Psychological disease treatment effect assessment method and system adopting data analysis

Abstract

The invention relates to the technical field of medical information, and discloses a psychological disease treatment effect evaluation method and system adopting data analysis. The method includes: classifying standardized physiological parameters based on the model; performing kernel principal component analysis on the sleep quality parameters to obtain dimensionality reduction vectors, and performing nonlinear mapping on the dimensionality reduction vectors and the voice emotion parameters to obtain cross-modal correlation features; if the correlation coefficient exceeds the threshold value, kernel function parameters are adjusted in combination with emotion stress parameters, and features are extracted again; a dynamic weight coefficient is obtained according to the cross-modal correlation features and a weight mechanism, and a treatment effect index is calculated in a weighted mode; and identifying an index abnormal mode through abnormal detection, and generating a personalized evaluation report. Through multi-dimensional physiological data analysis and dynamic adjustment, the problem that evaluation lacks objective multi-dimensional continuous monitoring and dynamic personalized analysis is solved, and the evaluation precision and the personalized level are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical information, in particular to a psychological disease treatment effect evaluation method and system using data analysis. BACKGROUND

[0002] Psychological disease treatment effect evaluation is an important research direction in the field of digital medicine. The existing technology mainly uses psychological scale evaluation, single physiological index monitoring and electronic medical record system recording methods to judge the treatment effect. The traditional evaluation method uses subjective scales such as self-report depression scale and anxiety scale, combined with discrete collection of basic physiological parameters such as heart rate and blood pressure, to perform static evaluation at specific time nodes during the treatment process, and the evaluation results are stored in the electronic medical record system for reference by doctors.

[0003] However, the existing technology has significant deficiencies: first, the evaluation is highly subjective, and self-reporting based on scales is easily influenced by patient subjective consciousness and social expectation bias, which cannot objectively reflect the real psychological state changes; second, the monitoring dimension is single, relying only on basic physiological indicators such as heart rate and blood pressure, lacking comprehensive analysis of multi-dimensional physiological behavior data such as sleep quality, emotional stress and voice characteristics; third, the evaluation is not timely, and the existing system can only perform static evaluation at discrete time points, and cannot realize continuous dynamic monitoring and real-time feedback during the treatment process.

[0004] Based on the above deficiencies of the existing technology, it can be found that relying solely on subjective scale evaluation leads to a lack of objective individual baseline reference standard, and thus personalized treatment effect evaluation benchmarks cannot be established; due to the lack of collaborative collection and cross-modal correlation analysis of multi-dimensional physiological data, the existing technology cannot discover the internal relationship and mutual influence mechanism between different physiological systems; and the static and discrete evaluation mode makes it impossible for the system to dynamically adjust the weight and optimize the treatment plan according to the real-time data changes during the treatment process, ultimately leading to the technical problems of low treatment effect evaluation accuracy and insufficient personalization. SUMMARY

[0005] The present application provides a psychological disease treatment effect evaluation method and system using data analysis, which solves the technical problem of lack of objective multi-dimensional continuous monitoring and dynamic personalized analysis in psychological disease treatment effect evaluation, and improves the accuracy and personalization level of treatment effect evaluation.

[0006] In a first aspect, the present application provides a psychological disease treatment effect evaluation method using data analysis, which comprises: Step S101, obtaining multi-dimensional physiological data of a patient, performing standardization processing on the multi-dimensional physiological data to obtain standardized physiological parameters, calculating statistical features and circadian rhythm patterns of each physiological parameter, and obtaining an individualized physiological baseline model according to individual differences of the patient; Step S102, dividing the standardized physiological parameters into sleep quality parameters, emotional stress parameters and voice emotion parameters based on the individualized physiological baseline model; inputting the sleep quality parameters into a kernel principal component analysis algorithm to obtain a reduced dimension feature vector, and inputting the reduced dimension feature vector and the voice emotion parameters into a nonlinear mapping model to obtain cross-modal correlation features; calculating a correlation coefficient of the cross-modal correlation features, and if the correlation coefficient exceeds a threshold, adjusting a kernel function parameter of the kernel principal component analysis algorithm according to the correlation coefficient and the emotional stress parameters and re-extracting the cross-modal correlation features; Step S103, obtaining a dynamic weight coefficient according to the cross-modal correlation features and a weight distribution mechanism, performing weighted calculation on the standardized physiological parameters by using the dynamic weight coefficient to obtain a treatment effect index, identifying an abnormal pattern of the treatment effect index by using an anomaly detection algorithm, and generating a personalized treatment effect evaluation report.

[0007] Optionally, the standardization processing on the multi-dimensional physiological data to obtain standardized physiological parameters comprises: calculating a mean value and a standard deviation of each physiological parameter during a baseline period, and processing the real-time collected physiological data by using a standardization formula, wherein a standardized value is equal to an original value minus the mean value and then divided by the standard deviation; respectively calculating statistical features of sleep breathing frequency, skin electrical response value and voice fundamental frequency, the statistical features comprising a mean value, a variance, a skewness and a kurtosis; if an absolute value of the skewness of a certain physiological parameter is greater than a preset skewness threshold, performing data correction by using a power transformation.

[0008] Optionally, the obtaining of the individualized physiological baseline model according to individual differences of the patient comprises: taking baseline data of consecutive fourteen days as an individual difference analysis window, and obtaining circadian rhythm parameters in the analysis window, the circadian rhythm parameters comprising a sleep latency, a rapid eye movement sleep proportion, a deep sleep proportion, a number of night awakenings, and an emotional stress peak occurrence time; obtaining the individualized physiological baseline model by using a sine function to fit each circadian rhythm parameter, and a model expression is baseline value equal to an amplitude coefficient multiplied by a sine function plus a direct current component, wherein a period of the sine function is twenty-four hours.

[0009] Optionally, the dividing of the standardized physiological parameters into sleep quality parameters, emotional stress parameters and voice emotion parameters based on the individualized physiological baseline model comprises: obtaining sleep-related data deviating from the baseline model by more than a first deviation threshold in the standardized physiological parameters, if there are multiple parameters meeting the condition, taking the parameter with the largest deviation as the main component of the sleep quality parameter; obtaining a second deviation threshold, if the current period is a night period, increasing the second deviation threshold, and taking the parameter deviating from the baseline by more than the second deviation threshold and having a frequency of change within a preset frequency range as the emotional stress parameter and the voice emotion parameter.

[0010] Optionally, the adjusting kernel function parameters of the kernel principal component analysis algorithm according to the correlation coefficient and the emotional stress parameter and re-extracting the cross-modal correlation features comprises: calculating a weighted sum of a ratio of the correlation coefficient to a benchmark correlation coefficient and a ratio of the emotional stress parameter to a benchmark stress parameter to obtain an adjustment factor; obtaining a value of the kernel function parameter of the kernel principal component analysis algorithm according to a formula that the new kernel function parameter is equal to the benchmark kernel function parameter multiplied by the adjustment factor plus one, if the new kernel function parameter is greater than a maximum kernel function parameter value, taking the maximum kernel function parameter value; updating the kernel principal component analysis algorithm using the adjusted kernel function parameter, and re-performing dimensionality reduction processing on the sleep quality parameter using the updated algorithm.

