPC parameter value, and the upper and lower control lines are obtained based on the monitoring index under the SPC parameter value and the false alarm rate,
  • a simulated data set containing multiple simulated instrument out-of-control locations or a real data set containing multiple real instrument out-of-control locations is used in the quality control model having the SPC parameter value to calculate monitoring indicators of instrument out-of-control at the parameter value, and the monitoring indicators are compared with the upper and lower control lines to calculate the average number of samples required from the start of the simulated instrument out-of-control locations or the real instrument out-of-control locations to the actual monitoring of the simulated instrument out-of-control or the real instrument out-of-control by the quality control model, that is, the average number of samples required for error detection ANPed, wherein the simulated data set is obtained by adding errors to the actual test results in the training set at different time points for multiple times, and the real data set is obtained from the data of the multiple patient samples or by obtaining the actual test results of multiple other patient samples for target items and their patient information, and
  • Step S334a selecting the SPC parameter value with the minimum number of samples required for average error detection and the upper and lower control lines corresponding to the SPC parameter value to construct an optimized quality control model.
  • the false alarm rate directly determines the upper and lower control lines, and the false alarm rate is usually set by the user in combination with the usage conditions, such as setting the false alarm rate to 0.1%. Once the false alarm rate is determined, the upper and lower control lines can also be determined.
  • a simulated data set containing multiple simulated instrument out-of-control situations is used to calculate the average number of samples required for error detection ANPed, that is: the actual test results in the training set and the patient information are used in the quality control model having the SPC parameter value to calculate the monitoring indicators under the SPC parameter value, and the upper and lower control lines are obtained based on the monitoring indicators under the parameter value and the false alarm rate; errors are added to the actual test results of the target items of the patient samples in the training set at different time points for multiple times, and after the errors are added, the actual test results of the target items of the patient samples in the training set are input into the quality control model having the SPC parameter value together with the corresponding patient information to obtain the monitoring indicators after the error is added and compared with the upper and lower control lines, so as to calculate the average number of samples required from the start of the error addition to the actual detection of the error by the quality control model, that is, the average number of samples required for error detection ANPed.
  • the parameter of the calculation model based on the floating mean method is a sliding window.
  • the false alarm rate is given to be 0.1%, and the parameter values of the given sliding window are 5, 10, 15, 20, 50 and 100.
  • steps S331a, S332a, S333a and S334a may be repeated, and then the minimum average error detection required sample number ANPedmin is selected from the average error detection required sample numbers under multiple false alarm rates FAR (e.g., 0.001%, 0.01%, 0.1%, 1%, 3%, 5%), so as to obtain the parameter value corresponding to the minimum average error detection required sample number and the upper and lower control lines corresponding to the parameter value, so as to construct an optimized quality control model.
  • FAR false alarm rates
  • the test set can be used as the real data set to obtain the upper and lower control lines and the average number of samples required for error detection under the false alarm rate for each SPC parameter value, in the following manner: in the quality control model with the SPC parameter value, the actual test results of the patient samples in the training set for the target items and the patient information are used to calculate the monitoring indicators under the SPC parameter value, and the upper and lower control lines are obtained based on the monitoring indicators and the false alarm rate under the SPC parameter value; the actual test results of the patient samples in the test set for the target items together with the corresponding patient information are input into the quality control model with the SPC parameter value to obtain the monitoring indicators corresponding to the test set and compare them with the upper and lower control lines, so as to calculate the average number of samples required from the start of the instrument out-of-control to the actual monitoring of the out-of-control by the quality control model, that is, the average number of samples required for error detection ANPed.
  • another test set can be obtained, wherein the other test set includes actual test results for target items of multiple patient samples and patient information, wherein the actual test results are obtained by testing the patient samples by a testing instrument, and the patient information includes the patient's disease, age, gender, and at least one of the departments to which the samples belong, wherein the other test set includes data on loss of control of the testing instrument.
  • the additional test set can be used to obtain the upper and lower control lines and the average number of samples required for error detection under the false alarm rate for each SPC parameter value, in the following manner: the actual test results of the patient samples in the training set for the target item and the patient information are used in the quality control model with the SPC parameter value to calculate the monitoring index under the SPC parameter value, and the upper and lower control lines are obtained based on the monitoring index and the false alarm rate under the SPC parameter value; the actual test results of the patient samples in the additional test set for the target item are input into the quality control model with the SPC parameter value together with the corresponding patient information to obtain the monitoring index corresponding to the additional test set and compare it with the upper and lower control lines, so as to calculate the average number of samples required from the instrument out of control to the quality control model actually monitoring the quality control, that is, the average number of samples required for error detection ANPed.
  • the actual test results of the patient samples in the training set for the target item can be truncated and optionally normalized before the machine
  • a fixed truncation ratio may be set for truncation processing.
  • the parameters of the truncation process and the parameters of the calculation model may be optimized in the following manner:
  • Step S331b giving a false alarm rate FAR
  • Step S332b providing a plurality of truncation ratios of the truncation process and a plurality of SPC parameter values of the calculation model;
  • Step S333b for each combination of each cutoff ratio and each SPC parameter value, obtain the upper and lower control lines and the number of samples required for the average error detection under the false alarm rate, in the following manner:
  • the actual test results in the training set and the patient information are used to calculate the monitoring index under the parameter value combination, and the upper and lower control lines are obtained based on the monitoring index under the parameter value combination and the false alarm rate,
  • a simulated data set containing multiple simulated instrument out-of-control or a real data set containing multiple real instrument out-of-control is used to calculate the monitoring index of the presence of instrument out-of-control under the parameter value combination, and the monitoring index is compared with the upper and lower control lines to calculate the average number of samples required from the start of the simulated instrument out-of-control or the real instrument out-of-control at each location to the actual monitoring of the simulated instrument out-of-control or the real instrument out-of-control by the quality control model, that is, the average number of samples required for error detection,
  • the simulated data set is obtained by adding errors to the actual test results in the training set at different time points for multiple times, and the real data set is obtained from the data of the multiple patient samples or by obtaining the actual test results of the target items of multiple other patient samples and their patient information;
  • Step S334b select the parameter value combination with the smallest number of samples required for average error detection and the upper and lower control lines corresponding to the parameter value combination to construct an optimized quality control model.
  • the values of other parameters of the quality control model are selected to be fixed, for example, selected based on experience.
  • a simulated data set containing multiple simulated instrument out-of-control situations is used to calculate the average number of samples required for error detection ANPed, that is: the actual test results of the target items of the patient samples in the training set and the patient information are used in the quality control model having the parameter value combination to calculate the monitoring index under the parameter value, and the upper and lower control lines are obtained based on the monitoring index and the false alarm rate under the parameter value; errors are added to the actual test results of the target items of the patient samples in the training set at different time points for multiple times, and after the errors are added, the actual test results of the target items of the patient samples in the training set together with the corresponding patient information are input into the quality control model having the parameter value combination to obtain the monitoring index after the error is added and compared with the upper and lower control lines, so as to calculate the average number of samples required from the start of the error addition to the actual detection of the error by the quality control model, that is, the average number of samples required for error detection ANPed.
  • steps S331b, S332b, S333b and S334b may be repeated, and then the minimum average error detection required sample number is selected from the average error detection required sample numbers under multiple false alarm rates (e.g., 0.001%, 0.01%, 0.1%, 1%, 3%, 5%), thereby obtaining a parameter value combination corresponding to the minimum average error detection required sample number and upper and lower control lines corresponding to the parameter value combination, so as to construct an optimized quality control model.
  • multiple false alarm rates e.g., 0.001%, 0.01%, 0.1%, 1%, 3%, 5%
  • the parameter of the calculation model based on the floating mean method is a sliding window.
  • the parameters to be optimized of the quality control model include the sliding window and the cutoff ratio.
  • the false alarm rate is given to be 0.1%
  • the parameter values of the sliding window are given to be 5, 10, 15, 20, 50 and 100
  • the parameter values of the truncation ratio of the given truncation processing are ⁇ 0%, ⁇ 1%, ⁇ 2%, ⁇ 5%.
  • the training set is used to calculate the monitoring indicators under different parameter value combinations of the sliding window and the cutoff ratio (there are 24 parameter value combinations in this example), and then the upper limit is calculated based on the monitoring indicators and the false alarm rate.
  • Lower control line for example, using the actual test results of the target items of N patient samples and patient information to calculate the monitoring indicators, the number of false alarms is N*0.1%, and the number of false alarms above and below is N*0.1%/2. Based on this, the upper control line and the lower control line are found, and there are N*0.1%/2 monitoring indicators exceeding the upper control line and the lower control line respectively.). Then, errors are randomly added to multiple places in the training set, the number of samples required for error detection each time is calculated, and then the average is taken to obtain the average number of samples required for error detection, so as to find the parameter value combination that meets the given false alarm rate and the minimum average number of samples required for error detection.
  • the test set can be used to obtain the upper and lower control lines and the average number of samples required for error detection under the false alarm rate for each parameter value combination, in the following manner: the actual test results of the target items of the patient samples in the training set and the patient information are used in the quality control model with the parameter value combination to calculate the monitoring indicators under the parameter value combination, and the upper and lower control lines are obtained based on the monitoring indicators and the false alarm rate under the parameter value combination, and the actual test results of the target items of the patient samples in the test set together with the corresponding patient information are input into the quality control model with the parameter value combination to obtain the monitoring indicators corresponding to the test set and compare them with the upper and lower control lines, so as to calculate the average number of samples required from the start of instrument quality control to the actual monitoring of the quality control by the quality control model, that is, the average number of samples required for error detection ANPed.
  • another test set can be obtained, wherein the other test set includes actual test results for target items of multiple patient samples and patient information, wherein the actual test results are obtained by testing the patient samples by a testing instrument, and the patient information includes the patient's disease, age, gender, and at least one of the departments to which the samples belong, wherein the other test set includes data on loss of control of the testing instrument.
  • the additional test set can be used to obtain the upper and lower control lines and the average number of samples required for error detection under the false alarm rate for each parameter value combination, in the following manner: in the quality control model with the parameter value combination, the actual test results of the patient samples in the training set for the target items and the patient information are used to calculate the monitoring indicators under the parameter value combination, and the upper and lower control lines are obtained based on the monitoring indicators and the false alarm rate under the parameter value combination, and the actual test results of the patient samples in the additional test set for the target items together with the corresponding patient information are input into the quality control model with the parameter value combination to obtain the monitoring indicators corresponding to the additional test set and compare them with the upper and lower control lines, so as to calculate the average number of samples required from the start of the instrument out of control to the actual monitoring of the quality control by the quality control model, that is, the average number of samples required for error detection ANPed.
  • the detection instrument when using the above-mentioned quality control model for real-time quality control, can be judged to be out of control and an alarm prompt can be output when at least N monitoring indicators among M consecutive monitoring indicators (obtained in chronological order) exceed the upper and lower control lines, wherein M and N are both natural numbers greater than 1 and M is greater than N.
  • M and N may also be parameters to be optimized for the quality control model, that is, the parameters of the calculation model and M, N, and the optional parameters of the truncation process are optimized in the following manner:
  • each parameter value of M for each SPC parameter value, each parameter value of M, each parameter value of N, and optionally each parameter value combination of each cutoff ratio, obtain the upper and lower control lines and the number of samples required for the average error detection at the false alarm rate, in the following manner:
  • the actual test results in the training set and the patient information are used to calculate the monitoring index under the parameter value combination, and the upper and lower control lines are obtained based on the monitoring index under the parameter value combination and the false alarm rate,
  • a simulated data set containing multiple simulated instrument out-of-control or a real data set containing multiple real instrument out-of-control is used to calculate the monitoring index of the presence of instrument out-of-control under the parameter value combination, and the monitoring index is compared with the upper and lower control lines to calculate the average number of samples required from the start of the simulated instrument out-of-control or the real instrument out-of-control at each location to the actual monitoring of the simulated instrument out-of-control or the real instrument out-of-control by the quality control model, that is, the average number of samples required for error detection,
  • the simulated data set is obtained by adding errors to the actual test results in the training set at different time points for multiple times, and the real data set is obtained from the data of the multiple patient samples or by obtaining multiple other
  • the values of other parameters of the quality control model are selected to be fixed, for example, selected based on experience.
  • the truncation ratio of the truncation process can be selected to be fixed.
  • steps S331c, S332c, S333c and S334c can be repeated, and then the minimum average error detection required sample number is selected from the average error detection required sample number under multiple false alarm rates (e.g., 0.001%, 0.01%, 0.1%, 1%, 3%, 5%), so as to obtain a parameter value combination corresponding to the minimum average error detection required sample number and an upper and lower control line corresponding to the parameter value combination, so as to construct an optimized quality control model.
  • multiple false alarm rates e.g., 0.001%, 0.01%, 0.1%, 1%, 3%, 5%
  • a simulated data set containing multiple simulated instrument out-of-control situations is used to calculate the average number of samples required for error detection ANPed, that is: the actual test results in the training set and the patient information are used in the quality control model having the parameter value combination to calculate the monitoring index under the parameter value, and the upper and lower control lines are obtained based on the monitoring index under the parameter value and the false alarm rate; errors are added to the actual test results of the target items of the patient samples in the training set at different time points for multiple times, and after the errors are added, the actual test results of the target items of the patient samples in the training set are input into the quality control model having the parameter value combination together with the corresponding patient information to obtain the monitoring index after the error is added and compare it with the upper and lower control lines, so as to calculate the average number of samples required from the start of the error addition to the actual detection of the error by the quality control model, that is, the average number of samples required for error detection ANPed.
  • the parameters of the calculation model based on the floating mean method are sliding windows.
  • the parameters to be optimized of the quality control model include sliding windows, cutoff ratios, and M and N.
