A method and system for monitoring the psychological state of a cancer patient

Abstract

The present application relates to the medical examination technical field, specifically to a kind of cancer patient psychological state monitoring method and system, comprising the following steps: detecting serum sample solution tryptophan and kynurenine concentration and operation output measured conversion rate, extract tumor entity diameter and C-reactive protein concentration, input to nonlinear regression model output physiological baseline conversion rate, execute numerical difference operation to obtain metabolic deviation residual value, when exceeding threshold, call segmented linear mapping function to generate psychological stress specific evaluation report.In the present application, by selecting tryptophan and kynurenine conversion rate as monitoring object, introducing tumor diameter and C-reactive protein to construct model to calculate physiological baseline, eliminate physiological metabolic background from measured conversion rate to obtain residual value representing pure psychological factors, realize specific quantitative evaluation of psychological stress state, eliminate masking effect of somatic pathological factors, improve evaluation accuracy and objectivity.

Description

Technical Field

[0001] This invention relates to the field of medical testing technology, and in particular to a method and system for monitoring the psychological state of cancer patients. Background Technology

[0002] The field of medical testing technology mainly involves the physical or chemical analysis of biological samples such as human blood, body fluids, and tissues to obtain objective data reflecting the body's health status, pathological changes, or physiological indicators, thereby assisting in clinical diagnosis and treatment monitoring. Traditional methods for monitoring the psychological state of cancer patients involve collecting venous blood samples, centrifuging them to obtain serum, and then using enzyme-linked immunosorbent assay (ELISA) kits or fully automated biochemical analyzers to quantitatively detect and analyze the concentration of specific biochemical indicators (such as cortisol, catecholamines, or specific neurotransmitter metabolites) in the serum that are related to stress responses or emotional fluctuations.

[0003] Existing psychological monitoring methods mainly rely on the quantitative detection of absolute concentrations of general biochemical indicators such as cortisol. However, the tumor burden and systemic inflammatory response accompanying cancer patients themselves will continuously induce abnormal metabolic fluctuations at the physiological level, resulting in the detection results being essentially confused with the pathophysiological effects of the tumor itself and the real psychological stress response. This makes it difficult for existing indicators to accurately separate the specific metabolic changes caused solely by psychological factors. Consequently, the monitoring data is easily interfered with by the progress of physical diseases and lacks specificity, failing to accurately reflect the patient's true psychological state and thus misleading subsequent clinical intervention decisions. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method and system for monitoring the psychological state of cancer patients.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for monitoring the psychological state of cancer patients, comprising the following steps:

[0006] S1: Control the fully automated biochemical analyzer to detect serum samples from cancer patients, calculate the molar concentration values ​​of tryptophan and kynurenine based on Beer-Lambert's law, perform a ratio calculation between the molar concentration values ​​of tryptophan and kynurenine, and output the measured conversion rate value.

[0007] S2: Extract the total diameter of the tumor and the concentration of C-reactive protein from the cancer patient through the electronic medical record server, input the total diameter of the tumor and the concentration of C-reactive protein into a multivariate nonlinear regression model for calculation, and output the physiological baseline conversion rate.

[0008] S3: Call the measured conversion rate value and the physiological baseline conversion rate value to perform a numerical difference operation, subtract the physiological baseline conversion rate value from the measured conversion rate value, and obtain the metabolic deviation residual value;

[0009] S4: Compare the metabolic deviation residual value with a preset normal fluctuation threshold. When the metabolic deviation residual value exceeds the normal fluctuation threshold, call the piecewise linear mapping function to perform interval mapping on the metabolic deviation residual value and generate a psychological stress-specific assessment report.

[0010] As a further aspect of the present invention, step S1 specifically comprises:

[0011] S11: Send a spectral scanning command to the fully automated biochemical analyzer to drive a multi-wavelength light source to perform transmission scanning on the serum sample, acquire absorbance response data in the target band, and perform background noise subtraction to generate a pure spectral absorbance dataset.

[0012] S12: Call the pre-stored extinction coefficients of tryptophan and kynurenine, and perform concentration inversion calculations in conjunction with the pure spectral absorbance dataset to establish molar concentration values ​​of tryptophan and kynurenine respectively.

[0013] S13: Using the molar concentration of kynurenine as the numerator and the molar concentration of tryptophan as the denominator, perform a division operation and normalize the result to output the measured conversion rate.

[0014] As a further aspect of the present invention, step S2 specifically comprises:

[0015] S21: Establish a data communication connection with the electronic medical record server, and extract the total diameter of the tumor in the most recent imaging examination and the concentration of C-reactive protein in the most recent blood biochemistry examination of cancer patients through semantic tag index.