[0011] Optionally, the obtaining the dynamic weight coefficient according to the cross-modal correlation features and the weight distribution mechanism comprises: calculating a contribution value of each cross-modal correlation feature to the prediction of the treatment effect, and taking a ratio of an absolute value of the contribution value of each feature to a sum of absolute values of contribution values of all features as a dynamic weight coefficient of the feature.

[0012] Optionally, the identifying an abnormal pattern of the treatment effect index by the anomaly detection algorithm and generating a personalized treatment effect evaluation report comprises: obtaining a termination threshold of the treatment effect index; starting from a first monitoring point of the treatment effect index sequence, obtaining a time node at which a first treatment effect index value is less than or equal to the termination threshold; calculating a number of treatment days or a number of treatment cycles experienced from a treatment start time point to the time node, and generating a personalized treatment effect evaluation report containing treatment suggestions in combination with the abnormal pattern analysis result.

[0013] In a second aspect, the present application provides a psychological disease treatment effect evaluation system using data analysis, which comprises: The baseline model construction module is configured to obtain multi-dimensional physiological data of a patient, perform standardization processing on the multi-dimensional physiological data to obtain standardized physiological parameters, calculate statistical features and circadian rhythm patterns of each standardized physiological parameter, and generate an individualized physiological baseline model in combination with individual differences of the patient. The feature processing module is configured to divide the standardized physiological parameters into sleep quality parameters, emotional stress parameters and voice emotion parameters according to the individualized physiological baseline model, input the sleep quality parameters into a kernel principal component analysis algorithm to obtain a reduced dimension feature vector, input the reduced dimension feature vector and the voice emotion parameters into a nonlinear mapping model to obtain cross-modal correlation features, and calculate a correlation coefficient of the features, wherein if the correlation coefficient exceeds a threshold, the kernel function parameters are adjusted in combination with the correlation coefficient and the emotional stress parameters to re-extract the cross-modal correlation features. The evaluation generation module is configured to determine a dynamic weight coefficient according to the cross-modal correlation features and a weight distribution mechanism, perform weighted calculation on the standardized physiological parameters by using the dynamic weight coefficient to obtain a treatment effect index, and generate a personalized treatment effect evaluation report by identifying an abnormal pattern of the treatment effect index through an anomaly detection algorithm.

[0014] In a third aspect, a psychological disease treatment effect evaluation device using data analysis is provided, which includes a memory and at least one processor, the memory storing instructions; the at least one processor invokes the instructions in the memory to enable the psychological disease treatment effect evaluation device using data analysis to perform the psychological disease treatment effect evaluation method using data analysis described above.

[0015] In a fourth aspect, a computer readable storage medium is provided, which stores instructions, when running on a computer, enables the computer to perform the psychological disease treatment effect evaluation method using data analysis described above.

[0016] The present application provides a psychological disease treatment effect evaluation method and system using data analysis, which is suitable for the evaluation of psychological disease treatment effect in the field of medical information technology, and can solve the technical problems of lack of objective multi-dimensional continuous monitoring and dynamic personalized analysis in psychological disease treatment effect evaluation. Compared with the prior art, the beneficial effects of the technical solution of the present application are at least as follows: First, by obtaining multi-dimensional physiological data of a patient and establishing an individualized physiological baseline model, an objective evaluation benchmark is provided, the dependence on subjective scales is reduced, and the authenticity and reliability of the evaluation results are enhanced. Second, cross-modal correlation features are extracted based on kernel principal component analysis and nonlinear mapping technology, which realizes the fusion analysis of multi-dimensional physiological data such as sleep quality, emotional stress and voice emotion, and improves the richness and relevance of feature expression. Third, by dynamically adjusting algorithm parameters and weight distribution mechanism, real-time response and adaptive optimization to data changes in the treatment process are realized, and the dynamic monitoring capability and personalized evaluation level of the system are enhanced. Fourth, abnormal patterns of treatment effect indexes are identified using an anomaly detection algorithm, and a personalized evaluation report containing treatment recommendations is generated, improving the timeliness of treatment effect evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor based on these drawings.

[0018] Figure 1 A flowchart of a psychological disease treatment effect evaluation method using data analysis in the present application; Figure 2 A schematic diagram of the processing process of a psychological disease treatment effect evaluation method using data analysis in the present application; Figure 3 A structural schematic diagram of a psychological disease treatment effect evaluation system using data analysis in the present application; Figure 4 A structural schematic diagram of a psychological disease treatment effect evaluation device using data analysis in the present application. DETAILED DESCRIPTION

[0019] The present application provides a psychological disease treatment effect evaluation method and system using data analysis. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the term "includes" or "has" and any variation thereof is intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0020] For ease of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 An embodiment of the psychological disease treatment effect evaluation method using data analysis in the present application includes: Step S101, obtain multi-dimensional physiological data of the patient, standardize the multi-dimensional physiological data to obtain standardized physiological parameters, calculate the statistical characteristics and circadian rhythm mode of each physiological parameter, and obtain an individualized physiological baseline model according to individual differences of the patient; Step S102, based on the individualized physiological baseline model, the standardized physiological parameters are divided into sleep quality parameters, emotional stress parameters and voice emotion parameters; the sleep quality parameters are input into a kernel principal component analysis algorithm to obtain a reduced dimension feature vector, and the reduced dimension feature vector and the voice emotion parameters are input into a nonlinear mapping model to obtain a cross-modal correlation feature; a correlation coefficient of the cross-modal correlation feature is calculated, and if the correlation coefficient exceeds a threshold value, the kernel function parameter of the kernel principal component analysis algorithm is adjusted according to the correlation coefficient and the emotional stress parameters, and the cross-modal correlation feature is extracted again; Step S103, a dynamic weight coefficient is obtained according to the cross-modal correlation feature and a weight distribution mechanism, the standardized physiological parameters are weighted and calculated by using the dynamic weight coefficient to obtain a treatment effect index, and an abnormal mode of the treatment effect index is identified by an anomaly detection algorithm to generate a personalized treatment effect evaluation report.

[0021] Specifically, multi-dimensional physiological data of the patient is collected, which covers sleep breathing frequency, skin electrical response value, voice fundamental frequency and other indicators related to psychological state, and is recorded continuously for a certain period to form baseline data. These data are standardized, the mean and standard deviation of each type of physiological parameter during the baseline period are calculated, and the standardized value of a certain real-time physiological data is equal to the original value minus the baseline mean of the corresponding parameter and then divided by the baseline standard deviation. The statistical characteristics of each parameter are also calculated, including mean, variance, skewness and kurtosis, and if the absolute value of skewness exceeds a preset threshold, power transformation is used to correct the data distribution. Diurnal rhythm parameters such as sleep latency and rapid eye movement sleep proportion are extracted, and a sinusoidal function with a period of one day (baseline value = amplitude coefficient × sinusoidal function + direct current component) is used to fit these parameters to obtain an individualized physiological baseline model, which provides an individual reference for subsequent evaluation and solves the problem of lack of objective individual reference in the prior art.