  • the false alarm rate is given to be 0.1%
  • the parameter values of the sliding window are given to be 10, 20, 50, 70, and 100
  • the parameter values of the cutoff ratio of the truncation treatment are given to be ⁇ 0%, ⁇ 1%, ⁇ 2%, ⁇ 5%
  • the parameter values of M and N are given as shown in Table 4.
  • the monitoring indicators under different parameter value combinations of the sliding window, cutoff ratio, and M and N are calculated using the training set, and then the upper and lower control lines are calculated based on the monitoring indicators and the false alarm rate.
  • the test set can be used to obtain the upper and lower control lines and the average number of samples required for error detection under the false alarm rate for each parameter value combination, in the following manner: the actual test results of the target items of the patient samples in the training set and the patient information are used in the quality control model having the parameter value combination to calculate the monitoring indicators under the parameter value combination, and the upper and lower control lines are obtained based on the monitoring indicators and the false alarm rate under the parameter value combination, and the actual test results of the target items of the patient samples in the test set together with the corresponding patient information are input into the quality control model having the parameter value combination to obtain the monitoring indicators corresponding to the test set and compare them with the upper and lower control lines, so as to calculate the average number of samples required from the start of the instrument out of control to the actual monitoring of the out of control by the quality control model, that is, the average number of samples required for error detection ANPed.
  • another test set can be obtained, wherein the other test set includes actual test results for target items of multiple patient samples and patient information, wherein the actual test results are obtained by testing the patient samples by a testing instrument, and the patient information includes the patient's disease, age, gender, and at least one of the departments to which the samples belong, wherein the other test set includes data on loss of control of the testing instrument.
  • the additional test set can be used to obtain the upper and lower control lines and the average number of samples required for error detection under the false alarm rate for each parameter value combination, in the following manner: the actual test results of the target items of the patient samples in the training set and the patient information are used in the quality control model with the parameter value combination to calculate the monitoring indicators under the parameter value combination, and the upper and lower control lines are obtained based on the monitoring indicators and the false alarm rate under the parameter value combination, and the actual test results of the target items of the patient samples in the additional test set together with the corresponding patient information are input into the quality control model with the parameter value combination to obtain the monitoring indicators corresponding to the additional test set and compare them with the upper and lower control lines, so as to calculate the average number of samples required from the start of the instrument out-of-control to the actual monitoring of the out-of-control by the quality control model, that is, the average number of samples required for error detection ANPed.
  • M and N may also be parameters to be optimized for the quality control model, and the parameters of the calculation model and M, N and the optional truncation processing parameters are optimized in the following manner:
  • the false alarm rate and the number of samples required for the average error detection are calculated for each control line position, each SPC parameter value, each alarm parameter value combination, and optionally each parameter value combination of each cutoff ratio, in the following manner:
  • the actual test results in the training set and the patient information are used to calculate the monitoring indicators under the parameter value combination, and the model false alarm rate is calculated based on the upper and lower control lines in the parameter value combination and the monitoring indicators under the parameter value combination,
  • a real data set or a real data set containing multiple real instrument out-of-control locations is used to calculate the monitoring index of instrument out-of-control under the parameter value combination, and the monitoring index is compared with the control line in the parameter value combination to calculate the average number of samples required from the start of the simulated instrument out-of-control or the real instrument out-of-control at each location to the actual monitoring of the simulated instrument out-of-control or the real instrument out-of-control by the quality control model, that is, the average number of samples required for error detection,
  • the simulated data set is obtained by adding errors to the actual test results in the training set at different time points for multiple times, and the real data set is obtained from the data of the multiple patient samples or by obtaining the actual test results of the target items of multiple other patient samples and their patient information;
  • the optimal combination of parameter values is determined based on the model false alarm rate of each parameter value combination and the number of samples required for average error detection to construct an optimized quality control model.
  • the parameter value combination with the smallest number of samples required for average error detection among the parameter value combinations with a model false alarm rate lower than a preset false alarm rate may be selected as the final optimized parameter value combination of the quality control model.
  • model false alarm rate and the number of samples required for average error detection of each parameter value combination may be weighted and then summed, and then the parameter value combination corresponding to the smallest sum value is selected.
  • the parameter of the calculation model based on the floating mean method is a sliding window.
  • the parameters to be optimized of the quality control model include sliding windows, cutoff ratios and M&N alarm parameter combinations.
  • S values are 0.001%, 0.01%, 0.1%, 1%, 3%, 5%
  • sliding window parameter values 10 20, 50, 70 and 100
  • truncation ratio parameter values of truncation processing ⁇ 0%, ⁇ 1%, ⁇ 2%, ⁇ 5% and given alarm parameter combinations as shown in Table 4.
  • the monitoring indicators under different parameter value combinations of upper and lower control line positions, sliding windows, cutoff ratios and alarm parameter combinations are calculated using the training set, and then the model false alarm rate is calculated based on the monitoring indicators and the upper and lower control line positions. Then, errors are randomly added to the training set at multiple locations, the number of samples required for error detection each time is calculated, and then the average number of samples required for error detection is obtained.
  • the parameter value combination whose model false alarm rate is lower than the preset false alarm rate the parameter value combination with the smallest number of samples required for average error detection is selected as the final optimized parameter value combination of the quality control model.
  • M and N may also be parameters to be optimized of the quality control model, and the control line of the quality control model, the parameters of the calculation model, and M, N and the optional parameters of the truncation process are optimized in the following manner:
  • the average number of samples required for error detection is obtained for each candidate parameter value combination, in which a simulated data set containing multiple simulated instrument out-of-control locations or a real data set containing multiple real instrument out-of-control locations is used in the quality control model having the candidate parameter value combination to calculate the monitoring index of the existence of instrument out-of-control under the candidate parameter value combination, and the monitoring index is compared with the control line in the candidate parameter value combination, so as to calculate the average number of samples required from the start of the simulated instrument out-of-control location or the real instrument out-of-control location to the actual monitoring of the simulated instrument out-of-control location or the real instrument out-of-control location by the quality control model, that is, the average number of samples required for error detection, wherein the simulated data set is obtained by adding errors to the actual detection results in the training set multiple times at different time points, and the real data set is obtained from the data of the multiple patient samples or by Acquire actual test results of target items and patient information of multiple additional patient samples; and
  • the candidate parameter value combination with the smallest number of samples required for average error detection is used to construct an optimized quality control model.
  • the values of other parameters of the quality control model are selected to be fixed, for example, selected based on experience.
  • the parameters of the calculation model, M, N, the truncation ratio of the truncation process can be selected to be fixed.
  • the quality control model in step S340, can be verified in the following manner, that is, the data of the test set is input into the quality control model constructed in step S330, and the corresponding monitoring indicators are calculated whether they are within the upper and lower control lines. If they exceed the upper and lower control lines, it means that the system has generated non-random deviations, the detection instrument is out of control, and an out-of-control alarm is generated; if the monitoring indicators do not exceed the upper and lower control lines, it means that the system is normal and the detection instrument is under control. If there is out-of-control state data of the instrument in the test set, it can be used directly to verify the effectiveness of the algorithm.
  • the allowable error can be added at a specified time in the test set to simulate the out-of-control state data of the detection instrument, thereby verifying the detection performance of the algorithm model.
  • the number of samples required for error detection of all simulations can be averaged to obtain the average number of samples required for error detection, and the performance of the quality control model can be verified using the average number of samples required for error detection.
  • quantizing the patient information of the patient samples in the training set may include: quantizing at least one of the patient information of the patient samples in the training set, especially at least one of the disease, gender, and department to which the sample belongs, into a matrix.
  • quantizing at least one type of patient information of the patient samples in the training set into matrices may include: quantizing each type of patient information of the at least one type of patient information of the patient samples in the training set into a one-dimensional matrix having multiple elements, wherein each element of the one-dimensional matrix represents a category, and the patient information belongs to at least one of the categories represented by the element.
  • quantizing the at least one patient information of the patient samples in the training set into a one-dimensional matrix having a plurality of elements may include:
  • Each type of patient information in the at least one type of patient information of the patient samples in the training set is quantified into a one-dimensional matrix consisting of 0 and 1 and having multiple elements, wherein the value of the element representing the category to which the patient information belongs is 1, and the values of the remaining elements are 0.
  • quantizing the patient information of the patient samples in the training set may include: quantizing the patient information of the patient samples in the training set to a fixed value, preferably to a fixed integer.
  • the patient information of the patient samples in the training set may be quantified by a table lookup method.
  • the patient information includes the patient's disease, age, gender, and the department to which the sample belongs.
  • the embodiment of the present application also relates to the application of the quality control model constructed according to the above-mentioned method 300 in the quality control of the detection instrument, wherein the actual test results and patient information of multiple patient samples for target items are obtained, wherein the actual test results are obtained by the detection instrument testing the patient samples, and the patient information includes the patient's disease, age, gender and at least one of the departments to which the samples belong; the patient information of the multiple patient samples is quantified and the quantified patient information is input into the quality control model together with the actual test results to obtain monitoring indicators. and judging whether the detection instrument is under control based on the monitoring index.
  • the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the embodiments of the present application may take the form of hardware embodiments, software embodiments, or embodiments in combination with software and hardware. Moreover, the embodiments of the present application may take the form of computer program products implemented on one or more computer-usable storage media (including disk storage and optical storage, etc.) that contain computer-usable program codes.
  • computer-usable storage media including disk storage and optical storage, etc.
  • each flow and/or box in the flow chart and/or block diagram and the combination of the flow chart and/or block diagram in the flow chart and/or block diagram can be realized by computer program operation.
  • These computer programs can be provided to operate on a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the operation performed by the processor of the computer or other programmable data processing device produces a device for realizing the function specified in one flow chart or multiple flows and/or one box or multiple boxes of the block chart.
  • These computer program operations may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the operations stored in the computer-readable memory produce a manufactured product including an operating device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.