[0016] S22: Perform range standardization on the sum of the tumor solid diameters and the C-reactive protein concentration values ​​to eliminate the order-of-magnitude differences between the differential physical dimensions and construct a standardized physiological feature vector;

[0017] S23: Load the pre-trained weight matrix of the multivariate nonlinear regression model, input the standardized physiological feature vector into the multivariate nonlinear regression model to perform forward propagation operation, and fit the physiological baseline conversion rate value.

[0018] As a further aspect of the present invention, step S3 specifically comprises:

[0019] S31: Verify the time stamp synchronization between the measured conversion rate value and the physiological baseline conversion rate value. After confirming that the two belong to the same monitoring period, pair them together to establish a metabolic data pair to be analyzed.

[0020] S32: Perform a subtraction operation, using the measured conversion rate value in the metabolic data pair to be analyzed as the minuend and the physiological baseline conversion rate value as the subtrahend, calculate the algebraic difference between the two, and generate the original metabolic difference floating-point value.

[0021] S33: Perform outlier detection and smoothing on the original metabolic difference floating-point values, remove noise data caused by instantaneous fluctuations in the equipment, retain effective components, and obtain metabolic deviation residual values.

[0022] As a further aspect of the present invention, step S4 specifically comprises:

[0023] S41: Load a preset normal fluctuation threshold, compare the metabolic deviation residual value with the normal fluctuation threshold, and if the metabolic deviation residual value is greater than the normal fluctuation threshold, generate a stress state trigger signal.

[0024] S42: In response to the stress state trigger signal, calculate the magnitude of the metabolic deviation residual value exceeding the normal fluctuation threshold, determine the mapping interval into which the magnitude falls based on the piecewise linear mapping function, and calculate the psychological stress quantification score.

[0025] S43: Based on the psychological stress quantitative score, match the corresponding clinical interpretation text and intervention suggestions from the preset expert knowledge base, automatically format and splice them according to the preset template format, and generate a psychological stress specific assessment report.

[0026] As a further aspect of the present invention, the calculation process of the multivariate nonlinear regression model is performed according to the following physiological baseline prediction formula:

[0027] ;

[0028] in, This represents the physiological baseline conversion rate value. This represents the total diameter of the tumor solid. This represents the numerical value of C-reactive protein concentration. This represents the reference diameter constant used for dimensionless processing. Represents the tumor burden weighting coefficient. Represents the weighting coefficient of inflammatory factors. Represents an inflammatory nonlinear response index. This represents the basal metabolic intercept constant.

[0029] As a further aspect of the present invention, the specific calculation logic of the piecewise linear mapping function is defined by the following psychological stress scoring formula:

[0030] ;

[0031] in, Represents the quantitative score of psychological stress. Represents the metabolic bias residual value. This represents the normal fluctuation threshold. Represents the stress sensitivity gain coefficient. This represents the baseline score for determining stress state.

[0032] As a further aspect of the present invention, the process of acquiring absorbance response data in the target band in S11 includes:

[0033] The optical detection unit of the fully automated biochemical analyzer is switched to a 280 nm wavelength filter and a 360 nm wavelength filter, respectively.

[0034] The absorption intensity of serum samples to ultraviolet light was measured at a wavelength of 280 nm to characterize the characteristic absorption peak of tryptophan, and the absorption intensity of serum samples to ultraviolet light was measured at a wavelength of 360 nm to characterize the characteristic absorption peak of kynurenine.

[0035] The measured light intensity values ​​at the two wavelengths are converted into corresponding optical density values, and baseline correction is performed using the optical density value of the blank solvent sample to generate dual-wavelength absorbance values.

[0036] As a further aspect of the present invention, the process of generating the psychological stress-specific assessment report includes:

[0037] Extract the quantitative score of psychological stress and plot it on a trend coordinate system with time as the horizontal axis and stress intensity as the vertical axis to generate a stress trend visualization chart;

[0038] Based on the severity level of the psychological stress quantitative score, the corresponding psychological counseling plan and medication suggestions are retrieved from the database. The search results are converted into structured text paragraphs and merged with the stress trend visualization chart to form a complete psychological stress-specific assessment report.

[0039] A psychological state monitoring system for cancer patients, the system being used to implement the aforementioned method for monitoring the psychological state of cancer patients, the system comprising:

[0040] The biochemical detection and calculation module is used to control the fully automated biochemical analyzer to collect spectral data of cancer patients' serum, calculate the concentrations of tryptophan and kynurenine based on Lambert-Beer's law, and perform ratio calculations to generate measured conversion rate values.