[0022] Based on the individualized physiological baseline model, the standardized physiological parameters are classified, a first deviation threshold is set, sleep-related data deviating from the baseline by more than the threshold is screened out, and if multiple data meet the standard, the one with the largest deviation is selected as the main component of the sleep quality parameter. A second deviation threshold is set, and if it is a night period (according to the diurnal rhythm in the baseline model), the second deviation threshold is increased, and parameters deviating from the adjusted threshold and changing within a preset range are selected from the remaining parameters, i.e. the skin electrical response value is classified into the emotional stress parameter, and the voice fundamental frequency is classified into the voice emotion parameter, which solves the problem of single monitoring dimension.

[0023] The sleep quality parameter is input into a kernel principal component analysis algorithm, is mapped to a low-dimensional space through a kernel function, and a dimension-reduced feature vector is obtained after removing redundant information. The vector and the speech emotion parameter are input into a nonlinear mapping model, a nonlinear correlation between the two types of data is captured through an activation function, a cross-modal correlation feature is generated, multi-modal data collaborative analysis is realized, and the problem that the internal relationship in the physiological system cannot be found is solved.

[0024] A correlation coefficient of the cross-modal correlation feature is calculated, if it exceeds a preset threshold, a ratio of the coefficient to a reference correlation coefficient, a ratio of the emotional stress parameter to a reference stress parameter are calculated, and an adjustment factor is obtained by weighted summation according to weights. The parameter value is calculated according to the formula "new kernel function parameter = reference kernel function parameter x (adjustment factor + 1)", and if it does not exceed the maximum value, the adjusted parameter is directly used. The kernel principal component analysis algorithm is updated with the adjusted parameter, the sleep quality parameter is dimension-reduced again, and a new cross-modal correlation feature is generated, so as to dynamically adjust and solve the problem that static evaluation cannot adapt to real-time data changes.

[0025] The contribution value of each cross-modal correlation feature to the treatment effect prediction is calculated, the feature is taken as an independent variable and a treatment effect quantitative index is taken as a dependent variable through linear regression, and the regression coefficient is the contribution value. The dynamic weight coefficient is obtained by calculating the proportion of the absolute value of the contribution value. The standardized physiological parameters of the corresponding category are weighted and calculated by using the dynamic weight, the weighted values of the sleep quality, emotional stress and speech emotion parameters are summed to obtain a treatment effect index, the dynamic weight solves the problem of lack of individualization of fixed weight, and the quantitative index improves objectivity.

[0026] The termination threshold of the treatment effect index is obtained, which is determined in combination with a clinical standard and a baseline model fluctuation range. From the first day of treatment, the daily index is compared with the threshold in sequence, and the time node of the first compliance is recorded. The time interval from the start of treatment to the node is calculated, the index sequence is analyzed through an anomaly detection algorithm, and if the index fluctuation in a period exceeds the normal range, it is determined as an abnormal mode. A report is generated in combination with the above information, the treatment compliance time, the abnormal mode and the corresponding suggestions are determined, the problem of poor timeliness of evaluation is solved, and precise guidance is provided for clinical treatment.

[0027] It can be understood that the execution subject of the present application can be a psychological disease treatment effect evaluation system using data analysis, and can also be a terminal or a server, which is not limited here. The server is taken as an example for description of the execution subject of the present application.

[0028] In a specific embodiment, the process of standardizing the multi-dimensional physiological data to obtain standardized physiological parameters can specifically include the following steps: The mean and standard deviation of each physiological parameter during the baseline period are calculated, and the real-time collected physiological data is processed by using a standardization formula, wherein the standardized value is equal to the original value minus the mean and then divided by the standard deviation; Calculate the statistical features of the sleep respiratory frequency, the skin conductance response value and the voice fundamental frequency respectively, the statistical features including mean, variance, skewness and kurtosis; If the absolute value of the skewness of a physiological parameter is greater than a preset skewness threshold, then power transformation is used for data correction.

[0029] Specifically, in the process of standardizing the multi-dimensional physiological data to obtain standardized physiological parameters, the baseline period data of each physiological parameter needs to be determined first. In the context of psychological disease treatment effect evaluation, the baseline period usually uses continuous fourteen days of physiological data. For each type of physiological parameter (such as sleep respiratory frequency, skin conductance response value, voice fundamental frequency, etc.), the mean and standard deviation of each type of physiological parameter in the baseline period are calculated. This step establishes an individualized reference benchmark for each parameter, which can avoid the problem of ignoring individual differences by using a unified standard in the prior art. The lack of objective individual baseline is one of the core pain points of the prior art. Subsequently, each real-time collected physiological data is standardized using the corresponding baseline mean and standard deviation. That is, the standardized value of a certain real-time physiological data is equal to the original value of the data minus the baseline mean of the parameter to which the data belongs, and then divided by the baseline standard deviation of the parameter. For example, the original value of the real-time collected sleep respiratory frequency needs to be subtracted by the baseline mean of the sleep respiratory frequency, and then divided by the baseline standard deviation of the sleep respiratory frequency to obtain its standardized value. Similarly, the real-time collected skin conductance response value and voice fundamental frequency are processed. This processing can eliminate the influence of different physiological parameters due to the difference in dimensions (such as the unit of sleep respiratory frequency is times / minute, and the unit of skin conductance response value is microsiemens), so that different dimensional parameters are in the same numerical order of magnitude, laying a foundation for subsequent multi-dimensional data comprehensive analysis, and solving the problem of single monitoring dimension in the prior art, which cannot realize the collaborative analysis of multi-physiological system data.

[0030] After completing the standardization process, the statistical features of the sleep respiratory frequency, the skin conductance response value and the voice fundamental frequency, which are closely related to the psychological state, are calculated respectively. These statistical features include mean, variance, skewness and kurtosis. The statistical feature mean of a certain parameter is the average level of all standardized data of the parameter, which reflects the overall trend of the parameter in the monitoring period. The statistical feature variance is the average of the square of the deviation of the standardized data of the parameter from the statistical feature mean, which reflects the stability of the parameter fluctuation. For example, the variance of the sleep respiratory frequency can reflect the stability of the patient's breathing during sleep, which is directly related to the sleep quality evaluation. The statistical feature skewness describes the degree of asymmetry of the distribution of the standardized data, and the statistical feature kurtosis reflects the steepness of the data distribution. Through the extraction of these four types of statistical features, the physiological parameter characteristics can be described from multiple angles such as overall trend, fluctuation and distribution form, which breaks through the limitation of relying on single physiological indicator value evaluation in the prior art, and enriches the representation dimension of physiological data, providing more comprehensive information for subsequent capture of the correlation between physiological data and psychological state.

[0031] After the skewness of each parameter is calculated, the absolute value thereof is compared with a preset skewness threshold. If the absolute value of the skewness of a physiological parameter is greater than the preset skewness threshold, it indicates that the standardized data distribution of the parameter has obvious asymmetry. Such non-normal distribution data will affect the processing effect of the subsequent kernel principal component analysis algorithm (used for sleep quality parameter dimension reduction). Since the kernel principal component analysis can more accurately extract key features when the data is approximately normally distributed, power transformation is needed to correct the standardized data of the parameter at this time. The data distribution form is adjusted through power transformation, so that the corrected data is closer to the normal distribution, ensuring the accuracy and reliability of the subsequent algorithm processing, avoiding feature extraction deviation caused by abnormal data distribution, solving the problem of low evaluation accuracy caused by non-standard data processing in the prior art, and providing data quality guarantee for the effective extraction of subsequent cross-modal correlation features.