  • These computer program operations may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the operations executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

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Abstract

本申请实施例涉及基于患者样本进行实时质量控制的方法、系统和构建用于基于患者样本进行实时质量控制的质控模型的方法。在检测仪器对患者样本进行针对目标项目的实时检测的过程中，获取患者样本的针对所述目标项目的实际检测结果以及患者信息，患者信息包括患者的疾病、年龄、性别以及样本所属科室中的至少一个；将患者信息进行量化并将量化后的患者信息输入机器学习模型中，以获得预测检测结果，机器学习模型通过多个已知患者样本的实际检测结果以及患者信息训练得到；基于实际检测结果以及预测检测结果获取监控指标；并且基于监控指标判断检测仪器是否受控和/或判断是否给出报警提示。由此获得的质量控制具有较高的稳定性和灵敏性。

Description

基于患者样本进行实时质量控制的方法、系统和构建质控模型的方法
交叉引用
本申请基于申请号为PCT/CN2023/097857、申请日为2023年06月01日的PCT国际专利申请提出，并要求该PCT国际专利申请的优先权，该PCT国际专利申请的全部公开内容通过引用并入本文。
技术领域
本申请涉及体外诊断仪器的质量控制领域，尤其是涉及基于患者样本进行实时质量控制的方法、系统和构建用于基于患者样本进行实时质量控制的质控模型的方法。
背景技术
在临床诊断和治疗疾病过程中，医学检验质量直接影响医生对疾病的诊断和治疗。高质量的医学检验结果依赖于医学检验实验室完善的质量控制体系。医学检验实验室质量控制包括分析前、分析中、分析后的质量控制三个方面，其中，分析中的质量控制是质量管理体系中的最关键过程。分析中的质量控制包含室内质量控制(internal quality control，IQC)。目前常见的IQC实施是检验人员按照一定频率检测质控品，使用统计学理论计算评估检测结果的可靠程度，进而观测和排除检测中的失控因素。然而，使用质控品进行IQC监控存在一些典型缺陷：1)使用质控品进行IQC监控的过程不是连续的，而是在单次时间点进行检测估计分析过程是否失控；2)质控品不是患者样本，因此存在基质效应，影响IQC结果；3)使用质控品进行IQC需要额外的质控品、试剂、人力等成本投入，成本较高。
发明内容
为了至少部分地解决上述技术问题，本申请的任务在于提供一种改进的基于PBRTQC的技术方案，其能够实现较为稳定且较为灵敏的室内质量控制。
为了实现本申请的上述任务，本申请第一方面提供一种基于患者样本进行实时质量控制的方法，包括：
在检测仪器对当前患者样本进行针对目标项目的实时检测的过程中，获取当前患者样本的由所述检测仪器针对所述目标项目所检测得到的实际检测结果以及患者信息，其中，所述患者信息包括患者的疾病、年龄、性别以及所述目标项目的开单科室中的至少一个；
将所述当前患者样本的患者信息进行量化并将量化后的所述患者信息输入预先构建好的机器学习模型中，以获得所述机器学习模型的输出结果作为针对所述目标项目的预测检测结果，其中，所述机器学习模型通过多个历史患者样本的针对所述目标项目的实际检测结果以及患者信息训练得到；
基于所述当前患者样本的实际检测结果以及所述预测检测结果获取监控指标；并且
基于所述监控指标判断所述检测仪器是否受控，如果否，则输出表明所述检测仪器失控的报警提示。
为了实现本申请的上述任务，本申请第二方面提供一种基于患者样本进行实时质量控制的计算机系统，包括：
数据获取模块，配置为在检测仪器对当前患者样本进行针对目标项目的实时检测的过程中获取所述当前患者样本的由所述检测仪器针对所述目标项目所检测得到的实际检测结果以及患者信息，其中，所述患者信息包括患者的疾病、年龄、性别以及所述目标项目的开单科室中的至少一个；
预测结果获取模块，配置为将所述当前患者样本的患者信息进行量化并将量化后的所述患者信息输入预先构建好的机器学习模型中，以获得所述机器学习模型的输出结果作为针对所述目标项目的预测检测结果，其中，所述机器学习模型通过多个历史患者样本的针对所述目标项目的实际检测结果以及患者信息训练得到；
监控指标获取模块，配置为基于所述当前患者样本的实际检测结果以及所述预测检测结果获取监控指标；并且
质控模块，配置为，基于所述监控指标判断所述检测仪器是否受控，如果否，。
为了实现本申请的上述任务，本申请第三方面提供一种构建用于基于患者样本进行实时质量控制的质控模型的方法，包括：
获取多个患者样本的数据并将所述数据划分为训练集和测试集，所述数据包括所述多个患者样本的针对目标项目的实际检测结果以及患者信息，其中，所述实际检测结果由检测仪器对所述患者样本进行检测而得到，所述患者信息包括患者的疾病、年龄、性别以及所述目标项目的开单科室中的至少一个，其中，所述训练集中的数据是在所述检测仪器处于受控状态下时获得的；
使用所述训练集建立机器学习模型，其方式为将所述训练集中的患者样本的患者信息进行量化，并在量化后的所述患者信息和所述训练集中的实际检测结果之间建立满足如下公式的机器学习模型，
Xt＝f(x1t,x2t,…,xnt)+εt
其中，Xt是所述训练集中的实际检测结果，f(x1t,x2t,...,xnt)是机器学习模型，x1t,x2t,…,xnt代表量化后的所述患者信息，εt表示使用所述机器学习模型估计Xt的残差；
基于所述机器学习模型构建用于基于患者样本进行实时质量控制的质控模型；以及
使用所述测试集对所述质控模型进行验证。
在本申请各方面提供的技术方案中，使用基于历史患者样本的实际检测结果和患者信息训练得到的机器学习模型来获取用于对检测仪器进行质控的监控指标。由此，不仅能够克服使用质控品进行质控的缺点，而且相比于现有技术的PBRTQC能够实现更稳定且更灵敏的室内质量控制。
附图说明
图1为按照本申请实施例的基于患者样本进行实时质量控制的方法的示意性流程图。
图2为获取误差检出所需样本数的示意性图。
图3为按照本申请实施例获得的监控指标与按照现有技术获得的监控指标的示意图。
图4为按照本申请实施例的基于患者样本进行实时质量控制的计算机系统的示意性框图。
图5为按照本申请实施例的构建用于基于患者样本进行实时质量控制的质控模型的方法的示意性流程图。
图6为按照本申请实施例的神经网络模型的一种结构示意图。
图7为按照本申请实施例的优化质控模型的方法的示意性流程图。
图8为按照本申请实施例的优化质控模型的另一方法的示意性流程图。
图9为按照本申请实施例的优化质控模型的再另一方法的示意性流程图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请的一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
近年来，基于患者样本的实时质量控制(patient-based real-time quality control，PBRTQC)由于其特性可以弥补目前IQC的部分缺陷。PBRTQC的优点在于：1)能够实时使用患者样本监控系统，及时发现仪器失控状态；2)由于使用患者样本，不存在基质效应的影响；3)PBRTQC是分析软件，不需要额外的质控品、试剂、人力成本，成本较低。
随着PBRTQC的研究和应用的深入，发现现有的PBRTQC对某些常规指标的误差检测性能不足，不能很好满足临床的IQC要求。
例如，对于患有疾病的患者，其参数结果波动较大，现有的PBRTQC需要大量连续样本才能识别出误差及系统失控状态，无法满足临床的IQC要求。尤其是，本申请的发明人发现，在针对常规指标如白细胞计数、红细胞计数、血红蛋白含量、促甲状腺激素等，由于患者的疾病、年龄、性别等因素使得参数指标分布和波动较大，导致现有的PBRTQC方法检测系统平均误差检出所需样本数较多。
因此，本申请提出基于患者样本和神经网络进行实时质量监控的技术方案，以便降低患者的疾病、年龄、性别等因素对PBRTQC质控性能的影响(即，提高质控稳定性)，同时相比于现有的PBRTQC质控减少误差检出所需样本数(即，提高灵敏度)。
图1示出按照本申请实施例的基于患者样本进行实时质量控制的方法100。如图1所示，方法100包括数据获取步骤S110、数据处理步骤S120、监控指标获取步骤S130以及质控步骤S140。
在数据获取步骤S110中，在检测仪器对当前患者样本进行针对目标项目的实时检测的过程中，也就是说实时地，获取所述当前患者样本的由所述检测仪器针对所述目标项目所检测得到的实际检测结果以及患者信息，其中，所述患者信息包括患者的疾病、年龄、性别以及样本所属科室中的至少一个。
在本申请实施例中，所述样本所述科室是指，所述目标项目的开单科室，即，请求使用所述检测仪器针对所述目标项目检测所述当前患者样本的医院科室。
在数据处理步骤S120中，将所述当前患者样本的患者信息进行量化并将量化后的所述患者信息输入预先构建好的机器学习模型中，以获得所述机器学习模型的输出结果作为针对所述目标项目的预测检测结果，其中，所述机器学习模型通过多个已知患者样本(即历史患者样本)的针对所述目标项目的实际检测结果以及患者信息训练得到。在此，本申请实施例提出的机器学习模型表征患者信息与目标项目的检测结果之间的关系。也就是说，如果将患者信息输入到该机器学习模型中，则将会得到由该机器学习模型预测的针对所述目标项目的预测检测结果。
在监控指标获取步骤S130中，基于在步骤S110获得的当前患者样本的实际检测结果以及在步骤S120中获得的预测检测结果获取监控指标。
在质控步骤S140中，基于所述监控指标判断所述检测仪器是否受控和/或基于所述监控指标判断是否给出表明检测仪器失控的报警提示。例如，当基于所述监控指标判断所述检测仪器不受控、即失控时，输出表明所述检测仪器失控的报警提示。
采用在相同的假报警率下的误差检出所需样本数NPed来验证本申请的基于PBRTQC的质控方法的性能与现有技术的基于PBRTQC的质控方法的性能，误差检出所需样本数越少说明误差检测的灵敏性能越好。其中，假报警率FAR指的是，基于在检测仪器受控状态下获得的数据计算的监控指标的假报警数(超出预设控制线的数目)/样本数*100％。误差检出所需样本数NPed指的是，从检测仪器产生失控到监控指标超出预设控制线所需要的样本数目，如图2所示。
以目标项目为白细胞计数WBC、红细胞计数RBC、血红蛋白含量HGB和促甲状腺激素TSH为例进行验证，分别采用在检测仪器受控状态下获得的WBC验证数据集、RBC验证数据集、HGB验证数据集和TSH验证数据集，通过多次添加误差的方式对本申请的基于PBRTQC的质控方法和现有技术的基于PBRTQC的质控方法进行仿真，得到如表1所示的平均误差检出所需样本数ANPed。如表1所示，本申请提出的基于PBRTQC的质控方法相比于现有技术显著提高了质控灵敏度。
表1不同目标项目的ANPed
此外，在相同的报警率的情况下，以目标项目为白细胞计数WBC为例，分别基于在检测仪器受控状态下获得的WBC数据集计算本申请实施例的监控指标NNs-PBRTQC和现有PBRTQC的监控指标PBRTQC，如图3所示。由图3可见，按照本申请实施例计算得到的监控指标比按照现有PBRTQC计算得到的监控指标更加稳定，而且在相同的报警率的情况下，按照本申请实施例获得的控制线比按照现有PBRTQC获得的控制线更小。
在本申请实施例中，检测仪器受控是指检测仪器状态正常，没有发生故障；而检测仪器失控是指检测仪器状态异常，可能发生故障。
在一些实施例中，机器学习模型可以为神经网络模型。在另一些实施例中，所述机器学习模型可以为基于SVM(支持向量机)和/或LDA(支持向量机)的机器学习模型。
在一些实施例中，所述目标项目可以为血常规检测项目，例如细胞计数(如白细胞计数、血小板计数、红细胞计数等)和分类(如白细胞分类等)、血红蛋白含量等；相应地，所述检测仪器为血细胞分析仪。
在另一些实施例中，所述目标项目可以为生化检测项目，例如甲状腺功能(例如促甲状腺激素等)、肝功能、肾功能、血脂、血糖等；相应地，所述检测仪器为生化分析仪。
在一些实施例中，所述患者信息可以包括患者的疾病、年龄、性别以及样本所属科室中的多个。优选地，所述患者信息可以包括患者的疾病、年龄、性别以及样本所属科室。
在一些实施例中，所述患者信息可以从检测仪器所在的医院的实验室(检验科)信息系 统获取。
在一些实施例中，基于所述实际检测结果以及所述预测检测结果获取监控指标的步骤S130可以包括：
计算所述实际检测结果与所述预测检测结果的差值；并且
将所述差值输入到基于过程控制SPC算法的计算模型中，以获得所述计算模型的输出结果作为所述监控指标。
所述过程控制SPC算法例如可以包括浮动均值、浮动中位数、指数加权移动平均、浮动标准差、浮动分位数、浮动非正常值患者数中的至少一个。
在另一些实施例中，基于所述实际检测结果以及所述预测检测结果获取监控指标的步骤S130也可以包括基于所述实际检测结果与所述预测检测结果的比例获取所述监控指标。
在一些实施例中，将所述当前患者样本的患者信息进行量化可以包括：将所述当前患者样本的患者信息中的至少一种患者信息、尤其是疾病、性别以及样本所属科室中的至少一个分别量化为矩阵。
在一个示例中，将所述当前患者样本的患者信息中的至少一种患者信息分别量化为矩阵可以包括：将所述当前患者样本的所述至少一种患者信息中的每种患者信息量化为具有多个元素的一维矩阵，其中，所述一维矩阵的每个元素分别代表一个类别，该种患者信息属于由所述元素代表的类别中的至少一个。
例如，当所述患者信息包括疾病信息时，如果疾病可以被分为33种疾病类别，则可以将所述疾病信息量化为具有33个元素的一维矩阵，其中，每个元素代表一种疾病。
又例如，当所述患者信息包括性别信息时，由于性别分为2个类别、即男性和女性，则可以将所述性别信息量化为具有2个元素的一维矩阵，其中，一个元素代表男性、另一个元素代表女性。