[0041] The baseline regression prediction module is used to communicate with the electronic medical record server to extract the total tumor diameter and C-reactive protein concentration, and to calculate the physiological baseline conversion rate using a multivariate nonlinear regression model.

[0042] The residual difference analysis module is used to receive the measured conversion rate value and the physiological baseline conversion rate value, perform numerical subtraction and data smoothing, and obtain the metabolic deviation residual value.

[0043] The stress assessment mapping module is used to compare the metabolic deviation residual value with the preset normal fluctuation threshold, and calculate the stress score using a piecewise linear mapping function when the limit is exceeded, thereby generating a psychological stress-specific assessment report.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0045] In this invention, the conversion rate of tryptophan and kynurenine is selected as the core monitoring object. A multivariate nonlinear regression model is constructed by introducing tumor diameter and C-reactive protein concentration to solve the baseline conversion rate at the physiological level. Numerical difference operation is used to remove the physiological metabolic background caused by tumor burden and inflammation from the measured conversion rate, and the metabolic deviation residual value representing purely psychological factors is obtained. Piecewise linear mapping is used to achieve specific quantitative assessment of psychological stress state, effectively eliminating the masking effect of physical pathological factors on psychological monitoring data, and significantly improving the accuracy and objectivity of psychological state assessment in complex cancer environments. Attached Figure Description

[0046] Figure 1 This is a flowchart of a method for monitoring the psychological state of cancer patients according to the present invention;

[0047] Figure 2 This is a flowchart of the measured conversion rate detection and calculation process of this invention;

[0048] Figure 3 This is a flowchart of the physiological baseline conversion rate fitting process of the present invention;

[0049] Figure 4 This is a flowchart illustrating the calculation of metabolic deviation residuals in this invention.

[0050] Figure 5 This is a flowchart for generating a psychological stress assessment report according to the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this invention clearer, the software-based technical solution is described in detail below with reference to system architecture diagrams and embodiments. It should be understood that the specific embodiments described herein are only for explaining the technical solutions of this invention and do not constitute a limitation on the scope of protection.

[0052] In the description of this invention, the system architecture relationships or data processing flows indicated by terms such as "layer," "module," "interface," "data flow," "client," and "server" are all defined based on the architecture diagram or flowchart corresponding to the embodiments. This way of describing is only used to clearly illustrate the logical relationships between the elements in the technical solution, and not to limit the physical deployment form. The term "multiple" includes two or more technical units, including but not limited to multiple data nodes, processing threads, service instances, or functional components and other scalable elements. The specific number is determined according to the actual business scenario and needs to be specifically specified.

[0053] Please see Figure 1 and Figure 2 This invention provides a technical solution: a method for monitoring the psychological state of cancer patients, comprising the following steps:

[0054] S1: Controls the fully automated biochemical analyzer to detect serum samples from cancer patients, calculates the molar concentration values ​​of tryptophan and kynurenine based on Beer-Lambert's law, performs a ratio calculation between the molar concentration values ​​of tryptophan and kynurenine, and outputs the measured conversion rate value.

[0055] The specific steps of S1 are as follows:

[0056] S11: Send a spectral scanning command to the fully automated biochemical analyzer to drive a multi-wavelength light source to perform transmission scanning on the serum sample, acquire absorbance response data in the target band, and perform background noise subtraction to generate a pure spectral absorbance dataset.

[0057] The process of acquiring absorbance response data in the target band in S11 includes:

[0058] The optical detection unit of the fully automated biochemical analyzer is switched to a 280 nm wavelength filter and a 360 nm wavelength filter, respectively.

[0059] The absorption intensity of serum samples to ultraviolet light was measured at a wavelength of 280 nm to characterize the characteristic absorption peak of tryptophan, and the absorption intensity of serum samples to ultraviolet light was measured at a wavelength of 360 nm to characterize the characteristic absorption peak of kynurenine.

[0060] The measured light intensity values ​​at the two wavelengths are converted into corresponding optical density values, and baseline correction is performed using the optical density value of the blank solvent sample to generate dual-wavelength absorbance values.

[0061] S12: Call the pre-stored extinction coefficients of tryptophan and kynurenine, and perform concentration inversion calculations in combination with the pure spectral absorbance dataset to establish molar concentration values ​​of tryptophan and kynurenine respectively.

[0062] S13: Using the molar concentration of kynurenine as the numerator and the molar concentration of tryptophan as the denominator, perform a division operation and normalize the result to output the measured conversion rate.