[0032] In a specific embodiment, the process of obtaining an individualized physiological baseline model according to individual differences of a patient can specifically include the following steps: The baseline data of the past fourteen days are taken as an individual difference analysis window, and circadian rhythm parameters in the analysis window are obtained, including sleep latency, rapid eye movement sleep proportion, deep sleep proportion, number of night awakenings, and emotional stress peak occurrence time. An individualized physiological baseline model is obtained by fitting each circadian rhythm parameter with a sine function. The model expression is that the baseline value is equal to the amplitude coefficient multiplied by the sine function plus the direct current component, where the period of the sine function is twenty-four hours.

[0033] Specifically, the baseline data of the past fourteen days are taken as an individual difference analysis window. In this window, the circadian rhythm parameters are obtained for the core indicators related to physiological rhythm in the evaluation of the treatment effect of psychological diseases. These parameters specifically include sleep latency, rapid eye movement sleep proportion, deep sleep proportion, number of night awakenings, and emotional stress peak occurrence time. Each parameter is extracted from fourteen days of continuous monitoring data. For example, sleep latency data needs to record the time from preparation for sleep to sleep state of the patient in the past fourteen days. Rapid eye movement sleep proportion needs to count the proportion of the duration of the rapid eye movement sleep stage in the total sleep time in each sleep cycle. Emotional stress peak occurrence time needs to capture the specific time of the peak of the emotional stress response of the patient in the past fourteen days. The collection of all parameters is in the form of continuous time series, rather than discrete time point data. This processing method addresses the problems of poor timeliness and inability to achieve continuous dynamic monitoring of the treatment process in the prior art. Through fourteen days of continuous data collection, a data source covering a complete physiological cycle without data breakpoints is provided for the subsequent establishment of individualized benchmarks, avoiding evaluation deviation caused by discrete data.

[0034] After obtaining fourteen days of continuous data of each circadian rhythm parameter, for each parameter, a sinusoidal function is used for fitting to construct an individualized physiological baseline model. The period of the sinusoidal function is set to twenty-four hours, fully matching the circadian characteristics of human physiological activities (such as sleep-wake cycle, emotional fluctuation rule). The model expression is specifically the baseline value equal to the amplitude coefficient multiplied by the sinusoidal function plus the direct current component. The amplitude coefficient is used to quantify the fluctuation amplitude of the parameter within the circadian cycle. For example, the amplitude coefficient of deep sleep proportion can reflect the difference between the individual's night deep sleep proportion and the daytime baseline level. The direct current component represents the basic stable level of the parameter within the circadian cycle. For example, the direct current component related to the voice emotion parameter can reflect the voice fundamental frequency baseline value of the individual in the daily state. In the specific fitting process, for the sleep latency parameter, the sleep latency data recorded at different time points in the fourteen days is substituted into the sinusoidal function one by one. The amplitude coefficient and the direct current component corresponding to the parameter are determined by calculation, and then the sleep latency individualized baseline model applicable only to the patient is formed. Similarly, the rapid eye movement sleep proportion, deep sleep proportion, night awakening times, and emotional stress peak occurrence time each need to use fourteen days of continuous data to perform sinusoidal function fitting. Each circadian rhythm parameter and the baseline model obtained by fitting form a one-to-one correspondence, ensuring that each model can accurately reflect the unique rhythm characteristics of the individual in a specific physiological indicator.

[0035] This processing logic based on fourteen days of continuous data and sinusoidal function fitting directly addresses the problem that existing technologies lack objective individual baseline reference standards and cannot establish personalized treatment effect evaluation benchmarks. Existing technologies rely on unified physiological indicator standards or discrete subjective reports, ignoring individual differences in physiological rhythms. In this scheme, fourteen days of continuous data collection ensures complete coverage of individual physiological rhythms, avoiding the problem that short-term data cannot reflect the true rhythm. The continuous data is converted into a quantifiable individualized baseline model through sinusoidal function fitting, so that each physiological parameter has a reference benchmark based on the individual itself, rather than a unified external standard. When classifying standardized physiological parameters based on the model, parameters that deviate from the individual's own baseline can be accurately identified. For example, when judging sleep quality parameters, the patient's exclusive sleep latency baseline model can be used instead of a universal threshold, effectively reducing misjudgments caused by individual differences. At the same time, the selected five circadian rhythm parameters cover sleep quality (sleep latency, rapid eye movement sleep proportion, deep sleep proportion, night awakening times) and emotional state (emotional stress peak occurrence time), two dimensions closely related to the treatment effect of psychological diseases, solving the problem of single monitoring dimension in existing technologies, which only relies on basic indicators such as heart rate and blood pressure. This provides multi-dimensional and individualized basic data support for subsequent cross-modal correlation feature extraction.

[0036] In a specific embodiment, the process of classifying the standardized physiological parameters into sleep quality parameters, emotional stress parameters and voice emotion parameters based on the individualized physiological baseline model can specifically include the following steps: Obtaining sleep-related data in the standardized physiological parameters that deviates from the baseline model by more than a first deviation threshold, and if there are multiple parameters that meet the condition, the parameter with the largest deviation is selected as the main component of the sleep quality parameter. Obtaining a second deviation threshold, if the current period is a night period, the second deviation threshold is increased, and the parameter in the remaining parameters that deviates from the baseline by more than the second deviation threshold and has a change frequency within a preset frequency range is selected as the emotional stress parameter and the voice emotion parameter.

[0037] Specifically, when classifying the standardized physiological parameters based on the individualized physiological baseline model, sleep-related data needs to be selected from all standardized physiological parameters. These sleep-related data specifically include standardized sleep respiratory frequency, sleep latency, rapid eye movement sleep proportion, deep sleep proportion, number of night awakenings, etc., all of which are directly related to sleep quality monitoring in psychological disease treatment effect evaluation. By calculating the deviation of each sleep-related data from the corresponding parameter in the individualized physiological baseline model, it is determined whether the deviation exceeds a preset first deviation threshold. For example, if the difference between the standardized sleep latency of a patient and the sleep latency in the individualized physiological baseline model exceeds the first deviation threshold, the sleep latency data is included in the selected range. If the deviation of multiple sleep-related data exceeds the first deviation threshold, the deviation values of each data need to be further calculated, and the data with the largest deviation is selected as the main component of the sleep quality parameter. For example, when the standardized deep sleep proportion deviates from the baseline by 40% and the number of night awakenings deviates from the baseline by 25%, if both exceed the first threshold, the deep sleep proportion is selected as the main component of the sleep quality parameter. This processing process addresses the problem of single monitoring dimension in the prior art, which cannot focus on core physiological indicators. By directionally selecting sleep-related data and locking the core parameter with the largest deviation, the sleep quality evaluation can accurately anchor the sleep indicators that have the most significant impact on the psychological state, avoiding evaluation deviation caused by parameter clutter, and providing clear core data support for subsequent sleep quality parameter dimension reduction.