再例如，当所述患者信息包括样本所属科室的信息时，如果样本所属科室可以被分为32个科室类别，则可以将所述样本所属科室的信息量化为具有32个元素的一维矩阵，其中，每个元素代表一个科室。
优选地，将所述当前患者样本的所述至少一种患者信息中的每种患者信息量化为具有多个元素的一维矩阵可以包括：将所述当前患者样本的所述至少一种患者信息中的每种患者信息量化为具有多个元素的由0和1组成的一维矩阵，其中，代表该种信息所属类别的元素的值为1，其余元素的值为0。由此简化机器学习模型的构建。
例如，当所述患者信息包括疾病信息时，如果疾病可以被分为33种疾病类别，则可以将所述疾病信息量化为具有33个元素的一维矩阵[X1，X2，…，X33]，其中，每个元素X代表一种疾病，例如X1代表甲状腺功能亢进症、X2代表高血压等。如果某个当前患者样本的疾病信息为甲状腺功能亢进症，则将该患者样本的疾病信息量化为[1,0,0,0，…，0]，即元素X1的值为1，其余元素的值为0。如果某个当前患者样本的疾病信息包括甲状腺功能亢进症和高血压，则将该患者样本的疾病信息量化为[1,1,0,0，…，0]，即元素X1和X2的值为1，其余元素的值为0。
又例如，当所述患者信息包括性别信息时，由于性别分为2个类别、即男性和女性，则可以将所述性别信息量化为具有2个元素的一维矩阵[Y1，Y2]，其中，元素Y1代表男性，元素Y2代表女性。如果某个患者样本的性别信息为男性，则将该患者样本的性别信息量化为[1,0]，即元素Y1的值为1，元素Y2的值为0。
再例如，当所述患者信息包括样本所属科室的信息时，如果样本所属科室可以被分为32个科室类别，则可以将所述样本所属科室的信息量化为具有32个元素的一维矩阵[Z1，Z2，…，Z32]，其中，每个元素代表一个科室,例如Z1代表内分泌科、Z2代表肾脏科等。如果某个患者样本的样本所属科室的信息为内分泌科，则将该患者样本的样本所属科室的信息量化为[1,0,0,0，…，0]，即元素Z1的值为1，其余元素的值为0。
在另一些实施例中，将所述当前患者样本的患者信息进行量化可以包括：将所述当前患者样本的患者信息中的至少一种患者信息、尤其是年龄分别量化为固定值、优选量化为固定整数。
在一个示例中，可以将年龄量化为0至150的固定数值，即，如果患者的年龄为30，则将其年龄量化为30。在其他示例中，可以将年龄量化为百分比。例如，如果患者的年龄为30，则将其年龄量化为30/100。
在一个示例中，可以将性别量化为第一正整数或第二正整数，其中，第一正整数代表男性，第二正整数代表女性。例如第一正整数为1，且第二正整数为2，但本申请不限于此。
备选地或附加地，可以通过查表法将所述当前患者样本的患者信息中的至少一种患者信息进行量化。由此能够简化机器学习模型的构建。
在一个示例中，可以为各种不同的疾病配设相应的不同的正整数，并将疾病与相应正整数的配设以查找表的形式预先存储。因此，在对患者的疾病进行量化时，通过查找表搜索与该患者的疾病对应的正整数。表2示出用于量化疾病的查找表的一个示例。
表2用于量化疾病的查找表
在一个示例中，也可以为各种不同的科室配设相应的不同的正整数，并将科室与相应正整数的配设以查找表的形式预先存储。因此，在对患者样本所属科室进行量化时，通过查找表搜索与该患者样本所属科室对应的正整数。表3示出用于量化科室的查找表的一个示例。
表3用于量化科室的查找表

在一些实施例中，在步骤S120中，也可以将所述多个已知患者样本中在特定时刻之前的n个患者样本(n的取值例如为检测仪器的每日测试通量)的针对目标项目的实际检测结果的平均值作为所述机器学习模型的变量，以便减小例如由对检测仪器的校准产生的日间影响以及由试剂产生的日间影响。
所述机器学习模型通过多个已知患者样本(即历史患者样本)的针对目标项目的实际检测结果以及患者信息训练得到。在此，所述机器学习模型也可以进一步表征患者信息、所述多个已知患者样本的实际检测结果的均值与目标项目的检测结果之间的关系。也就是说，如果将患者信息输入到该机器学习模型中，则将会得到由该机器学习模型预测的针对目标项目的预测检测结果。
在一些实施例中，在步骤S120中，即，基于所述实际检测结果以及所述预测检测结果获取监控指标可以包括
对所述实际检测结果进行数据处理，其中，所述数据处理包括截断处理和/或正态化处理；并且
基于经过数据处理的所述实际检测结果以及所述预测检测结果获取所述监控指标。
然而，在本发明实施例中，由于采用机器学习模型，可以不对所述实际检测结果进行正态化。由此简化数据处理。
在一些实施例中，在步骤S140中，可以在一个所述监控指标超出预设控制线时就判断所述检测仪器失控并输出报警提示。
在另一些实施例中，在步骤S140中，可以在(按时间顺序先后获得的)连续M个监控指标超出预设控制线时才判断所述检测仪器失控并输出报警提示，其中M为大于1的自然数。相比于在一个监控指标超出预设控制线时就报警的情况，能够降低假报警率。
在又另一些实施例中，在步骤S140中，可以按时间顺序先后获取连续第一数量M的当前患者样本的监控指标，并基于所述连续第一数量M的当前患者样本的监控指标判断所述检测仪器是否受控；当(按时间顺序先后获得的)连续第一数量M的当前患者的监控指标中至少有第二数量N的当前患者样本的监控指标超出预设控制线时才判断所述检测仪器失控并输出表明所述检测仪器失控的报警提示，其中，第一数量M和第二数量N均为大于1的自然数且第一数量M大于第二数量N。由此能够显著区分假报警和真报警。相比于在一个监控指标超出预设控制线时就报警的情况，能够降低假报警率，同时误差检出所需样本数尽可能保持少。
在一些实施例中，在步骤S140中，当基于所述监控指标判断所述检测仪器失控时，可以自动调取针对目标项目的质控品至所述检测仪器检测，以获取质控品的检测结果；基于该质控品的检测结果判断所述检测仪器是否失控。也就是说，在基于实际患者样本的检测结果判断检测仪器失控时，进一步使用质控品来测试检测仪器，以判断检测仪器是否真的失控。
在一些实施例中，可以通过如下方式对所述计算模型的参数进行优化：
获取多个历史患者样本的针对所述目标项目的实际检测结果以及患者信息，其中，所述实际检测结果由所述检测仪器在受控状态下对所述多个历史患者样本进行检测而得到；
给定假报警率；
给定所述计算模型的多个SPC参数值；
针对每个SPC参数值求取在所述假报警率下的上下控制线和平均误差检出所需样本数，其方式为：
在具有该SPC参数值的所述计算模型中使用所述多个历史患者样本的实际检测结果以及患者信息计算在该SPC参数值下的监控指标，并基于在该SPC参数值下的监控指标和所述假报警率获取所述上下控制线，
在具有该SPC参数值的所述计算模型中使用包含多处模拟仪器失控的模拟数据集或包含多处真实仪器失控的真实数据集来计算在该SPC参数值下的存在仪器失控的监控指标，并将其与所述上下控制线进行对比，以便计算从各处模拟仪器失控或真实仪器失控开始到实际监测到该模拟仪器失控或真实仪器失控所需要的平均样本数、即平均误差检出所需样本数，
其中，所述模拟数据集通过多次在不同时间点为所述多个历史患者样本的实际检测结果添加误差而获得，所述真实数据集从所述多个历史患者样本的实际检测结果以及患者信息获得或者通过获取多个另外的历史患者样本的针对所述目标项目的实际检测结果及其患者信息来获得，以及
选择平均误差检出所需样本数最小的SPC参数值以及与该SPC参数值对应的上下控制线，以构建优化的所述计算模型。
在另一些实施例中，可以通过如下方式对所述截断处理的参数和所述计算模型的参数进行优化：
获取多个历史患者样本的针对所述目标项目的实际检测结果以及患者信息，其中，所述实际检测结果由所述检测仪器在受控状态下对所述历史患者样本进行检测而得到；
给定假报警率；
给定所述截断处理的多个截断比例和所述计算模型的多个SPC参数值；
针对每个截断比例和每个SPC参数值的每个参数值组合求取在所述假报警率下的上下控制线和平均误差检出所需样本数，其方式为：
在具有该参数值组合的所述计算模型中使用所述多个历史患者样本的实际检测结果以及患者信息计算在该参数值组合下的监控指标，并基于在该参数值组合下的监控指标和所述假报警率获取所述上下控制线，
在具有该参数值组合的所述计算模型中使用包含多处模拟仪器失控的模拟数据集或包含多处真实仪器失控的真实数据集来计算在该参数值组合下的存在仪器失控的监控指标，并将其与所述上下控制线进行对比，以便计算从各处模拟仪器失控或真实仪器失控开始到实际监测到该模拟仪器失控或真实仪器失控所需要的平均样本数、即平均误差检出所需样本数，
其中，所述模拟数据集通过多次在不同时间点为所述多个历史患者样本的实际检测结果添加误差而获得，所述真实数据集从所述多个历史患者样本的实际检测结果以及患者信息获得或者通过获取多个另外的历史患者样本的针对所述目标项目的实际检测结果及其患者信息来获得；以及
选择平均误差检出所需样本数最小的参数值组合以及与该参数值组合对应的上下控制线，以构建优化的所述截断处理和所述计算模型。
图4为按照本申请实施例的基于患者样本进行实时质量控制的计算机系统200的示意性框图。如图4所示，计算机系统200包括数据获取模块210、预测结果获取模块220、监控指标获取模块230和质控模块240。
数据获取模块210配置为在检测仪器对当前患者样本进行针对目标项目的实时检测的过程中、即实时地获取所述当前患者样本的由所述检测仪器针对所述目标项目所检测得到的实际检测结果以及患者信息，其中，所述患者信息包括患者的疾病、年龄、性别以及样本所属科室中的至少一个。
预测结果获取模块220配置为将所述当前患者样本的患者信息进行量化并将量化后的所述患者信息输入预先构建好的机器学习模型中，以获得所述机器学习模型的输出结果作为针对所述目标项目的预测检测结果，其中，所述机器学习模型通过多个已知患者样本的针对所述目标项目的实际检测结果以及患者信息训练得到。
监控指标获取模块230配置为基于所述当前患者样本的实际检测结果以及所述预测检测结果获取监控指标。
质控模块240配置为基于所述监控指标判断所述检测仪器是否受控和/或基于所述监控指标判断是否给出表明检测仪器失控的报警提示。当基于所述监控指标判断所述检测仪器失控时，质控模块240输出表明所述检测仪器失控的报警提示。
本申请实施例提出的计算机系统200可以以硬件的形式、例如以处理器的形式实现，也可以以软件的形式实现、例如为能在计算机上安装并执行的软件。本申请对此不作具体限定。
在一些实施例中，机器学习模型可以为神经网络模型。在另一些实施例中，所述机器学习模型可以为基于SVM(支持向量机)和/或LDA(支持向量机)的机器学习模型。
在一些实施例中，所述目标项目可以为血常规检测项目，例如细胞计数(如白细胞计数、血小板计数、红细胞计数等)和分类(如白细胞分类等)、血红蛋白含量等；相应地，所述检测仪器为血细胞分析仪。
在另一些实施例中，所述目标项目可以为生化检测项目，例如甲状腺功能(例如促甲状腺激素等)、肝功能、肾功能、血脂、血糖等；相应地，所述检测仪器为生化分析仪。
在一些实施例中，所述患者信息可以包括患者的疾病、年龄、性别以及样本所属科室中的多个。优选地，所述患者信息可以包括患者的疾病、年龄、性别以及样本所属科室。
在一些实施例中，所述患者信息可以从检测仪器所在的医院的实验室(检验科)信息系统获取。
在一些实施例中，监控指标获取模块230可以进一步配置为：
计算所述实际检测结果与所述预测检测结果的差值；并且
将所述差值输入到基于过程控制SPC算法的计算模型中，以获得所述计算模型的输出结果作为所述监控指标。
所述过程控制SPC算法例如可以包括浮动均值、浮动中位数、指数加权移动平均、浮动标准差、浮动分位数、浮动非正常值患者数中的至少一个。
在一些实施例中，预测结果获取模块220可以进一步配置为将所述当前患者样本的患者信息中的至少一种患者信息、尤其是疾病、性别以及样本所属科室中的至少一个分别量化为矩阵。
进一步地，预测结果获取模块220可以进一步配置为将所述当前患者样本的所述至少 一种患者信息中的每种患者信息量化为具有多个元素的一维矩阵，其中，所述一维矩阵的每个元素分别代表一个类别，该种患者信息属于由所述元素代表的类别中的至少一个。
优选地，预测结果获取模块220可以进一步配置为将所述当前患者样本的所述至少一种患者信息中的每种患者信息量化为具有多个元素的由0和1组成的一维矩阵，其中，代表该种患者信息所属类别的元素的值为1，其余元素的值为0。
在一些实施例中，预测结果获取模块220可以进一步配置为将所述当前患者样本的患者信息中的至少一种患者信息、尤其是年龄量化为固定值、优选量化为固定整数。
备选地或附加地，预测结果获取模块220可以进一步配置为通过查表法将所述当前患者样本的患者信息中的至少一种患者信息进行量化。由此能够简化机器学习模型的构建。
关于具体的量化示例，可以参考以上对方法100的各个实施例的详细描述，在此不再赘述。
在一些实施例中，监控指标获取模块230可以进一步配置为：
对所述实际检测结果进行数据处理，其中，所述数据处理包括截断处理和/或正态化处理；并且
基于经过数据处理的所述实际检测结果以及所述预测检测结果获取所述监控指标。
然而，在本发明实施例中，由于采用机器学习模型，可以不对所述实际检测结果进行正态化。由此简化数据处理。
在一些实施例中，质控模块240可以进一步配置为，在一个所述监控指标超出预设控制线时就判断所述检测仪器失控并输出报警提示。
在另一些实施例中，质控模块240可以进一步配置为，在(按时间顺序先后获得的)连续M个监控指标超出预设控制线时才判断所述检测仪器失控并输出报警提示，其中M为大于1的自然数。相比于在一个监控指标超出预设控制线时就报警的情况，能够降低假报警率。
在又另一些实施例中，质控模块240可以进一步配置为，按时间顺序先后获取连续第一数量M的当前患者样本的监控指标，基于所述连续第一数量M的当前患者样本的监控指标判断所述检测仪器是否受控；在(按时间顺序先后获得的)连续第一数量M的当前患者样本的监控指标中有至少有第二数量N的当前患者样本的监控指标超出预设控制线时才判断所述检测仪器失控并输出表明所述检测仪器失控的报警提示，其中，第一数量M和第二数量N均为大于1的自然数且第一数量M大于第二数量N。由此能够显著区分假报警和真报警。相比于在一个监控指标超出预设控制线时就报警的情况，能够降低假报警率，同时误差检出所需样本数尽可能保持少。