[0063] The monitoring system's main control unit sends hexadecimal command code 0x55AA0103 to the fully automated biochemical analyzer via an RS-232 serial communication interface, triggering the ultraviolet spectrophotometric detection sequence. The biochemical analyzer's stepper motor drives the filter wheel to rotate, placing a narrow-band interference filter with a center wavelength of 280nm on the optical path axis. Simultaneously, the spectral passband width is locked at 2nm via a slit adjustment mechanism. The continuous ultraviolet beam emitted by the deuterium lamp source, after being monochromated by the filter, is incident perpendicularly to the optical path length. A 1.0 cm quartz cuvette was used, pre-filled with a lung cancer patient serum sample (sample number PID-20250512-CN) that had been centrifuged (3000 rpm, 10 minutes). A photomultiplier tube (PMT) was used to receive the transmitted light intensity signal behind the sample cell. An analog-to-digital converter (ADC) acquired the light intensity signal at a sampling frequency of 100 Hz and averaged it. The system register recorded the transmitted light intensity value at a wavelength of 280 nm. Subsequently, the system sends command 0x55AA0104 to drive the filter wheel to switch to the 360nm wavelength filter, repeating the above exposure and acquisition process, and recording the transmitted light intensity value at the 360nm wavelength. Prior to this, the system had performed the same dual-wavelength scan procedure on a blank solvent, phosphate-buffered saline (PBS, pH 7.4), to obtain the incident light intensity reference values ​​for the blank solvent. and .

[0064] The system's central processing unit (CPU) calls the floating-point unit (FPU) to perform absorbance conversion and baseline correction. First, the raw optical density value at 280 nm is calculated. ,in The initial intensity of the light source before sample attenuation is given here by... The substitute is used as a reference. The system performs logarithmic operations to obtain... The corrected tryptophan absorbance is obtained by subtracting the system dark current noise constant (set to 0.0005). Similarly, the system uses 360nm band data to calculate... The absorbance of kynurenine was then corrected to obtain the absorbance. In a specific example, the serum sample of the patient was measured... The value is 0.420, and the light transmittance is approximately 38%. The value is 0.018, and the light transmittance is approximately 96%.

[0065] The system then retrieves the molar extinction constant stored in non-volatile memory (NVRAM). The molar extinction constant of tryptophan at 280 nm... Set to 5579 The molar extinction coefficient of kynurenine at 360 nm Set to 4530 The system is based on the Lambert-Beer Law. Perform concentration inversion calculations. For the numerical value of tryptophan molar concentration... The system performs a division operation. The result is Regarding the molar concentration of kynurenine... The system performs a division operation. The result is The system stores these two double-precision floating-point numbers into a temporary data stack.

[0066] The system extracts the kynurenine concentration value from the top of the stack as the numerator and the tryptophan concentration value as the denominator, and performs a floating-point division operation: To comply with clinical data recording standards, the system multiplies this ratio by a normalization factor of 100 to obtain the final measured conversion rate. The value is 5.278. This value is marked as "high metabolic state" by the system and associated with the current system timestamp 2025-12-12 09:00:00, and is transmitted to the data analysis module via the internal bus.

[0067] Please see Figure 1 and Figure 3 S2: Extract the total diameter of the tumor and the concentration of C-reactive protein from the electronic medical record server, input the total diameter of the tumor and the concentration of C-reactive protein into the multivariate nonlinear regression model for calculation, and output the physiological baseline conversion rate.

[0068] The specific steps of S2 are as follows:

[0069] S21: Establish a data communication connection with the electronic medical record server, and extract the total diameter of the tumor in the most recent imaging examination and the concentration of C-reactive protein in the most recent blood biochemistry examination of cancer patients through semantic tag index.

[0070] S22: The sum of the diameters of the tumor solids and the concentration of C-reactive protein are normalized by range to eliminate the order-of-magnitude differences between the different physical dimensions and to construct a standardized physiological feature vector.

[0071] S23: Load the pre-trained weight matrix of the multivariate nonlinear regression model, input the standardized physiological feature vector into the multivariate nonlinear regression model to perform forward propagation operation, and fit the physiological baseline conversion rate value.

[0072] The calculation process of the multiple nonlinear regression model is performed according to the following physiological baseline prediction formula:

[0073] ;

[0074] in, This represents the physiological baseline conversion rate value. This represents the total diameter of the tumor solid. This represents the numerical value of C-reactive protein concentration. This represents the reference diameter constant used for dimensionless processing. Represents the tumor burden weighting coefficient. Represents the weighting coefficient of inflammatory factors. Represents an inflammatory nonlinear response index. This represents the basal metabolic intercept constant.