[0038] After the sleep quality parameter screening is completed, the remaining standardized physiological parameters (including standardized electrodermal response values, voice fundamental frequency, and emotion stress peak time related data) need to be classified. First, a preset second deviation threshold is obtained, and then it is determined whether the current period is a night period (usually combined with the 24-hour period of the circadian rhythm parameter in the individualized physiological baseline model, such as 22:00 to 6:00 the next day as the night period). If it is a night period, the second deviation threshold is increased, for example, the daytime second deviation threshold of 15% is adjusted to 25% at night. This is because the human body is relatively stable in physiological activity at night, and slight fluctuations are mostly normal rhythm performance. Increasing the threshold value can avoid misjudging normal fluctuations as abnormal data, which meets the needs of adjusting the judgment standard in combination with the circadian rhythm in psychological disease assessment, and solves the problem that the prior art lacks dynamic threshold adjustment and cannot adapt to individual physiological rhythms. Then, the deviation of the remaining standardized physiological parameters from the corresponding parameters in the individualized physiological baseline model is calculated, and parameters with a deviation exceeding the adjusted second deviation threshold are screened out. At the same time, it is necessary to verify whether the frequency of change of these parameters is within the preset frequency range. The preset frequency range is set according to the common rules of emotion and voice response in psychological disease assessment, for example, the change frequency of emotion stress parameters (such as electrodermal response values) is usually 1-5 minutes / time, and the change frequency of voice emotion parameters (such as voice fundamental frequency) is usually 10 seconds-2 minutes / time. If a parameter meets both the deviation threshold and the corresponding frequency range, it is classified into the corresponding category. Among them, electrodermal response values, emotion stress peak time related data are usually classified as emotion stress parameters, and voice fundamental frequency data are classified as voice emotion parameters. This classification logic combines period-adjusted threshold and frequency screening to ensure that the screening of emotion stress parameters and voice emotion parameters meets the characteristics of individual physiological rhythms and accurately matches the dynamic change characteristics of the two types of parameters, solving the problem that the prior art cannot distinguish the dynamic characteristics of different physiological parameters, leading to classification confusion. At the same time, a parameter system of three dimensions of sleep quality, emotion stress, and voice emotion is formed, providing multi-dimensional and highly correlated basic data for subsequent cross-modal correlation feature extraction, breaking through the limitations of relying on a single physiological indicator in the prior art.

[0039] In a specific embodiment, the process of adjusting the kernel function parameter of the kernel principal component analysis algorithm according to the correlation coefficient and the emotion stress parameter and re-extracting the cross-modal correlation feature can specifically include the following steps: Calculate the weighted sum of the ratio of the correlation coefficient to the reference correlation coefficient and the ratio of the emotion stress parameter to the reference stress parameter to obtain an adjustment factor; According to the formula that the new kernel function parameter is equal to the reference kernel function parameter multiplied by the adjustment factor plus one, the kernel function parameter value of the kernel principal component analysis algorithm is obtained. If the new kernel function parameter is greater than the maximum kernel function parameter value, the maximum kernel function parameter value is taken; The kernel principal component analysis algorithm is updated using the adjusted kernel function parameter, and the updated algorithm is used to re-process the sleep quality parameters.

[0040] Specifically, when calculating the adjustment factor, the specific values of the correlation coefficient and the reference correlation coefficient need to be determined first. The correlation coefficient is derived from the previous calculation results of the cross-modal correlation features, which are generated by nonlinear mapping of the sleep quality parameter dimension reduction feature vector and the speech emotion parameter. The reference correlation coefficient is a reference value preset for the psychological disease treatment effect evaluation scene, which is usually statistically obtained based on a large number of stable state patient cross-modal correlation features, for example, when the patient's mood is not significantly fluctuating and the sleep state is stable, the correlation coefficient of the cross-modal correlation features is often set to 0.6 as the reference. The emotional stress parameter is a parameter classified based on the individualized physiological baseline model, such as the standardized skin conductance response value or the emotional stress peak value occurrence time deviation value. The reference stress parameter corresponds to the reference level of the emotional stress parameter in the individualized physiological baseline model, for example, the reference value of the skin conductance response value in the baseline model of a certain patient is 5 microsiemens, which is generated by fitting the baseline data for fourteen consecutive days. Then, two ratios are calculated, i.e. the ratio of the correlation coefficient to the reference correlation coefficient and the ratio of the emotional stress parameter to the reference stress parameter. In the psychological disease evaluation scene, the emotional stress state has a more significant impact on the physiological data correlation pattern, so the weight of the emotional stress parameter correlation ratio is usually set to 0.6, and the weight of the correlation coefficient correlation ratio is set to 0.4. The two weighted sums are summed to obtain the adjustment factor, for example, when the correlation coefficient is 0.8 and the reference correlation coefficient is 0.6, the ratio is 0.8 / 0.6≈1.33; when the emotional stress parameter is 8 microsiemens and the reference stress parameter is 5 microsiemens, the ratio is 8 / 5=1.6, and the adjustment factor is 1.33×0.4+1.6×0.6≈0.532+0.96=1.492.

[0041] The reference kernel function parameter of the kernel principal component analysis algorithm needs to be preset according to the type of the sleep quality parameter, for example, for continuous parameters such as sleep breathing frequency and deep sleep proportion, the RBF kernel function is often used, and the reference gamma value is set to 0.3. According to the formula, the new kernel function parameter is equal to the reference kernel function parameter multiplied by (the adjustment factor plus one), i.e. 0.3×(1.492+1)=0.3×2.492≈0.7476. At the same time, the maximum value of the kernel function parameter needs to be preset to avoid overfitting of the algorithm due to too large parameter, in the psychological disease physiological data processing, the maximum value of the gamma of the RBF kernel is usually set to 0.8, if the new kernel function parameter does not exceed the maximum value, the calculation result is directly used; if the adjustment factor is larger, for example, the adjustment factor is 2.5, the new kernel function parameter is 0.3×(2.5+1)=1.05, which exceeds the maximum value 0.8, then 0.8 is taken as the final kernel function parameter.

[0042] The sleep quality parameters (such as the standardized sleep latency, the proportion of rapid eye movement sleep, etc.) obtained by the previous classification are re-input into the updated kernel principal component analysis algorithm for dimension reduction processing to generate a new dimension reduction feature vector. The vector can more accurately reflect the key information of the sleep quality parameters under the current emotional stress state compared to before the adjustment. The new dimension reduction feature vector and the speech emotion parameters are re-input into the nonlinear mapping model to obtain the adjusted cross-modal correlation features. The prior art has the problem of static discrete evaluation, and cannot optimize the analysis model according to the real-time data changes in the treatment process. By dynamically adjusting the kernel function parameters based on the correlation coefficient and the emotional stress parameters, the kernel principal component analysis algorithm can adapt to the changes in the physiological state of the patient. For example, when the emotional stress of the patient increases, the adjusted kernel function parameters can enhance the ability to capture abnormal fluctuation characteristics in the sleep quality parameters, avoid feature extraction bias caused by fixed model parameters, and solve the problem of low evaluation accuracy in the prior art. At the same time, the adjustment process is based on individualized data of the patient rather than general parameters, further improving the individualization level of the evaluation, and meeting the needs of individual difference adaptation in the evaluation of the treatment effect of psychological diseases.

[0043] In a specific embodiment, the process of obtaining the dynamic weight coefficient according to the cross-modal correlation feature and the weight distribution mechanism can specifically include the following steps: Calculate the contribution value of each cross-modal correlation feature to the prediction of the treatment effect, and take the ratio of the absolute value of the contribution value of each feature to the sum of the absolute values of the contribution values of all features as the dynamic weight coefficient of the feature.