在一些实施例中，质控模块240可以进一步配置为，当基于所述监控指标判断所述检测仪器失控时，可以自动调取针对目标项目的质控品至所述检测仪器检测，以获取质控品的检测结果；基于该质控品的检测结果判断所述检测仪器是否失控。
在一些实施例中，所述监控指标获取模块可以通过如下方式被优化：
获取多个历史患者样本的针对所述目标项目的实际检测结果以及患者信息，其中，所述实际检测结果由所述检测仪器在受控状态下对所述多个历史患者样本进行检测而得到；
给定假报警率；
给定所述计算模型的多个SPC参数值；
针对每个SPC参数值求取在所述假报警率下的上下控制线和平均误差检出所需样本 数，其方式为：
在具有该SPC参数值的所述计算模型中使用所述多个历史患者样本的实际检测结果以及患者信息计算在该SPC参数值下的监控指标，并基于在该SPC参数值下的监控指标和所述假报警率获取所述上下控制线，
在具有该SPC参数值的所述计算模型中使用包含多处模拟仪器失控的模拟数据集或包含多处真实仪器失控的真实数据集来计算在该SPC参数值下的存在仪器失控的监控指标，并将其与所述上下控制线进行对比，以便计算从各处模拟仪器失控或真实仪器失控开始到实际监测到该模拟仪器失控或真实仪器失控所需要的平均样本数、即平均误差检出所需样本数，
其中，所述模拟数据集通过多次在不同时间点为所述多个历史患者样本的实际检测结果添加误差而获得，所述真实数据集从所述多个历史患者样本的实际检测结果以及患者信息获得或者通过获取多个另外的历史患者样本的针对所述目标项目的实际检测结果及其患者信息来获得，以及
选择平均误差检出所需样本数最小的SPC参数值以及与该SPC参数值对应的上下控制线，以构建优化的所述计算模型。
在另一些实施例中，所述监控指标获取模块可以通过如下方式被优化：
获取多个历史患者样本的针对所述目标项目的实际检测结果以及患者信息，其中，所述实际检测结果由所述检测仪器在受控状态下对所述历史患者样本进行检测而得到；
给定假报警率；
给定所述截断处理的多个截断比例和所述计算模型的多个SPC参数值；
针对每个截断比例和每个SPC参数值的每个参数值组合求取在所述假报警率下的上下控制线和平均误差检出所需样本数，其方式为：
在具有该参数值组合的所述计算模型中使用所述多个历史患者样本的实际检测结果以及患者信息计算在该参数值组合下的监控指标，并基于在该参数值组合下的监控指标和所述假报警率获取所述上下控制线，
在具有该参数值组合的所述计算模型中使用包含多处模拟仪器失控的模拟数据集或包含多处真实仪器失控的真实数据集来计算在该参数值组合下的存在仪器失控的监控指标，并将其与所述上下控制线进行对比，以便计算从各处模拟仪器失控或真实仪器失控开始到实际监测到该模拟仪器失控或真实仪器失控所需要的平均样本数、即平均误差检出所需样本数，
其中，所述模拟数据集通过多次在不同时间点为所述多个历史患者样本的实际检测结果添加误差而获得，所述真实数据集从所述多个历史患者样本的实际检测结果以及患者信息获得或者通过获取多个另外的历史患者样本的针对所述目标项目的实际检测结果及其患者信息来获得；以及
选择平均误差检出所需样本数最小的参数值组合以及与该参数值组合对应的上下控制线，以构建优化的所述截断处理和所述计算模型。
本申请实施例提供的方法100的各个实施例及其优点可相应地转用到本申请实施例提供的计算机系统200上，因此不再赘述。
图5示出按照本申请实施例的构建用于基于患者样本进行实时质量控制的质控模型的方法300，包括下列步骤：
S310，获取多个患者样本的数据并将所述数据划分为训练集和测试集(例如可以按照时间先后划分为训练集和测试集)，所述数据包括所述多个患者样本的针对目标项目的实际检测结果以及患者信息，其中，所述实际检测结果由检测仪器对所述患者样本进行检测而得到，所述患者信息包括患者的疾病、年龄、性别以及样本所属科室中的至少一个，其中，所述训练集中的数据是在所述检测仪器处于受控状态下时获得的。在此，例如获取在一定时间内按照时间顺序被检测仪器检测的多个患者样本、尤其是所有患者样本的针对目标项目的实际检测结果以及患者信息。
在步骤S310中，训练集和测试集的样本比例可设置为6:4或7:3等。
S320，使用所述训练集建立机器学习模型，其方式为将所述训练集中的患者样本的患者信息进行量化，并在量化后的所述患者信息和所述训练集中的实际检测结果之间建立满足如下公式的机器学习模型，
Xt＝f(x1t,x2t,...,xnt)+εt
其中，Xt是所述训练集中的实际检测结果，f(x1t,x2t,...,xnt)是机器学习模型，x1t,x2t,...,xnt代表量化后的所述患者信息，εt表示使用所述机器学习模型估计Xt的残差。也就是说，使用检测仪器受控的患者历史检测结果和患者信息学习回归出机器学习模型f，此时的εt近似为均值为0的随机分布。
S330，基于所述机器学习模型构建用于基于患者样本进行实时质量控制的质控模型。
S340，使用所述测试集对所述质控模型进行验证，以便验证所述质控模型的性能。
由于机器学习模型f是一种人工神经网络非线性函数，其将疾病、年龄、性别等因素带来的偏差进行合理估计，剔除此类影响参数波动的因素后，采用基于机器学习模型构建的质控模型获得的监控指标稳定，与现有技术的PBRTQC相比提高了系统误差检测性能。
当检测仪器处于正常受控状态时，那么残差εt随机波动且平方和误差最小。在一些实施例中可以基于此原则建立损失函数min∑‖εt‖2，通过将训练样本、即上述多个患者样本的实际检测结果以及患者信息作为输入，计算损失函数在最小情况下的机器学习模型f(x1t,x2t,…,xnt)。
在一些实施例中，机器学习模型可以为神经网络模型，其中，神经网络模型的结构如图6所示。在另一些实施例中，所述机器学习模型可以为基于SVM(支持向量机)和/或LDA(支持向量机)的机器学习模型。
在一些实施例中，在步骤S330中，基于所述机器学习模型构建用于基于患者样本进行实时质量控制的质控模型可以包括：根据所述机器学习模型和基于过程控制SPC算法的计算模型构建所述质控模型，其中，将实际检测结果与机器学习模型f所预测的检测结果之间的差值、即残差εt输入到基于过程控制SPC算法的计算模型中，以获得所述计算模型的输出作为所述质控模型的监控指标。
然而，在其他实施例中，也可以通过其他方式、例如求比例的方式从实际检测结果与机器学习模型f所预测的检测结果计算得到所述质控模型的监控指标。
在一些实施例中，在步骤S320中，也可以将所述测试集中的在特定时刻之前的n个患者样本(n的取值例如为检测仪器的每日测试通量)的针对目标项目的实际检测结果的平均值作为所述机器学习模型的变量，以便减小例如由对检测仪器的校准产生的日间影响以及由试剂产生的日间影响。
进一步地，在步骤S330中，给定假报警率并基于所述监控指标和所述假报警率获取所述质控模型的上下控制线。在此，在使用上述质控模型进行实时质控的过程中，如果检 测仪器产生失控，则残差εt会产生非随机特征，通过基于过程控制SPC算法的计算后，监控指标会超出上控制线或下控制线，因而产生报警，提示检测仪器失控。
在一些实施例中，所述过程控制SPC算法可以包括浮动均值、浮动中位数、指数加权移动平均、浮动标准差、浮动分位数、浮动非正常值患者数中的至少一个。
在一些实施例中，如图7所示，在步骤S330中可以通过如下方式对所述计算模型的参数进行优化：
步骤S331a，给定假报警率FAR；
步骤S332a，给定所述计算模型的多个SPC参数值；
步骤S333a，针对每个SPC参数值求取在所述假报警率下的上下控制线和平均误差检出所需样本数，其方式为：
在具有该SPC参数值的所述质控模型中使用所述训练集中的实际检测结果以及患者信息计算在该SPC参数值下的监控指标，并基于在该SPC参数值下的监控指标和所述假报警率获取所述上下控制线，
在具有该SPC参数值的所述质控模型中使用包含多处模拟仪器失控的模拟数据集或包含多处真实仪器失控的真实数据集来计算在该参数值下的存在仪器失控的监控指标，并将其与所述上下控制线进行对比，以便计算从各处模拟仪器失控或真实仪器失控开始到所述质控模型实际监测到该模拟仪器失控或真实仪器失控所需要的平均样本数、即平均误差检出所需样本数ANPed，其中，所述模拟数据集通过多次在不同时间点为所述训练集中的实际检测结果添加误差而获得，所述真实数据集从所述多个患者样本的数据获得或者通过获取多个另外的患者样本的针对目标项目的实际检测结果及其患者信息来获得，以及
步骤S334a，选择平均误差检出所需样本数最小的SPC参数值以及与该SPC参数值对应的上下控制线，以构建优化的质控模型。
在此，假报警率直接决定了上下控制线，假报警率通常由用户结合使用情况人为设定，如设定假报警率为0.1％。一旦确定了假报警率，则也可以确定上下控制线。
在此，可以理解地，在对所述计算模型的参数进行优化的过程中，质控模型的其他参数、例如截断比例的值选择为固定的，例如依据经验来选取。
优选地，使用包含多处模拟仪器失控的模拟数据集计算平均误差检出所需样本数ANPed，即：在具有该SPC参数值的所述质控模型中使用所述训练集中的实际检测结果以及患者信息计算在该SPC参数值下的监控指标，并基于在该参数值下的监控指标和所述假报警率获取所述上下控制线；多次在不同时间点为所述训练集中的患者样本的针对目标项目的实际检测结果添加误差，在添加误差之后将所述训练集中的患者样本的针对目标项目的实际检测结果连同相应的患者信息输入具有该SPC参数值的所述质控模型中，以获取添加误差之后的监控指标并将其与所述上下控制线进行对比，以便计算从添加误差开始到所述质控模型实际监测到该误差所需要的平均样本数、即平均误差检出所需样本数ANPed。
以过程控制SPC算法为浮动均值法为例，基于浮动均值法的计算模型的参数为滑动窗口。给定假报警率为0.1％，并且给定滑动窗口的参数值为5、10、15、20、50和100。使用训练集计算在不同的滑动窗口下的监控指标，然后基于监控指标和假报警率计算上下控制线(例如，使用N个患者样本的针对目标项目的实际检测结果以及患者信息计算监控指标，则假报警数为N*0.1％个，上下各假报警数为N*0.1％/2个，据此找到如上控制线和下 控制线，分别存在N*0.1％/2个监控指标超出该上控制线和下控制线。)。接着在训练集随机多处添加误差，计算每次的误差检出所需样本数，然后取平均得到平均误差检出所需样本数，从而求出满足给定的假报警率和最小平均误差检出所需样本数的参数值。
在一些实施例中，可以重复步骤S331a、S332a、S333a和S334a，然后从多个假报警率FAR(例如，0.001％、0.01％、0.1％、1％、3％、5％)下的各个平均误差检出所需样本数中选择最小平均误差检出所需样本数ANPedmin，从而得到与该最小平均误差检出所需样本数对应的参数值以及与该参数值对应的上下控制线，以构建优化的质控模型。当然，在其他实施例中，也可以对假报警率FAR和在该假报警率FAR下的最小平均误差检出所需样本数ANPedmin进行加权、然后求和，即sum＝FAR*a+ANPedmin*b,其中，a为假报警率FAR的权重，b为最小平均误差检出所需样本数ANPedmin的权重。计算各个假报警率FAR以及相应假报警率FAR下的最小平均误差检出所需样本数ANPedmin的求和值,然后选择求和值最小所对应的最小平均误差检出所需样本数及其对应的参数值。
在另一些实施例中，如果所述测试集中存在检测仪器失控的数据，则可以使用所述测试集作为所述真实数据集针对每个SPC参数值求取在所述假报警率下的上下控制线和平均误差检出所需样本数，其方式为：在具有该SPC参数值的所述质控模型中使用所述训练集中的患者样本的针对目标项目的实际检测结果以及患者信息计算在该SPC参数值下的监控指标，并基于在该SPC参数值下的监控指标和所述假报警率获取所述上下控制线；将所述测试集中的患者样本的针对目标项目的实际检测结果连同相应的患者信息输入具有该SPC参数值的所述质控模型，以获取所述测试集对应的监控指标并将其与所述上下控制线进行对比，以便计算从仪器失控开始到所述质控模型实际监测到该失控所需要的平均样本数、即平均误差检出所需样本数ANPed。
在又另一些实施例中，可以获取另外的测试集，所述另外的测试集包括多个患者样本的针对目标项目的实际检测结果以及患者信息，其中，所述实际检测结果由检测仪器对所述患者样本进行检测而得到，所述患者信息包括患者的疾病、年龄、性别以及样本所属科室中的至少一个，其中，所述另外的测试集包括检测仪器失控的数据。在此，可以使用所述另外的测试集针对每个SPC参数值求取在所述假报警率下的上下控制线和平均误差检出所需样本数，其方式为：在具有该SPC参数值的所述质控模型中使用所述训练集中的患者样本的针对目标项目的实际检测结果以及患者信息计算在该SPC参数值下的监控指标，并基于在该SPC参数值下的监控指标和所述假报警率获取所述上下控制线；将所述另外的测试集中的患者样本的针对目标项目的实际检测结果连同相应的患者信息输入具有该SPC参数值的所述质控模型，以获取所述另外的测试集对应的监控指标并将其与所述上下控制线进行对比，以便计算从仪器失控开始到所述质控模型实际监测到该质控所需要的平均样本数、即平均误差检出所需样本数ANPed。在一些实施例中，可以在建立机器学习模型之前对所述训练集中的患者样本的针对目标项目的实际检测结果进行截断处理以及可选的正态变换。
在一些实施例中，可以设置固定的截断比例进行截断处理。
在一些实施例中，如图8所示，可以通过如下方式对所述截断处理的参数和所述计算模型的参数进行优化：
步骤S331b,给定假报警率FAR；
步骤S332b,给定所述截断处理的多个截断比例和所述计算模型的多个SPC参数值；
步骤S333b,针对每个截断比例和每个SPC参数值的每个参数值组合求取在所述假报警率下的上下控制线和平均误差检出所需样本数，其方式为：
在具有该参数值组合的所述质控模型中使用所述训练集中的实际检测结果以及患者信息计算在该参数值组合下的监控指标，并基于在该参数值组合下的监控指标和所述假报警率获取所述上下控制线，