[0075] The system establishes an encrypted socket connection (port 8080) with the hospital's internal electronic medical record server (EMRServer) via the TCP / IP protocol stack. The system constructs and sends an HL7v3 format query message containing the patient's unique identifier (PID): GET / observations?subject=PID-20250512-CN&category=imaging,lab. The server returns a JSON data stream containing the patient's most recent examination results. The system enables a JSON parser, using the key-value pair index "code":"RECIST_1.1" to locate the imaging report node and traverses the "lesion_diameters" array under that node. The parser extracts the three target lesion length diameter values ​​recorded in the array: 25.0 (mm), 18.0 (mm), and 9.0 (mm). The system's arithmetic logic unit (ALU) performs an summation operation on these three values ​​to calculate the total diameter of the tumor solid. mm. Subsequently, the system used the key-value pair index "code":"CRP_Serum" to locate the biochemical test node and extracted the corresponding "value" field value of 18.5 (unit: mg / L) as the C-reactive protein concentration value. .

[0076] The system loads a multivariate nonlinear regression model to quantify the baseline contribution of physiological factors to metabolic conversion efficiency. The system first performs dimensionless preprocessing on the input variables. A baseline diameter constant is then set. The value is 10.0 mm, and the system calculates the diameter ratio. Next, the system calls the math library function log() to calculate the natural logarithm of the ratio. At the same time, the system... The value 18.5 is used to perform a power operation, with the exponent being the preset nonlinear response exponent. The calculation process of the multiple nonlinear regression model is performed according to the following physiological baseline prediction formula: in, Represents the physiological baseline conversion rate value; This represents the total diameter of the tumor mass, which is 52.0 in this case. This represents the reference diameter constant used for dimensionless processing, with a value of 10.0; The tumor burden weighting coefficient, which was set to 1.25 based on the least squares fitting of historical data; The weighting coefficient for inflammatory factors is set to 0.08. This represents the C-reactive protein concentration, which is 18.5 in this case. The nonlinear response index representing inflammation is set to 0.65; This represents the basal metabolic intercept constant, set to 1.50.

[0077] The system inputs the specific numerical values ​​into the formula to perform the final weighted summation operation. First operation: The second operation: First calculate... The system call to the function pow(18.5, 0.65) returns approximately 6.6542, which is then multiplied by a weighting factor of 0.08, i.e. The third operation: directly call the intercept constant 1.50. Summation: The result indicates that, based on the patient's current tumor burden and inflammation level, the physiological baseline metabolic conversion rate should be 4.0932. The system stores this result in the baseline field of the database.

[0078] Please see Figure 1 and Figure 4 S3: Call the measured conversion rate value and the physiological baseline conversion rate value to perform numerical difference operation, subtract the physiological baseline conversion rate value from the measured conversion rate value, and obtain the metabolic deviation residual value;

[0079] The specific steps for S3 are as follows:

[0080] S31: Verify the time stamp synchronization between the measured conversion rate value and the physiological baseline conversion rate value. After confirming that the two belong to the same monitoring period, pair them together to establish a metabolic data pair to be analyzed.

[0081] S32: Perform a subtraction operation, using the measured conversion rate value in the metabolic data pair to be analyzed as the minuend and the physiological baseline conversion rate value as the subtrahend, calculate the algebraic difference between the two, and generate the original metabolic difference floating-point value.

[0082] S33: Outlier detection and smoothing are performed on the original metabolic difference floating-point values ​​to remove noise data caused by instantaneous fluctuations in the equipment, retain the effective components, and obtain the metabolic deviation residual value.

[0083] The data processing unit retrieves the measured conversion rate values ​​generated in step S1 from the database. (timestamp) The conversion rate value of the physiological baseline generated in step S2) (timestamp) The system performs time synchronization verification and calculation. The absolute difference. If the difference is less than 86,400 seconds (i.e., 24 hours), the system determines the data is valid and establishes a pairing group. The system performs a floating-point subtraction operation: This result, 1.1848, is the current floating-point value of the original metabolic difference.

[0084] To filter out random noise from a single measurement, the system uses a weighted moving average (WMA) filter of length 4 to smooth the raw difference values. The system reads the raw metabolic difference values ​​calculated from the first three monitoring cycles of this patient from the circular buffer, which are 1.1200 ( Time), 1.1500 ( (time) and 1.2000 ( (Time). The system sets a set of time decay weight vectors. The current moment The weight is the largest. The system performs the vector dot product operation: The specific calculations are as follows: The system marks the calculated result of 1.1759 as the final metabolic bias residual value. .