[0044] Specifically, when obtaining the dynamic weight coefficient according to the cross-modal correlation feature and the weight distribution mechanism, it is necessary to first determine the composition of the cross-modal correlation feature. Such features are the dimension reduction feature vectors obtained by kernel principal component analysis of sleep quality parameters, and the fusion features generated by nonlinear mapping of speech emotion parameters. In the context of psychological disease treatment effect evaluation, they can specifically include sleep rhythm and speech fundamental frequency correlation features, sleep depth and speech emotion intensity correlation features, etc. Each feature corresponds to the internal correlation between sleep and speech physiological data in two dimensions, and is a key intermediate indicator reflecting the treatment effect.

[0045] When calculating the contribution value of each cross-modal correlation feature to the prediction of treatment effect, the objective reference index of mental illness treatment effect needs to be combined. Usually, the cross-modal correlation feature is taken as the independent variable, and the quantitative index of treatment effect, such as the improvement rate of patient's depressive symptoms in the treatment period, the reduction amplitude of anxiety episode frequency, the remission degree of sleep disorder, etc., is taken as the dependent variable. Feature importance evaluation method is used for calculation. Taking linear regression model as an example, all cross-modal correlation features and treatment effect quantitative index are substituted into the model for fitting. The regression coefficient corresponding to each independent variable (i.e. each cross-modal correlation feature) output by the model is the contribution value of the feature to the prediction of treatment effect. The numerical value of the regression coefficient directly reflects the influence degree of the feature on the prediction of treatment effect. A positive coefficient indicates that the feature positively promotes the accuracy of treatment effect evaluation, and a negative coefficient indicates that the feature has a reverse influence on the prediction of treatment effect. This calculation method can accurately capture the correlation strength between different cross-modal correlation features and treatment effect, and avoid the bias caused by subjective setting of feature importance.

[0046] After obtaining the contribution value of each cross-modal correlation feature, the absolute value of each contribution value needs to be taken, because the weight distribution needs to focus on the influence degree of the feature on the prediction of treatment effect, not the influence direction, to avoid the distortion of weight calculation caused by the mutual offset of positive and negative contribution values. Then, the sum of the absolute values of the contribution values of all cross-modal correlation features is calculated. The ratio of the absolute value of the contribution value of a single cross-modal correlation feature to the sum is calculated to obtain the dynamic weight coefficient corresponding to the cross-modal correlation feature. For example, a patient's cross-modal correlation features include feature A, feature B and feature C. After fitting, the contribution value of feature A is 1.5, the contribution value of feature B is -0.9, and the contribution value of feature C is 0.6. The absolute values of the contribution values of the three features are 1.5, 0.9 and 0.6 respectively, and the sum is 3.0. Therefore, the dynamic weight coefficient of feature A is 1.5 / 3.0=0.5, the dynamic weight coefficient of feature B is 0.9 / 3.0=0.3, and the dynamic weight coefficient of feature C is 0.6 / 3.0=0.2. The weight coefficient of each feature directly corresponds to the contribution degree of the feature to the prediction of treatment effect. The greater the contribution, the higher the weight.

[0047] The prior art has the problem of lack of dynamic individualization analysis. Fixed weights are used to process multi-dimensional data, which cannot adapt to the correlation differences between different patient physiological characteristics and treatment effects. The dynamic weight coefficient is calculated based on the actual contribution value of the patient's own cross-modal correlation feature. The weight distribution of each patient matches the influence degree of the individual feature on the treatment effect, solving the technical problem of insufficient individualization. At the same time, the existing technology has the problem of insufficient feature representation due to single monitoring dimension. The cross-modal correlation feature covers sleep and speech dimensions. The dynamic weight can highlight the features that have more significant influence on the treatment effect, avoid the evaluation bias caused by the weight imbalance of single-dimensional features, further improve the accuracy of treatment effect evaluation, and meet the needs of individual difference adaptation and effective use of multi-dimensional data in the evaluation of mental illness treatment effect.

[0048] In a specific embodiment, the process of identifying an abnormal pattern of treatment effect index by an abnormality detection algorithm and generating a personalized treatment effect evaluation report can specifically include the following steps: Obtaining a termination threshold of the treatment effect index; From the first monitoring point of the treatment effect index sequence, obtaining the time node at which the first treatment effect index value is less than or equal to the termination threshold; Calculate the number of treatment days or treatment cycles experienced from the treatment start time point to the time node, and generate a personalized treatment effect evaluation report containing treatment recommendations combined with the abnormal pattern analysis result.

[0049] Specifically, when obtaining the termination threshold of the treatment effect index, it needs to be determined in combination with the clinical standard of psychological disease treatment effect evaluation and the individualized physiological baseline model of the patient, referring to the reference range of the treatment effect index when the treatment of the same psychological disease (such as depression, anxiety) is effective in the clinic, while superimposing the fluctuation interval of the treatment effect index in the patient's own individualized physiological baseline model, by statistics of the lower limit value of the treatment effect index when the same disease and similar physiological characteristics of the patient are treated to clinical remission, combined with the influence amplitude of the normal fluctuation of the patient's circadian rhythm parameters (such as sleep latency, emotional stress peak time) on the index, the final termination threshold is determined. For example, a certain depressive patient, the treatment effect index corresponding to the clinical remission of depression is usually converted into an index threshold of 28 based on the Hamilton Depression Scale score, the fluctuation of the emotional stress parameter in the patient's individualized physiological baseline model will cause the treatment effect index to produce a deviation of ±4, therefore the termination threshold is comprehensively determined as 32.

[0050] The treatment effect index sequence is generated by calculating the standardized physiological parameters (sleep quality parameters, emotional stress parameters, voice emotion parameters) with dynamic weight coefficients every day, and each monitoring point corresponds to the index value of the day. From the first monitoring point of the sequence (i.e. the day the treatment starts), compare the treatment effect index value of each monitoring point with the termination threshold one by one, when the treatment effect index value of a monitoring point is less than or equal to the termination threshold for the first time, record the specific time corresponding to the monitoring point, which is the target time node. For example, the treatment effect index of the patient on the first day of treatment is 52 (corresponding to the standardized value of sleep breathing frequency 1.2, the standardized value of skin electrical response 1.5, and the standardized value of voice fundamental frequency 1.3), 47 on the second day, 41 on the third day, 36 on the fourth day, and 31 on the fifth day, the termination threshold is 32, the first time node that meets the condition is the fifth day of treatment, and the index value of this node is 31, which corresponds to the physiological state improvement of the standardized value of sleep depth ratio to 0.8 and the standardized value of emotional stress parameter to 0.6.