在具有该参数值组合的所述质控模型中使用包含多处模拟仪器失控的模拟数据集或包含多处真实仪器失控的真实数据集来计算在该参数值组合下的存在仪器失控的监控指标，并将其与所述上下控制线进行对比，以便计算从各处模拟仪器失控或真实仪器失控开始到所述质控模型实际监测到该模拟仪器失控或真实仪器失控所需要的平均样本数、即平均误差检出所需样本数，
其中，所述模拟数据集通过多次在不同时间点为所述训练集中的实际检测结果添加误差而获得，所述真实数据集从所述多个患者样本的数据获得或者通过获取多个另外的患者样本的针对目标项目的实际检测结果及其患者信息来获得；以及
步骤S334b,选择平均误差检出所需样本数最小的参数值组合以及与该参数值组合对应的上下控制线，以构建优化的质控模型。
在此，可以理解地，在对所述截断处理的参数和所述计算模型的参数进行优化的过程中，质控模型的其他参数的值选择为固定的，例如依据经验来选取。
优选地，使用包含多处模拟仪器失控的模拟数据集计算平均误差检出所需样本数ANPed，即：在具有该参数值组合的所述质控模型中使用所述训练集中的患者样本的针对目标项目的实际检测结果以及患者信息计算在该参数值下的监控指标，并基于在该参数值下的监控指标和所述假报警率获取所述上下控制线；多次在不同时间点为所述训练集中的患者样本的针对目标项目的实际检测结果添加误差，在添加误差之后将所述训练集中的患者样本的针对目标项目的实际检测结果连同相应的患者信息输入具有该参数值组合的所述质控模型，以获取添加误差之后的监控指标并将其与所述上下控制线进行对比，以便计算从添加误差开始到所述质控模型实际监测到该误差所需要的平均样本数、即平均误差检出所需样本数ANPed。
同样地，在一些实施例中，可以重复步骤S331b、S332b、S333b和S334b，然后从多个假报警率(例如，0.001％、0.01％、0.1％、1％、3％、5％)下的各个平均误差检出所需样本数中选择最小平均误差检出所需样本数，从而得到与该最小平均误差检出所需样本数对应的参数值组合以及与该参数值组合对应的上下控制线，以构建优化的质控模型。当然，在其他实施例中，也可以对假报警率FAR和在该假报警率FAR下的最小平均误差检出所需样本数ANPedmin进行加权、然后求和，即sum＝FAR*a+ANPedmin*b,其中，a为假报警率FAR的权重，b为最小平均误差检出所需样本数ANPedmin的权重。计算各个假报警率FAR以及相应假报警率FAR下的最小平均误差检出所需样本数ANPedmin的求和值,然后选择求和值最小所对应的最小平均误差检出所需样本数及其对应的参数值组合。
以过程控制SPC算法为浮动均值法为例，基于浮动均值法的计算模型的参数为滑动窗口。所述质控模型的待优化参数包括滑动窗口和截断比例。给定假报警率为0.1％，并且给定滑动窗口的参数值为5、10、15、20、50和100以及给定截断处理的截断比例的参数值为±0％、±1％、±2％、±5％。使用训练集计算在滑动窗口与截断比例的不同参数值组合(在该示例中一共有24个参数值组合)下的监控指标，然后基于监控指标和假报警率计算上 下控制线(例如，使用N个患者样本的针对目标项目的实际检测结果以及患者信息计算监控指标，则假报警数为N*0.1％个，上下各假报警数为N*0.1％/2个，据此找到如上控制线和下控制线，分别存在N*0.1％/2个监控指标超出该上控制线和下控制线。)。接着在训练集随机多处添加误差，计算每次的误差检出所需样本数，然后取平均得到平均误差检出所需样本数，从而求出满足给定的假报警率和最小平均误差检出所需样本数的参数值组合。
在一些实施例中，如果所述测试集中存在检测仪器失控的数据，则可以使用所述测试集针对每个参数值组合求取在所述假报警率下的上下控制线和平均误差检出所需样本数，其方式为：在具有该参数值组合的所述质控模型中使用所述训练集中的患者样本的针对目标项目的实际检测结果以及患者信息计算在该参数值组合下的监控指标，并基于在该参数值组合下的监控指标和所述假报警率获取所述上下控制线，将所述测试集中的患者样本的针对目标项目的实际检测结果连同相应的患者信息输入具有该参数值组合的所述质控模型，以获取所述测试集对应的监控指标并将其与所述上下控制线进行对比，以便计算从仪器质控开始到所述质控模型实际监测到该质控所需要的平均样本数、即平均误差检出所需样本数ANPed。
在又另一些实施例中，可以获取另外的测试集，所述另外的测试集包括多个患者样本的针对目标项目的实际检测结果以及患者信息，其中，所述实际检测结果由检测仪器对所述患者样本进行检测而得到，所述患者信息包括患者的疾病、年龄、性别以及样本所属科室中的至少一个，其中，所述另外的测试集包括检测仪器失控的数据。在此，可以使用所述另外的测试集针对每个参数值组合求取在所述假报警率下的上下控制线和平均误差检出所需样本数，其方式为：在具有该参数值组合的所述质控模型中使用所述训练集中的患者样本的针对目标项目的实际检测结果以及患者信息计算在该参数值组合下的监控指标，并基于在该参数值组合下的监控指标和所述假报警率获取所述上下控制线，将所述另外的测试集中的患者样本的针对目标项目的实际检测结果连同相应的患者信息输入具有该参数值组合的所述质控模型，以获取所述另外的测试集对应的监控指标并将其与所述上下控制线进行对比，以便计算从仪器失控开始到所述质控模型实际监测到该质控所需要的平均样本数、即平均误差检出所需样本数ANPed。
在一些实施例中，在使用上述质控模型进行实时质控时，可以在(按时间顺序先后获得的)连续M个监控指标中至少有N个监控指标超出上下控制线时才判断检测仪器失控并输出报警提示，其中，M和N均为大于1的自然数且M大于N。
在一些实施例中，如图9所示，M和N也可以为质控模型的待优化参数，即通过如下方式对所述计算模型的参数以及M、N以及可选的截断处理的参数进行优化：
S331c,给定假报警率；
S332c，给定所述计算模型的多个SPC参数值、M的多个参数值、N的多个参数值以及可选地所述截断处理的多个截断比例；
S333c，针对每个SPC参数值、M的每个参数值、N的每个参数值以及可选地每个截断比例的每个参数值组合求取在所述假报警率下的上下控制线和平均误差检出所需样本数，其方式为：
在具有该参数值组合的所述质控模型中使用所述训练集中的实际检测结果以及患者信息计算在该参数值组合下的监控指标，并基于在该参数值组合下的监控指标和所述假报警率获取所述上下控制线，
在具有该参数值组合的所述质控模型中使用包含多处模拟仪器失控的模拟数据集或包含多处真实仪器失控的真实数据集来计算在该参数值组合下的存在仪器失控的监控指标，并将其与所述上下控制线进行对比，以便计算从各处模拟仪器失控或真实仪器失控开始到所述质控模型实际监测到该模拟仪器失控或真实仪器失控所需要的平均样本数、即平均误差检出所需样本数，
其中，所述模拟数据集通过多次在不同时间点为所述训练集中的实际检测结果添加误差而获得，所述真实数据集从所述多个患者样本的数据获得或者通过获取多个另
外的患者样本的针对目标项目的实际检测结果及其患者信息来获得；以及
S334c，选择平均误差检出所需样本数最小的参数值组合以及与该参数值组合对应的上下控制线，以构建优化的质控模型。
在此可以理解的，在对所述计算模型的参数以及M、N以及可选的截断处理的参数进行优化时，质控模型的其他参数的值选择为固定的，例如依据经验来选取。例如，当对所述计算模型的参数以及M、N进行优化时，截断处理的截断比例可以选择为固定的。
同样地，在一些实施例中，可以重复步骤S331c、S332c、S333c和S334c，然后从多个假报警率(例如，0.001％、0.01％、0.1％、1％、3％、5％)下的各个平均误差检出所需样本数中选择最小平均误差检出所需样本数，从而得到与该最小平均误差检出所需样本数对应的参数值组合以及与该参数值组合对应的上下控制线，以构建优化的质控模型。当然，在其他实施例中，也可以对假报警率FAR和在该假报警率FAR下的最小平均误差检出所需样本数ANPedmin进行加权、然后求和，即sum＝FAR*a+ANPedmin*b,其中，a为假报警率FAR的权重，b为最小平均误差检出所需样本数ANPedmin的权重。计算各个假报警率FAR以及相应假报警率FAR下的最小平均误差检出所需样本数ANPedmin的求和值,然后选择求和值最小所对应的最小平均误差检出所需样本数及其对应的参数值组合。
优选地，使用包含多处模拟仪器失控的模拟数据集计算平均误差检出所需样本数ANPed，即：在具有该参数值组合的所述质控模型中使用所述训练集中的实际检测结果以及患者信息计算在该参数值下的监控指标，并基于在该参数值下的监控指标和所述假报警率获取所述上下控制线；多次在不同时间点为所述训练集中的患者样本的针对目标项目的实际检测结果添加误差，在添加误差之后将所述训练集中的患者样本的针对目标项目的实际检测结果连同相应的患者信息输入具有该参数值组合的所述质控模型中，以获取添加误差之后的监控指标并将其与所述上下控制线进行对比，以便计算从添加误差开始到所述质控模型实际监测到该误差所需要的平均样本数、即平均误差检出所需样本数ANPed。
以过程控制SPC算法为浮动均值法为例，基于浮动均值法的计算模型的参数为滑动窗口。所述质控模型的待优化参数包括滑动窗口、截断比例和M、N。给定假报警率为0.1％，并且给定滑动窗口的参数值为10、20、50、70和100、给定截断处理的截断比例的参数值为±0％、±1％、±2％、±5％以及给定如表4所示的M与N的参数值。使用训练集计算在滑动窗口、截断比例和M、N的不同参数值组合下的监控指标，然后基于监控指标和假报警率计算上下控制线。接着在训练集随机多处添加误差，计算每次的误差检出所需样本数，然后取平均得到平均误差检出所需样本数。重新给定不同的多个假报警率，例如0.001％、0.01％、1％、3％、5％)等，重复上述过程，从而求出满足最小平均误差检出所需样本数的参数值组合。
表4报警参数的参数值
在另一些实施例中，如果所述测试集中存在检测仪器失控的数据，则可以使用所述测试集针对每个参数值组合求取在所述假报警率下的上下控制线和平均误差检出所需样本数，其方式为：在具有该参数值组合的所述质控模型中使用所述训练集中的患者样本的针对目标项目的实际检测结果以及患者信息计算在该参数值组合下的监控指标，并基于在该参数值组合下的监控指标和所述假报警率获取所述上下控制线，将所述测试集中的患者样本的针对目标项目的实际检测结果连同相应的患者信息输入具有该参数值组合的所述质控模型，以获取所述测试集对应的监控指标并将其与所述上下控制线进行对比，以便计算从仪器失控开始到所述质控模型实际监测到该失控所需要的平均样本数、即平均误差检出所需样本数ANPed。
在又另一些实施例中，可以获取另外的测试集，所述另外的测试集包括多个患者样本的针对目标项目的实际检测结果以及患者信息，其中，所述实际检测结果由检测仪器对所述患者样本进行检测而得到，所述患者信息包括患者的疾病、年龄、性别以及样本所属科室中的至少一个，其中，所述另外的测试集包括检测仪器失控的数据。在此，可以使用所述另外的测试集针对每个参数值组合求取在所述假报警率下的上下控制线和平均误差检出所需样本数，其方式为：在具有该参数值组合的所述质控模型中使用所述训练集中的患者样本的针对目标项目的实际检测结果以及患者信息计算在该参数值组合下的监控指标，并基于在该参数值组合下的监控指标和所述假报警率获取所述上下控制线，将所述另外的测试集中的患者样本的针对目标项目的实际检测结果连同相应的患者信息输入具有该参数值组合的所述质控模型，以获取所述另外的测试集对应的监控指标并将其与所述上下控制线进行对比，以便计算从仪器失控开始到所述质控模型实际监测到该失控所需要的平均样本数、即平均误差检出所需样本数ANPed。
在另一些未示出的实施例中，M和N也可以为质控模型的待优化参数，通过如下方式对所述计算模型的参数以及M、N以及可选的截断处理的参数进行优化：
给定所述质控模型的多个控制线位置、所述计算模型的多个SPC参数值、M和N的多个报警参数值组合以及可选地所述截断处理的多个截断比例；
针对每个控制线位置、每个SPC参数值、每个报警参数值组合以及可选地每个截断比例的每个参数值组合求取假报警率和平均误差检出所需样本数，其方式为：
在具有该参数值组合的所述质控模型中使用所述训练集中的实际检测结果以及患者信息计算在该参数值组合下的监控指标，并基于该参数值组合中的上下控制线和在该参数值组合下的监控指标计算模型假报警率，
在具有该参数值组合的所述质控模型中使用包含多处模拟仪器失控的模拟数据 集或包含多处真实仪器失控的真实数据集来计算在该参数值组合下的存在仪器失控的监控指标，并将其与该参数值组合中的控制线进行对比，以便计算从各处模拟仪器失控或真实仪器失控开始到所述质控模型实际监测到该模拟仪器失控或真实仪器失控所需要的平均样本数、即平均误差检出所需样本数，
其中，所述模拟数据集通过多次在不同时间点为所述训练集中的实际检测结果添加误差而获得，所述真实数据集从所述多个患者样本的数据获得或者通过获取多个另外的患者样本的针对目标项目的实际检测结果及其患者信息来获得；以及
根据各个参数值组合的模型假报警率和平均误差检出所需样本数来确定最优的参数值组合，以构建优化的质控模型。
在一些实施例中，可以将模型假报警率低于预设假报警率的参数值组合中平均误差检出所需样本数最小的参数值组合选择为质控模型的最终优化参数值组合。
在其他实施例中，也可以对各个参数值组合的模型假报警率和平均误差检出所需样本数进行加权、然后求和，然后选择求和值最小所对应的参数值组合。
以过程控制SPC算法为浮动均值法为例，基于浮动均值法的计算模型的参数为滑动窗口。所述质控模型的待优化参数包括滑动窗口、截断比例和M&N报警参数组合。给定上下控制线位置(监控指标的上下各S/2的百分数位置得到，S取值为0.001％、0.01％、0.1％、1％、3％、5％)、给定滑动窗口的参数值为10、20、50、70和100、给定截断处理的截断比例的参数值为±0％、±1％、±2％、±5％以及给定如表4所示的报警参数组合的参数值。使用训练集计算在上下控制线位置、滑动窗口、截断比例和报警参数组合的不同参数值组合下的监控指标，然后基于监控指标和上下控制线位置计算模型假报警率。接着在训练集随机多处添加误差，计算每次的误差检出所需样本数，然后取平均得到平均误差检出所需样本数。将模型假报警率低于预设假报警率的参数值组合中平均误差检出所需样本数最小的参数值组合选择为质控模型的最终优化参数值组合。