[0085] Table 1 lists a snapshot of the data during the weighted moving average processing:

[0086] Table 1. Data on Smoothed Residuals of Metabolic Bias | Time Node

[0087] Time Node Original difference (Diff) Weighting coefficient (W) Weighted components <![CDATA[t −3 (Previous 3rd round)]]> 1.1200 0.1 0.1120 <![CDATA[t −2 (Previous 2nd round)]]> 1.1500 0.2 0.2300 <![CDATA[t −1 (Previous 1st)]]> 1.2000 0.3 0.3600 <![CDATA[t0 (Current)]]> 1.1848 0.4 0.4739

[0088] Referring to Table 1, this step outputs a more robust metabolic bias residual of 1.1759 through weighted fusion of data from multiple time points. This value quantifies the degree of tryptophan metabolism abnormalities in patients that exceed the scope of explanation by purely physiological and pathological factors.

[0089] Please see Figure 1 and Figure 5 S4: Compare the metabolic deviation residual value with the preset normal fluctuation threshold. When the metabolic deviation residual value exceeds the normal fluctuation threshold, call the piecewise linear mapping function to perform interval mapping on the metabolic deviation residual value and generate a psychological stress-specific assessment report.

[0090] The specific steps for S4 are as follows:

[0091] S41: Load the preset normal fluctuation threshold, compare the metabolic deviation residual value with the normal fluctuation threshold, and generate a stress state trigger signal if the metabolic deviation residual value is greater than the normal fluctuation threshold.

[0092] S42: In response to the stress state trigger signal, calculate the magnitude of the metabolic deviation residual value exceeding the normal fluctuation threshold, determine the mapping interval into which the magnitude falls based on the piecewise linear mapping function, and calculate the psychological stress quantitative score.

[0093] The specific calculation logic of the piecewise linear mapping function is defined by the following psychological stress rating formula:

[0094] ;

[0095] in, Represents the quantitative score of psychological stress. Represents the metabolic bias residual value. This represents the normal fluctuation threshold. Represents the stress sensitivity gain coefficient. The baseline score representing the determination of stress state;

[0096] S43: Based on the quantitative score of psychological stress, match the corresponding clinical interpretation text and intervention suggestions from the pre-set expert knowledge base, automatically format and splice them according to the preset template format, and generate a psychological stress-specific assessment report;

[0097] The process of generating a psychological stress-specific assessment report includes:

[0098] Extract the quantitative score of psychological stress and plot it on a trend coordinate system with time as the horizontal axis and stress intensity as the vertical axis to generate a stress trend visualization chart;

[0099] Based on the severity level of the psychological stress quantitative score, corresponding psychological counseling plans and medication recommendations are retrieved from the database. The search results are converted into structured text paragraphs and combined with stress trend visualization charts to form a complete psychological stress-specific assessment report.

[0100] The system reads the preset normal fluctuation threshold from the configuration register. The threshold is set to 0.35. The system comparator will use the metabolic bias residual value output in step S3. and Perform a logical comparison. Because... When the comparator outputs a high-level logic TRUE, the system immediately generates a stress state trigger signal and jumps to the psychological stress quantification assessment subroutine.

[0101] The system calls a piecewise linear mapping function to calculate the psychological stress score. The specific calculation logic of the piecewise linear mapping function is defined by the following psychological stress scoring formula: in, This represents a quantitative score of psychological stress, with the output range limited to between 0 and 100. This represents the metabolic bias residual value, which is 1.1759 in this case. This represents the normal fluctuation threshold, with a value of 0.35. The stress sensitivity gain coefficient is set to 45.0, which is used to amplify small values ​​of metabolic bias into the numerical space of clinical rating scales. The baseline score for determining stress state is set at 40 points.

[0102] The system substitutes the specific values ​​into the formula to perform the calculation: First, it calculates the net deviation: Then multiply by the gain factor: Finally, add the basic starting score: The system performs a round() operation on the results and outputs the final psychological stress quantification score. .

[0103] The system performs a search operation in the knowledge base index table based on a score of 77. The index table defines the range [70, 85) as corresponding to the "severe anxiety / stress" level. The system retrieves and extracts the structured text blocks corresponding to this level: Text_Interpretation: "Metabolic monitoring shows significant activation of the tryptophan-kynurenine pathway, suggesting severe psychological stress." and Text_Intervention: "Psychiatric consultation and SSRI medication evaluation are recommended." The system's drawing engine reads data from the past 14 days. The historical data array [45,48,52,...,77] was used to create a line trend chart in SVG format with time as the X-axis and score as the Y-axis. Finally, the system concatenates the patient information header, current score, trend chart SVG code, and retrieved text blocks according to a predefined HTML template to generate a "Psychological Stress Specific Assessment Report" file, which is then stored in a designated directory of the Hospital Information System (HIS). This result indicates that the patient is currently in a state of severe psychological stress requiring clinical intervention.