[0051] The time interval from the treatment start time point to the target time node is calculated, the treatment days are counted by natural days, or the treatment cycle number is converted according to the preset treatment cycle (for example, 7 days are usually set as a psychological disease treatment cycle in clinical treatment), and the index change mode is analyzed through an abnormality detection algorithm (for example, a sliding window abnormality detection based on the treatment effect index sequence). If the single-day change amplitude of the treatment effect index in a certain period of time exceeds the preset fluctuation threshold (in psychological disease treatment, the single-day normal fluctuation range of the treatment effect index is usually not more than 10, and if it exceeds, it indicates that there may be physiological data abnormalities or abnormal treatment intervention responses), it is determined as an abnormal mode. For example, the index of a patient on the second day of treatment is 47 to 41 on the third day (a decrease of 6, within the normal range), 41 on the third day to 36 on the fourth day (a decrease of 5, normal), 36 on the fourth day to 31 on the fifth day (a decrease of 5, normal), and there is no abnormal mode; if a patient's index on the third day is 40 and on the fourth day is 22 (a decrease of 18, exceeding the fluctuation threshold), it is determined as an abnormal mode, and the corresponding physiological parameter changes (such as a sudden drop of 2.0 in the standardized value of the voice emotion parameter on the day, which may be related to a sudden emotional stress event of the patient on the day) need to be recorded synchronously.

[0052] The treatment days (or cycle numbers), abnormal mode analysis results (including abnormal occurrence time, corresponding physiological parameter abnormal items and fluctuation amplitudes) are integrated, and a personalized treatment effect evaluation report is generated in combination with the psychological disease clinical treatment guidelines. The report needs to clearly mark the time when the treatment reaches the termination threshold and the key changes of the physiological parameters during the period, if there is an abnormal mode, it needs to make targeted suggestions (such as the abnormal mode is caused by a sudden change in the emotional stress parameter, it is suggested to increase the monitoring frequency of the emotional stress parameter, and adjust the emotional relief scheme in the psychological intervention); if there is no abnormal mode, it is suggested to maintain the current treatment scheme, continue to monitor the changes of the deep sleep proportion in the sleep quality parameter to consolidate the effect, the whole report content directly relates to the individualized physiological data changes and treatment effect of the patient, avoids the evaluation deviation caused by the dependence on the subjective scale in the prior art, solves the problems of strong subjectivity and poor timeliness of the evaluation, and through the abnormal mode recognition, the dynamic monitoring of the treatment process is realized, and the individualization level of the evaluation is improved.

[0053] The psychological disease treatment effect evaluation method using data analysis in the embodiments of the present application is described above, please refer to Figure 2 , the psychological disease treatment effect evaluation processing process using data analysis in the embodiments of the present application is described as follows: Firstly, the multi-dimensional physiological data of the patient is acquired, which covers multiple aspects and provides a basis for subsequent analysis. Then, the acquired data is standardized to make data from different sources and different scales comparable. Then, statistical characteristics and circadian rhythms are calculated to extract representative statistical characteristics and information reflecting the circadian variation of the physiological state of the patient from the data. Based on this information, an individual physiological baseline model is established, which reflects the physiological characteristics of the patient in a normal state. Secondly, the parameter classification link is entered, and the data is divided into three categories: speech emotion parameters, sleep quality parameters, and emotional stress parameters, which are processed separately. For speech emotion parameters, cross-modal feature extraction is performed first to extract multiple features related to emotions from speech data. Then, the correlation coefficient is calculated to determine the correlation of these features with the treatment effect. If the correlation coefficient exceeds the threshold, the kernel function parameter is adjusted, otherwise kernel principal component analysis is performed for dimension reduction. Sleep quality parameters are directly processed by kernel principal component analysis for dimension reduction. Emotional stress parameters are first calculated with dynamic weights according to their importance, and then the treatment effect index is calculated. After the above processing, abnormal pattern recognition is performed to find possible abnormalities in the processed data. Finally, an evaluation report is generated to evaluate and summarize the treatment effect of psychological diseases based on all the analysis results.

[0054] The above describes the psychological disease treatment effect evaluation method using data analysis in the embodiments of the present application. The psychological disease treatment effect evaluation system 300 using data analysis in the embodiments of the present application is described below. Please refer to Figure 3 An embodiment of the psychological disease treatment effect evaluation system using data analysis in the embodiments of the present application includes: The baseline model construction module 301 is configured to acquire multi-dimensional physiological data of the patient, perform standardization processing on the multi-dimensional physiological data to obtain standardized physiological parameters, calculate statistical characteristics and circadian rhythm patterns of each standardized physiological parameter, and generate an individual physiological baseline model in combination with individual differences of the patient. The feature processing module 302 is configured to divide the standardized physiological parameters into sleep quality parameters, emotional stress parameters, and speech emotion parameters according to the individual physiological baseline model, input the sleep quality parameters into a kernel principal component analysis algorithm to obtain a dimension-reduced feature vector, input the dimension-reduced feature vector and the speech emotion parameters into a nonlinear mapping model to obtain cross-modal correlation features, calculate the correlation coefficient of the features, adjust the kernel function parameter in combination with the correlation coefficient and the emotional stress parameters if the correlation coefficient exceeds the threshold, and re-extract the cross-modal correlation features. The evaluation generation module 303 is configured to determine a dynamic weight coefficient according to the cross-modal correlation features and a weight distribution mechanism, perform weighted calculation on the standardized physiological parameters using the dynamic weight coefficient to obtain a treatment effect index, and generate a personalized treatment effect evaluation report by identifying the abnormal pattern of the treatment effect index through an abnormality detection algorithm.

[0055] Through the synergistic cooperation of each component, the baseline model construction module 301 first acquires the multi-dimensional physiological data of the patient and completes the standardization processing, calculates the statistical characteristics and the circadian rhythm mode to generate an individualized physiological baseline model, which provides the core classification basis for the feature processing module. The feature processing module divides the standardized physiological parameters into sleep quality, emotional stress, and voice emotion parameters according to the deviation threshold set by the baseline model, and the circadian rhythm cycle data in the baseline model also provides a reference for adjusting the second deviation threshold for the feature processing module in the night period, avoiding misjudgment of physiological parameter fluctuations at night. The feature processing module 302 obtains the dimension reduction feature vector by kernel principal component analysis of the sleep quality parameter, and generates the cross-modal correlation feature by nonlinear mapping with the voice emotion parameter. If the correlation coefficient of the feature is above the threshold, the emotional stress parameter and the reference stress parameter in the baseline model are called to calculate the adjustment factor, and the feature is extracted again after optimizing the kernel function parameter, and the updated cross-modal correlation feature is directly delivered to the evaluation generation module to provide multi-modal data support for dynamic weight calculation. The evaluation generation module 303 determines the dynamic weight coefficient by combining the cross-modal correlation feature and the weight distribution mechanism, and calculates the treatment effect index by weighting the standardized physiological parameters. In the process, the individual baseline fluctuation range of the physiological parameters in the baseline model needs to be referred to, to ensure that the index fits the patient differences; then the abnormal detection algorithm is used to identify the abnormal pattern of the index, and the parameter change frequency data of the feature processing module and the circadian rhythm rule of the baseline model are referred to during the identification, and finally the core information of each module is integrated to generate a personalized evaluation report containing treatment suggestions, forming a complete cooperative link from data acquisition to evaluation output, effectively solving the problem of lack of objective and dynamic analysis in traditional evaluation.

[0056] The above Figure 3 From the perspective of modular functional entities, the psychological disease treatment effect evaluation system using data analysis in the embodiment of the application is described in detail below. From the perspective of hardware processing, the psychological disease treatment effect evaluation device 400 using data analysis in the embodiment of the application is described in detail.