在又另一些未示出的实施例中，M和N也可以为质控模型的待优化参数，通过如下方式对所述质控模型的控制线、所述计算模型的参数以及M、N以及可选的截断处理的参数进行优化：
给定所述质控模型的预设假报警率、多个控制线位置、所述计算模型的多个SPC参数值、M和N的多个报警参数值组合以及可选地所述截断处理的多个截断比例；
针对每个控制线位置、每个SPC参数值、每个报警参数值组合以及可选地每个截断比例的每个参数值组合求取假报警率，其方式为：在具有该参数值组合的所述质控模型中使用所述训练集中的实际检测结果以及患者信息计算在该参数值组合下的监控指标，并基于该参数值组合中的控制线位置和在该参数值组合下的监控指标计算模型假报警率；
将模型假报警率小于所述预设假报警率的模型参数值组合选择为候选参数值组合；
针对每个候选参数值组合求取平均误差检出所需样本数，其方式为，在具有该候选参数值组合的所述质控模型中使用包含多处模拟仪器失控的模拟数据集或包含多处真实仪器失控的真实数据集来计算在该候选参数值组合下的存在仪器失控的监控指标，并将其与该候选参数值组合中的控制线进行对比，以便计算从各处模拟仪器失控或真实仪器失控开始到所述质控模型实际监测到该模拟仪器失控或真实仪器失控所需要的平均样本数、即平均误差检出所需样本数，其中，所述模拟数据集通过多次在不同时间点为所述训练集中的实际检测结果添加误差而获得，所述真实数据集从所述多个患者样本的数据获得或者通过 获取多个另外的患者样本的针对目标项目的实际检测结果及其患者信息来获得；以及
将平均误差检出所需样本数最小的候选参数值组合用于构建优化的质控模型。
在此，可以理解地，对所述质控模型的控制线、所述计算模型的参数以及M、N以及可选的截断处理的参数进行优化时，质控模型的其他参数的值选择为固定的，例如依据经验来选取。例如，对所述质控模型的控制线、所述计算模型的参数以及M、N进行优化时，截断处理的截断比例可以选择为固定的。
在一些实施例中，在步骤S340中，可以通过如下方式对质控模型进行验证，即，将测试集的数据输入在步骤S330中构建的质控模型，计算相应的监控指标是否在上下控制线之内，如果超出上下控制线则说明系统产生了非随机偏差，检测仪器失控，产生失控报警；如果监控指标未超出上下控制线，则说明系统正常、检测仪器受控。如果测试集中存在仪器失控状态数据，可以直接用来验证算法有效性。对针对测试集中不存在检测仪器失控状态数据的情况，可以在测试集中指定时刻开始添加允许误差，模拟检测仪器的失控状态数据，从而验证算法模型的检测性能。通过多次模拟(在测试集的不同时间点添加误差)可以对所有模拟的误差检出所需样本数求平均，得到平均误差检出所需样本数，利用该平均误差检出所需样本数来验证质控模型的性能。
在一些实施例中，在步骤S320中，将所述训练集中的患者样本的患者信息进行量化可以包括：将所述训练集中的患者样本的患者信息中的至少一种患者信息、尤其是疾病、性别以及样本所属科室中的至少一个分别量化为矩阵。
进一步地，将所述训练集中的患者样本的患者信息中的至少一种患者信息分别量化为矩阵可以包括：将所述训练集中的患者样本的所述至少一种患者信息中的每种患者信息量化为具有多个元素的一维矩阵，其中，所述一维矩阵的每个元素分别代表一个类别，该种患者信息属于由所述元素代表的类别中的至少一个。
优选地，将所述训练集中的患者样本的所述至少一种患者信息分别量化为具有多个元素的一维矩阵可以包括：
将所述训练集中的患者样本的所述至少一种患者信息中的每种患者信息量化为具有多个元素的由0和1组成的一维矩阵，其中，代表该种患者信息所属类别的元素的值为1，其余元素的值为0。
备选地或附加地，将所述训练集中的患者样本的患者信息进行量化可以包括：将所述训练集中的患者样本的患者信息量化为固定值、优选量化为固定整数。
备选地或附加地，可以通过查表法将所述训练集中的患者样本的患者信息进行量化。
关于具体的量化示例，可以参考以上对方法100的各个实施例的详细描述，在此不再赘述。
在一些实施例中，所述患者信息包括患者的疾病、年龄、性别以及样本所属科室。
本申请实施例提供的方法100和计算机系统300的各个实施例及其优点可相应地转用到本申请实施例提供的模型构建方法300上，因此不再赘述。
本申请实施例还涉及根据上述的方法300构建的质控模型在对检测仪器进行质控中的应用，其中，获取多个患者样本的针对目标项目的实际检测结果以及患者信息，其中，所述实际检测结果由检测仪器对所述患者样本进行检测而得到，所述患者信息包括患者的疾病、年龄、性别以及样本所属科室中的至少一个；将所述多个患者样本的患者信息进行量化并将量化后的患者信息连同所述实际检测结果一起输入所述质控模型中，以获取监控指 标；并且基于所述监控指标判断所述检测仪器是否受控。
本领域内技术人员应理解，本申请实施例可提供为方法、系统、或计算机程序产品。因此，本申请实施例可采用硬件实施例、软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请实施例可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括磁盘存储器和光学存储器等)上实施的计算机程序产品的形式。
本申请实施例是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序操作实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序操作到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的操作产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序操作也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的操作产生包括操作装置的制造品，该操作装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序操作也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的操作提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
以上在说明书、附图以及权利要求书中提及的特征或者特征组合，只要在本申请的范围内是有意义的并且不会相互矛盾，均可以任意相互组合使用或者单独使用。参考本申请实施例提供的方法所说明的优点和特征以相应的方式适用于本申请实施例提供的系统和应用，反之亦然。
以上所述仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是在本申请的发明构思下，利用本申请说明书及附图内容所作的等效变换方案，或直接/间接运用在其他相关的技术领域均包括在本申请的专利保护范围内。

Claims (37)

  1. 一种基于患者样本进行实时质量控制的方法，包括：
    在检测仪器对当前患者样本进行针对目标项目的实时检测的过程中，获取所述当前患者样本的由所述检测仪器针对所述目标项目所检测得到的实际检测结果以及患者信息，其中，所述患者信息包括患者的疾病、年龄、性别以及所述目标项目的开单科室中的至少一个；
    将所述当前患者样本的患者信息进行量化并将量化后的所述患者信息输入预先构建好的机器学习模型中，以获得所述机器学习模型的输出结果作为针对所述目标项目的预测检测结果，其中，所述机器学习模型通过多个历史患者样本的针对所述目标项目的实际检测结果以及患者信息训练得到；
    基于所述当前患者样本的实际检测结果以及所述预测检测结果获取监控指标；并且
    基于所述监控指标判断所述检测仪器是否受控，如果否，则输出表明所述检测仪器失控的报警提示。
  2. 根据权利要求1所述的方法，其中，基于所述当前患者样本的实际检测结果以及所述预测检测结果获取监控指标，包括：
    计算所述实际检测结果与所述预测检测结果的差值；并且
    将所述差值输入到基于过程控制SPC算法的计算模型中，以获得所述计算模型的输出结果作为所述监控指标。
  3. 根据权利要求1或2所述的方法，其中，将所述当前患者样本的患者信息进行量化，包括：
    将所述当前患者样本的患者信息中的至少一种患者信息、尤其是疾病、性别以及所述目标项目的开单科室中的至少一个分别量化为矩阵。
  4. 根据权利要求3所述的方法，其中，将所述当前患者样本的患者信息中的至少一种患者信息量分别化为矩阵，包括：
    将所述当前患者样本的所述至少一种患者信息中的每种患者信息量化为具有多个元素的一维矩阵，其中，所述一维矩阵的每个元素分别代表一个类别，该种患者信息属于由所述元素代表的类别中的至少一个；
    优选地，将所述当前患者样本的所述至少一种患者信息中的每种患者信息量化为具有多个元素的一维矩阵，包括：
    将所述当前患者样本的所述至少一种患者信息中的每种患者信息量化为具有多个元素的由0和1组成的一维矩阵，其中，代表该种患者信息所属类别的元素的值为1，其余元素的值为0。
  5. 根据权利要求1或2所述的方法，其中，将所述当前患者样本的患者信息进行量化，包括：
    将所述当前患者样本的患者信息中的至少一种患者信息、尤其是年龄分别量化为固定 值、优选量化为固定整数。
  6. 根据权利要求1至5中任一项所述的方法，其中，通过查表法将所述当前患者样本的患者信息中的至少一种患者信息进行量化。
  7. 根据权利要求1至6中任一项所述的方法，其中，所述患者信息包括患者的疾病、年龄、性别以及所述目标项目所属的开单科室。
  8. 根据权利要求1至7中任一项所述的方法，其中，基于所述实际检测结果以及所述预测检测结果获取监控指标，包括
    对所述实际检测结果进行数据处理，其中，所述数据处理包括截断处理和/或正态化处理；并且
    基于经过数据处理的所述实际检测结果以及所述预测检测结果获取所述监控指标。
  9. 根据权利要求1至8中任一项所述的方法，其中，所述机器学习模型为神经网络模型。
  10. 根据权利要求1至8中任一项所述的方法，其中，所述机器学习模型为基于SVM和/或LDA的机器学习模型。
  11. 根据权利要求1至10中任一项所述的方法，其中，基于所述监控指标判断所述检测仪器是否受控包括：按时间顺序先后获取连续第一数量的当前患者样本的监控指标，基于所述连续第一数量的当前患者样本的监控指标判断所述检测仪器是否受控；
    其中，当所述连续第一数量的当前患者样本的监控指标中至少有第二数量的当前患者样本的监控指标超过控制线时，输出表明所述检测仪器失控的报警提示，其中，所述第一数量大于所述第二数量并且所述第二数量大于1。
  12. 根据权利要求2所述的方法，其中，通过如下方式对所述计算模型的参数进行优化：
    获取多个历史患者样本的针对所述目标项目的实际检测结果以及患者信息，其中，所述实际检测结果由所述检测仪器在受控状态下对所述多个历史患者样本进行检测而得到；
    给定假报警率；
    给定所述计算模型的多个SPC参数值；
    针对每个SPC参数值求取在所述假报警率下的上下控制线和平均误差检出所需样本数，其方式为：
    在具有该SPC参数值的所述计算模型中使用所述多个历史患者样本的实际检测结果以及患者信息计算在该SPC参数值下的监控指标，并基于在该SPC参数值下的监控指标和所述假报警率获取所述上下控制线，
    在具有该SPC参数值的所述计算模型中使用包含多处模拟仪器失控的模拟数据集或包含多处真实仪器失控的真实数据集来计算在该SPC参数值下的存在仪器失控的监 控指标，并将其与所述上下控制线进行对比，以便计算从各处模拟仪器失控或真实仪器失控开始到实际监测到该模拟仪器失控或真实仪器失控所需要的平均样本数、即平均误差检出所需样本数，
    其中，所述模拟数据集通过多次在不同时间点为所述多个历史患者样本的实际检测结果添加误差而获得，所述真实数据集从所述多个历史患者样本的实际检测结果以及患者信息获得或者通过获取多个另外的历史患者样本的针对所述目标项目的实际检测结果及其患者信息来获得，以及
    选择平均误差检出所需样本数最小的SPC参数值以及与该SPC参数值对应的上下控制线，以构建优化的所述计算模型。
  13. 根据权利要求8所述的方法，其中，通过如下方式对所述截断处理的参数和所述计算模型的参数进行优化：
    获取多个历史患者样本的针对所述目标项目的实际检测结果以及患者信息，其中，所述实际检测结果由所述检测仪器在受控状态下对所述历史患者样本进行检测而得到；
    给定假报警率；
    给定所述截断处理的多个截断比例和所述计算模型的多个SPC参数值；
    针对每个截断比例和每个SPC参数值的每个参数值组合求取在所述假报警率下的上下控制线和平均误差检出所需样本数，其方式为：
    在具有该参数值组合的所述计算模型中使用所述多个历史患者样本的实际检测结果以及患者信息计算在该参数值组合下的监控指标，并基于在该参数值组合下的监控指标和所述假报警率获取所述上下控制线，
    在具有该参数值组合的所述计算模型中使用包含多处模拟仪器失控的模拟数据集或包含多处真实仪器失控的真实数据集来计算在该参数值组合下的存在仪器失控的监控指标，并将其与所述上下控制线进行对比，以便计算从各处模拟仪器失控或真实仪器失控开始到实际监测到该模拟仪器失控或真实仪器失控所需要的平均样本数、即平均误差检出所需样本数，
    其中，所述模拟数据集通过多次在不同时间点为所述多个历史患者样本的实际检测结果添加误差而获得，所述真实数据集从所述多个历史患者样本的实际检测结果以及患者信息获得或者通过获取多个另外的历史患者样本的针对所述目标项目的实际检测结果及其患者信息来获得；以及