[0104] A psychological state monitoring system for cancer patients, the system being used to execute the aforementioned method for monitoring the psychological state of cancer patients, the system comprising:

[0105] The biochemical detection and calculation module is used to control the fully automated biochemical analyzer to collect spectral data of cancer patients' serum, calculate the concentrations of tryptophan and kynurenine based on Lambert-Beer's law, and perform ratio calculations to generate measured conversion rate values.

[0106] The baseline regression prediction module is used to communicate with the electronic medical record server to extract the total tumor diameter and C-reactive protein concentration, and to calculate the physiological baseline conversion rate using a multivariate nonlinear regression model.

[0107] The residual difference analysis module is used to receive the measured conversion rate value and the physiological baseline conversion rate value, perform numerical subtraction and data smoothing, and obtain the metabolic deviation residual value.

[0108] The stress assessment mapping module compares the metabolic deviation residual value with the preset normal fluctuation threshold, and calculates the stress score using a piecewise linear mapping function when the limit is exceeded, generating a psychological stress-specific assessment report.

[0109] The above embodiments illustrate preferred embodiments of the present invention. Any equivalent adjustments to the technical solution based on software engineering methods are within the scope of protection, including but not limited to: implementing algorithm logic using different programming languages, refactoring functional modules into services, adjusting data interaction protocols, and optimizing resource scheduling strategies. Any implementation scheme derived from reasonable modifications to the data processing flow, service call chain, or system architecture layer without departing from the core technology of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for monitoring the psychological state of cancer patients, characterized in that, Includes the following steps: S1: Control the fully automated biochemical analyzer to detect serum samples from cancer patients, calculate the molar concentration values ​​of tryptophan and kynurenine based on Beer-Lambert's law, perform a ratio calculation between the molar concentration values ​​of tryptophan and kynurenine, and output the measured conversion rate value. S2: Extract the total diameter of the tumor and the concentration of C-reactive protein from the cancer patient through the electronic medical record server, input the total diameter of the tumor and the concentration of C-reactive protein into a multivariate nonlinear regression model for calculation, and output the physiological baseline conversion rate. S3: Call the measured conversion rate value and the physiological baseline conversion rate value to perform a numerical difference operation, subtract the physiological baseline conversion rate value from the measured conversion rate value, and obtain the metabolic deviation residual value; S4: Compare the metabolic deviation residual value with a preset normal fluctuation threshold. When the metabolic deviation residual value exceeds the normal fluctuation threshold, call the piecewise linear mapping function to perform interval mapping on the metabolic deviation residual value and generate a psychological stress-specific assessment report.

2. The method for monitoring the psychological state of cancer patients according to claim 1, characterized in that, The specific steps of S1 are as follows: S11: Send a spectral scanning command to the fully automated biochemical analyzer to drive a multi-wavelength light source to perform transmission scanning on the serum sample, acquire absorbance response data in the target band, and perform background noise subtraction to generate a pure spectral absorbance dataset. S12: Call the pre-stored extinction coefficients of tryptophan and kynurenine, and perform concentration inversion calculations in conjunction with the pure spectral absorbance dataset to establish molar concentration values ​​of tryptophan and kynurenine respectively. S13: Using the molar concentration of kynurenine as the numerator and the molar concentration of tryptophan as the denominator, perform a division operation and normalize the result to output the measured conversion rate.

3. The method for monitoring the psychological state of cancer patients according to claim 2, characterized in that, The specific steps of S2 are as follows: S21: Establish a data communication connection with the electronic medical record server, and extract the total diameter of the tumor in the most recent imaging examination and the concentration of C-reactive protein in the most recent blood biochemistry examination of cancer patients through semantic tag index. S22: Perform range standardization on the sum of the tumor solid diameters and the C-reactive protein concentration values ​​to eliminate the order-of-magnitude differences between the differential physical dimensions and construct a standardized physiological feature vector; S23: Load the pre-trained weight matrix of the multivariate nonlinear regression model, input the standardized physiological feature vector into the multivariate nonlinear regression model to perform forward propagation operation, and fit the physiological baseline conversion rate value.