    选择平均误差检出所需样本数最小的参数值组合以及与该参数值组合对应的上下控制线，以构建优化的所述截断处理和所述计算模型。
  14. 一种基于患者样本进行实时质量控制的计算机系统，包括：
    数据获取模块，配置为在检测仪器对当前患者样本进行针对目标项目的实时检测的过程中获取所述当前患者样本的由所述检测仪器针对所述目标项目所检测得到的实际检测结果以及患者信息，其中，所述患者信息包括患者的疾病、年龄、性别以及所述目标项目的开单科室中的至少一个；
    预测结果获取模块，配置为将所述当前患者样本的患者信息进行量化并将量化后的所述患者信息输入预先构建好的机器学习模型中，以获得所述机器学习模型的输出结果作为 针对所述目标项目的预测检测结果，其中，所述机器学习模型通过多个历史患者样本的针对所述目标项目的实际检测结果以及患者信息训练得到；
    监控指标获取模块，配置为基于所述当前患者样本的实际检测结果以及所述预测检测结果获取监控指标；并且
    质控模块，配置为，基于所述监控指标判断所述检测仪器是否受控，如果否，则输出表明检测仪器失控的报警提示。
  15. 根据权利要求14所述的计算机系统，其中，所述监控指标获取模块进一步配置为：
    计算所述实际检测结果与所述预测检测结果的差值；并且
    将所述差值输入到基于过程控制SPC算法的计算模型中，以获得所述计算模型的输出结果作为所述监控指标。
  16. 根据权利要求14或15所述的计算机系统，其中，所述预测结果获取模块进一步配置为将所述当前患者样本的患者信息中的至少一种患者信息、尤其是疾病、性别以及所述目标项目的开单科室中的至少一个分别量化为矩阵。
  17. 根据权利要求16所述的计算机系统，其中，所述预测结果获取模块进一步配置为将所述当前患者样本的所述至少一种患者信息中的每种患者信息量化为具有多个元素的一维矩阵，其中，所述一维矩阵的每个元素分别代表一个类别，该种患者信息属于由所述元素代表的类别中的至少一个；
    优选地，所述预测结果获取模块进一步配置为将所述当前患者样本的所述至少一种患者信息中的每种患者信息量化为具有多个元素的由0和1组成的一维矩阵，其中，代表该种患者信息所属类别的元素的值为1，其余元素的值为0。
  18. 根据权利要求14或15所述的计算机系统，其中，所述预测结果获取模块进一步配置为将所述当前患者样本的患者信息量化为固定值、优选量化为固定整数。
  19. 根据权利要求14至18中任一项所述的计算机系统，其中，所述预测结果获取模块进一步配置为通过查表法将所述当前患者样本的患者信息进行量化。
  20. 根据权利要求14至19中任一项所述的计算机系统，其中，所述患者信息包括患者的疾病、年龄、性别以及所述目标项目所属的开单科室。
  21. 根据权利要求14至10中任一项所述的计算机系统，其中，所述监控指标获取模块进一步配置为：
    对所述实际检测结果进行数据处理，其中，所述数据处理包括截断处理和/或正态化处理；并且
    基于经过数据处理的所述实际检测结果以及所述预测检测结果获取所述监控指标。
  22. 根据权利要求14至21中任一项所述的计算机系统，其中，所述机器学习模型为神经网络模型；或者
    所述机器学习模型为基于SVM和/或LDA的机器学习模型。
  23. 根据权利要求14至22中任一项所述的计算机系统，其中，所述质控模块进一步配置为，按时间顺序先后获取连续第一数量的当前患者样本的监控指标，基于所述连续第一数量的当前患者样本的监控指标判断所述检测仪器是否受控，当所述连续第一数量的当前患者样本的监控指标中至少有第二数量的当前患者样本的监控指标超过控制线时，输出表明所述检测仪器失控的报警提示，其中，所述第一数量大于所述第二数量并且所述第二数量大于1。
  24. 根据权利要求15所述的计算机系统，其中，所述监控指标获取模块通过如下方式被优化：
    获取多个历史患者样本的针对所述目标项目的实际检测结果以及患者信息，其中，所述实际检测结果由所述检测仪器在受控状态下对所述多个历史患者样本进行检测而得到；
    给定假报警率；
    给定所述计算模型的多个SPC参数值；
    针对每个SPC参数值求取在所述假报警率下的上下控制线和平均误差检出所需样本数，其方式为：
    在具有该SPC参数值的所述计算模型中使用所述多个历史患者样本的实际检测结果以及患者信息计算在该SPC参数值下的监控指标，并基于在该SPC参数值下的监控指标和所述假报警率获取所述上下控制线，
    在具有该SPC参数值的所述计算模型中使用包含多处模拟仪器失控的模拟数据集或包含多处真实仪器失控的真实数据集来计算在该SPC参数值下的存在仪器失控的监控指标，并将其与所述上下控制线进行对比，以便计算从各处模拟仪器失控或真实仪器失控开始到实际监测到该模拟仪器失控或真实仪器失控所需要的平均样本数、即平均误差检出所需样本数，
    其中，所述模拟数据集通过多次在不同时间点为所述多个历史患者样本的实际检测结果添加误差而获得，所述真实数据集从所述多个历史患者样本的实际检测结果以及患者信息获得或者通过获取多个另外的历史患者样本的针对所述目标项目的实际检测结果及其患者信息来获得，以及
    选择平均误差检出所需样本数最小的SPC参数值以及与该SPC参数值对应的上下控制线，以构建优化的所述计算模型。
  25. 根据权利要求21所述的计算机系统，其中，所述监控指标获取模块通过如下方式被优化：
    获取多个历史患者样本的针对所述目标项目的实际检测结果以及患者信息，其中，所述实际检测结果由所述检测仪器在受控状态下对所述历史患者样本进行检测而得到；
    给定假报警率；
    给定所述截断处理的多个截断比例和所述计算模型的多个SPC参数值；
    针对每个截断比例和每个SPC参数值的每个参数值组合求取在所述假报警率下的上下控制线和平均误差检出所需样本数，其方式为：
    在具有该参数值组合的所述计算模型中使用所述多个历史患者样本的实际检测结果以及患者信息计算在该参数值组合下的监控指标，并基于在该参数值组合下的监控指标和所述假报警率获取所述上下控制线，
    在具有该参数值组合的所述计算模型中使用包含多处模拟仪器失控的模拟数据集或包含多处真实仪器失控的真实数据集来计算在该参数值组合下的存在仪器失控的监控指标，并将其与所述上下控制线进行对比，以便计算从各处模拟仪器失控或真实仪器失控开始到实际监测到该模拟仪器失控或真实仪器失控所需要的平均样本数、即平均误差检出所需样本数，
    其中，所述模拟数据集通过多次在不同时间点为所述多个历史患者样本的实际检测结果添加误差而获得，所述真实数据集从所述多个历史患者样本的实际检测结果以及患者信息获得或者通过获取多个另外的历史患者样本的针对所述目标项目的实际检测结果及其患者信息来获得；以及
    选择平均误差检出所需样本数最小的参数值组合以及与该参数值组合对应的上下控制线，以构建优化的所述截断处理和所述计算模型。
  26. 一种构建用于基于患者样本进行实时质量控制的质控模型的方法，包括：
    获取多个患者样本的数据并将所述数据划分为训练集和测试集，所述数据包括所述多个患者样本的针对目标项目的实际检测结果以及患者信息，其中，所述实际检测结果由检测仪器对所述患者样本进行检测而得到，所述患者信息包括患者的疾病、年龄、性别以及所述目标项目的开单科室中的至少一个，其中，所述训练集中的数据是在所述检测仪器处于受控状态下时获得的；
    使用所述训练集建立机器学习模型，其方式为将所述训练集中的患者样本的患者信息进行量化，并在量化后的所述患者信息和所述训练集中的实际检测结果之间建立满足如下公式的机器学习模型，
    Xt＝f(x1t,x2t,…,xnt)+εt
    其中，Xt是所述训练集中的的实际检测结果，f(x1t,x2t,…,xnt)是机器学习模型，x1t,x2t,…,xnt代表量化后的所述患者信息，εt表示使用所述机器学习模型估计Xt的残差；
    基于所述机器学习模型构建用于基于患者样本进行实时质量控制的质控模型；以及
    使用所述测试集对所述质控模型进行验证。
  27. 根据权利要求26所述的方法，其中，所述机器学习模型为神经网络模型；或者
    所述机器学习模型为基于SVM和/或LDA的机器学习模型。
  28. 根据权利要求26或27所述的方法，其中，基于所述机器学习模型构建用于基于患者样本进行实时质量控制的质控模型，包括：
    根据所述机器学习模型和基于过程控制SPC算法的计算模型构建所述质控模型，其中，将残差εt输入到基于过程控制SPC算法的计算模型中，以获得所述计算模型的输出作为所述质控模型的监控指标。
  29. 根据权利要求28所述的方法，其中，通过如下方式对所述计算模型的参数进行优化：
    给定假报警率；
    给定所述计算模型的多个SPC参数值；
    针对每个SPC参数值求取在所述假报警率下的上下控制线和平均误差检出所需样本数，其方式为：
    在具有该SPC参数值的所述质控模型中使用所述训练集中的实际检测结果以及患者信息计算在该SPC参数值下的监控指标，并基于在该SPC参数值下的监控指标和所述假报警率获取所述上下控制线，
    在具有该SPC参数值的所述质控模型中使用包含多处模拟仪器失控的模拟数据集或包含多处真实仪器失控的真实数据集来计算在该SPC参数值下的存在仪器失控的监控指标，并将其与所述上下控制线进行对比，以便计算从各处模拟仪器失控或真实仪器失控开始到所述质控模型实际监测到该模拟仪器失控或真实仪器失控所需要的平均样本数、即平均误差检出所需样本数，
    其中，所述模拟数据集通过多次在不同时间点为所述训练集中的实际检测结果添加误差而获得，所述真实数据集从所述多个患者样本的数据获得或者通过获取多个另外的患者样本的针对目标项目的实际检测结果及其患者信息来获得，以及
    选择平均误差检出所需样本数最小的SPC参数值以及与该SPC参数值对应的上下控制线，以构建优化的质控模型。
  30. 根据权利要求28所述的方法，其中，在建立机器学习模型之前对所述训练集中的患者样本的针对目标项目的实际检测结果进行数据处理，其中，所述数据处理包括进行截断处理和/或正态化处理。
  31. 根据权利要求30所述的方法，其中，通过如下方式对所述截断处理的参数和所述计算模型的参数进行优化：
    给定假报警率；
    给定所述截断处理的多个截断比例和所述计算模型的多个SPC参数值；
    针对每个截断比例和每个SPC参数值的每个参数值组合求取在所述假报警率下的上下控制线和平均误差检出所需样本数，其方式为：
    在具有该参数值组合的所述质控模型中使用所述训练集中的实际检测结果以及患者信息计算在该参数值组合下的监控指标，并基于在该参数值组合下的监控指标和所述假报警率获取所述上下控制线，
    在具有该参数值组合的所述质控模型中使用包含多处模拟仪器失控的模拟数据集或包含多处真实仪器失控的真实数据集来计算在该参数值组合下的存在仪器失控的监控指标，并将其与所述上下控制线进行对比，以便计算从各处模拟仪器失控或真实仪器失控开始到所述质控模型实际监测到该模拟仪器失控或真实仪器失控所需要的平均样本数、即平均误差检出所需样本数，
    其中，所述模拟数据集通过多次在不同时间点为所述训练集中的实际检测结果添加误差而获得，所述真实数据集从所述多个患者样本的数据获得或者通过获取多个另外的患者 样本的针对目标项目的实际检测结果及其患者信息来获得；以及
    选择平均误差检出所需样本数最小的参数值组合以及与该参数值组合对应的上下控制线，以构建优化的质控模型。
  32. 根据权利要求26至31中任一项所述的方法，其中，将所述训练集中的患者样本的患者信息进行量化，包括：
    将所述训练集中的患者样本的患者信息中的至少一种患者信息、尤其是疾病、性别以及所述目标项目的开单科室中的至少一个分别量化为矩阵。
  33. 根据权利要求32所述的方法，其中，将所述训练集中的患者样本的患者信息中的至少一种患者信息分别量化为矩阵，包括：
    将所述训练集中的患者样本的所述至少一种患者信息中的每种患者信息量化为具有多个元素的一维矩阵，其中，所述一维矩阵的每个元素分别代表一个类别，该种患者信息属于由所述元素代表的类别中的至少一个。
    优选地，将所述训练集中的患者样本的所述至少一种患者信息中的每种患者信息量化为具有多个元素的一维矩阵，包括：
    将所述训练集中的患者样本的所述至少一种患者信息中的每种患者信息量化为具有多个元素的由0和1组成的一维矩阵，其中，代表该种患者信息所属类别的元素的值为1，其余元素的值为0。
  34. 根据权利要求26至31中任一项所述的方法，其中，将所述训练集中的患者样本的患者信息进行量化，包括：
    将所述训练集中的患者样本的患者信息量化为固定值、优选量化为固定整数。
  35. 根据权利要求26至34中任一项所述的方法，其中，通过查表法将所述训练集中的患者样本的患者信息进行量化。
  36. 根据权利要求26至35中任一项所述的方法，其中，所述患者信息包括患者的疾病、年龄、性别以及所述目标项目所属的开单科室。
  37. 一种基于患者样本进行实时质量控制的方法，包括：
    在检测仪器对当前患者样本进行针对目标项目的实时检测的过程中，获取所述当前患者样本的由所述检测仪器针对所述目标项目所检测得到的实际检测结果以及患者信息，其中，所述患者信息包括患者的疾病、年龄、性别以及所述目标项目的开单科室中的至少一个；
    将所述当前患者样本的患者信息进行量化并将量化后的所述患者信息输入预先构建好的机器学习模型中，以获得所述机器学习模型的输出结果作为针对所述目标项目的预测检测结果，其中，所述机器学习模型通过多个历史患者样本的针对所述目标项目的实际检测结果以及患者信息训练得到；
    基于所述当前患者样本的实际检测结果以及所述预测检测结果获取监控指标；并且
    基于所述监控指标判断所述检测仪器是否受控和/或基于所述监控指标判断是否给出表明所述检测仪器失控的报警提示。
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