4. The method for monitoring the psychological state of cancer patients according to claim 3, characterized in that, The specific steps of S3 are as follows: S31: Verify the time stamp synchronization between the measured conversion rate value and the physiological baseline conversion rate value. After confirming that the two belong to the same monitoring period, pair them together to establish a metabolic data pair to be analyzed. S32: Perform a subtraction operation, using the measured conversion rate value in the metabolic data pair to be analyzed as the minuend and the physiological baseline conversion rate value as the subtrahend, calculate the algebraic difference between the two, and generate the original metabolic difference floating-point value. S33: Perform outlier detection and smoothing on the original metabolic difference floating-point values, remove noise data caused by instantaneous fluctuations in the equipment, retain effective components, and obtain metabolic deviation residual values.

5. The method for monitoring the psychological state of cancer patients according to claim 4, characterized in that, The specific steps of S4 are as follows: S41: Load a preset normal fluctuation threshold, compare the metabolic deviation residual value with the normal fluctuation threshold, and if the metabolic deviation residual value is greater than the normal fluctuation threshold, generate a stress state trigger signal. S42: In response to the stress state trigger signal, calculate the magnitude of the metabolic deviation residual value exceeding the normal fluctuation threshold, determine the mapping interval into which the magnitude falls based on the piecewise linear mapping function, and calculate the psychological stress quantification score. S43: Based on the psychological stress quantitative score, match the corresponding clinical interpretation text and intervention suggestions from the preset expert knowledge base, automatically format and splice them according to the preset template format, and generate a psychological stress specific assessment report.

6. The method for monitoring the psychological state of cancer patients according to claim 3, characterized in that, The calculation process of the multivariate nonlinear regression model is performed according to the following physiological baseline prediction formula: ； in, This represents the physiological baseline conversion rate value. This represents the total diameter of the tumor solid. This represents the numerical value of C-reactive protein concentration. This represents the reference diameter constant used for dimensionless processing. Represents the tumor burden weighting coefficient. Represents the weighting coefficient of inflammatory factors. Represents an inflammatory nonlinear response index. This represents the basal metabolic intercept constant.

7. The method for monitoring the psychological state of cancer patients according to claim 5, characterized in that, The specific calculation logic of the piecewise linear mapping function is defined by the following psychological stress scoring formula: ； in, Represents the quantitative score of psychological stress. Represents the metabolic bias residual value. This represents the normal fluctuation threshold. Represents the stress sensitivity gain coefficient. This represents the baseline score for determining stress state.

8. The method for monitoring the psychological state of cancer patients according to claim 2, characterized in that, The process of acquiring absorbance response data in the target band in S11 includes: The optical detection unit of the fully automated biochemical analyzer is switched to a 280 nm wavelength filter and a 360 nm wavelength filter, respectively. The absorption intensity of serum samples to ultraviolet light was measured at a wavelength of 280 nm to characterize the characteristic absorption peak of tryptophan, and the absorption intensity of serum samples to ultraviolet light was measured at a wavelength of 360 nm to characterize the characteristic absorption peak of kynurenine. The measured light intensity values ​​at the two wavelengths are converted into corresponding optical density values, and baseline correction is performed using the optical density value of the blank solvent sample to generate dual-wavelength absorbance values.

9. The method for monitoring the psychological state of cancer patients according to claim 5, characterized in that, The process of generating the psychological stress-specific assessment report includes: Extract the quantitative score of psychological stress and plot it on a trend coordinate system with time as the horizontal axis and stress intensity as the vertical axis to generate a stress trend visualization chart; Based on the severity level of the psychological stress quantitative score, the corresponding psychological counseling plan and medication suggestions are retrieved from the database. The search results are converted into structured text paragraphs and merged with the stress trend visualization chart to form a complete psychological stress-specific assessment report.

10. A psychological state monitoring system for cancer patients, characterized in that, The system is used to implement the method for monitoring the psychological state of cancer patients according to any one of claims 1-9, and the system comprises: The biochemical detection and calculation module is used to control the fully automated biochemical analyzer to collect spectral data of cancer patients' serum, calculate the concentrations of tryptophan and kynurenine based on Lambert-Beer's law, and perform ratio calculations to generate measured conversion rate values. The baseline regression prediction module is used to communicate with the electronic medical record server to extract the total tumor diameter and C-reactive protein concentration, and to calculate the physiological baseline conversion rate using a multivariate nonlinear regression model. The residual difference analysis module is used to receive the measured conversion rate value and the physiological baseline conversion rate value, perform numerical subtraction and data smoothing, and obtain the metabolic deviation residual value. The stress assessment mapping module is used to compare the metabolic deviation residual value with the preset normal fluctuation threshold, and calculate the stress score using a piecewise linear mapping function when the limit is exceeded, thereby generating a psychological stress-specific assessment report.

Images