[0057] Referring to Figure 4 In the embodiment of the application, a psychological disease treatment effect evaluation device 400 using data analysis is also provided. The psychological disease treatment effect evaluation device using data analysis can be a server, and its internal structure can be as follows Figure 4The psychological disease treatment effect evaluation device using data analysis shown in the figure includes a processor 402, a memory 403, a display screen 404, an input device 405, a network interface 406 and a database 407 connected through a system bus 401. Among them, the computer designed processor 402 is used to provide computing and control ability. The memory 403 of the psychological disease treatment effect evaluation device using data analysis includes a non-volatile storage medium 4031 and an internal memory 4032. The non-volatile storage medium 4031 stores an operating system and a computer program. The internal memory 4032 provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database 407 of the psychological disease treatment effect evaluation device using data analysis is used to store the corresponding data in the embodiment. The network interface 406 of the psychological disease treatment effect evaluation device using data analysis is used to communicate with the external terminal through the network connection. The computer program executed by the processor can realize the above-mentioned method.

[0058] Those skilled in the art can understand that, Figure 4 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the psychological disease treatment effect evaluation device using data analysis to which the scheme of the present application is applied.

[0059] The present application also provides a computer readable storage medium, which can be a non-volatile computer readable storage medium, and can also be a volatile computer readable storage medium, and the computer readable storage medium stores instructions, when the instructions run on the computer, make the computer execute the steps of the psychological disease treatment effect evaluation method using data analysis.

[0060] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-mentioned system, system and unit can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

[0061] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or the entire or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a data analysis-based psychological disease treatment effect evaluation device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0062] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for evaluating the treatment effect of mental illness using data analysis, characterized in that, The method includes: Step S101: Obtain the patient's multidimensional physiological data, standardize the multidimensional physiological data to obtain standardized physiological parameters, calculate the statistical characteristics and diurnal rhythm patterns of each physiological parameter, and obtain an individualized physiological baseline model based on the individual differences of the patient. Step S102: Based on the individualized physiological baseline model, standardized physiological parameters are divided into sleep quality parameters, emotional stress parameters, and voice emotion parameters; the sleep quality parameters are input into the kernel principal component analysis algorithm to obtain a dimensionality-reduced feature vector, and the dimensionality-reduced feature vector and voice emotion parameters are input into a nonlinear mapping model to obtain cross-modal correlation features; the correlation coefficient of the cross-modal correlation features is calculated, and if the correlation coefficient exceeds a threshold, the kernel function parameters of the kernel principal component analysis algorithm are adjusted according to the correlation coefficient and emotional stress parameters, and the cross-modal correlation features are re-extracted; Step S103: Obtain dynamic weight coefficients based on the cross-modal association features and weight allocation mechanism; use the dynamic weight coefficients to perform weighted calculations on standardized physiological parameters to obtain a treatment effect index; identify abnormal patterns in the treatment effect index using an anomaly detection algorithm and generate a personalized treatment effect evaluation report.

2. The method according to claim 1, characterized in that, The standardization process for the multidimensional physiological data to obtain standardized physiological parameters includes: Calculate the mean and standard deviation of each physiological parameter during the baseline period, and process the real-time physiological data using a standardization formula, where the standardized value is equal to the original value minus the mean and then divided by the standard deviation. Statistical characteristics of sleep respiratory rate, skin conductance response value and speech fundamental frequency were calculated respectively, including mean, variance, skewness and kurtosis; If the absolute value of the skewness of a certain physiological parameter is greater than the preset skewness threshold, then power transformation is used for data correction.

3. The method according to claim 1, characterized in that, The individualized physiological baseline model obtained based on individual patient differences includes: The baseline data for fourteen consecutive days was used as the individual difference analysis window to obtain the circadian rhythm parameters within the analysis window. The circadian rhythm parameters include sleep latency, REM sleep percentage, deep sleep percentage, number of nighttime awakenings, and the time of peak emotional stress. Individualized physiological baseline models are obtained by fitting various diurnal rhythm parameters with a sine function. The model expression is that the baseline value is equal to the amplitude coefficient multiplied by the sine function plus the DC component, where the period of the sine function is 24 hours.

4. The method according to claim 1, characterized in that, The standardized physiological parameters based on the individualized physiological baseline model are divided into sleep quality parameters, emotional stress parameters, and voice emotion parameters, including: Acquire sleep-related data from standardized physiological parameters that deviate from the baseline model by more than the first deviation threshold. If multiple parameters meet the conditions, the parameter with the largest deviation is taken as the main component of the sleep quality parameter. Obtain a second deviation threshold. If the current time period is nighttime, increase the second deviation threshold. Among the remaining parameters, those that deviate from the baseline by more than the second deviation threshold and whose change frequency is within a preset frequency range are used as emotional stress parameters and voice emotion parameters.

5. The method according to claim 1, characterized in that, The step of adjusting the kernel function parameters of the kernel principal component analysis algorithm and re-extracting cross-modal association features based on the correlation coefficient and emotional stress parameters includes: The adjustment factor is obtained by weighting the ratio of the correlation coefficient to the baseline correlation coefficient and the ratio of the emotional stress parameter to the baseline stress parameter. The kernel function parameter value of the kernel principal component analysis algorithm is obtained by using the formula that the new kernel function parameter is equal to the baseline kernel function parameter multiplied by the adjustment factor plus one. If the new kernel function parameter is greater than the maximum kernel function parameter value, then the maximum kernel function parameter value is taken. The kernel principal component analysis algorithm is updated using the adjusted kernel function parameters, and the sleep quality parameters are then re-dimension-reduced using the updated algorithm.

6. The method according to claim 1, characterized in that, The process of obtaining dynamic weight coefficients based on the cross-modal association features and weight allocation mechanism includes: Calculate the contribution of each cross-modal association feature to the prediction of treatment effect, and use the ratio of the absolute value of the contribution of each feature to the sum of the absolute values ​​of the contribution of all features as the dynamic weight coefficient of that feature.

7. The method according to claim 1, characterized in that, The process of identifying abnormal patterns in the treatment efficacy index using an anomaly detection algorithm and generating a personalized treatment efficacy evaluation report includes: The termination threshold for obtaining the treatment efficacy index; Starting from the first monitoring point in the treatment effect index sequence, the time node at which the first treatment effect index value is less than or equal to the termination threshold is obtained; Calculate the number of treatment days or treatment cycles from the start of treatment to the specified time point, and generate a personalized treatment effect evaluation report containing treatment recommendations by combining the results of abnormal pattern analysis.

8. A system for evaluating the treatment effectiveness of mental illness using data analysis, characterized in that, For implementing the data analysis-based method for evaluating the treatment effectiveness of mental illnesses as described in any one of claims 1-7, the data analysis-based system for evaluating the treatment effectiveness of mental illnesses comprises: The baseline model building module is used to acquire multidimensional physiological data of patients, perform standardization processing on the multidimensional physiological data to obtain standardized physiological parameters, calculate the statistical characteristics and diurnal rhythm patterns of each standardized physiological parameter, and generate an individualized physiological baseline model by combining individual patient differences. The feature processing module, based on the individualized physiological baseline model, divides the standardized physiological parameters into sleep quality parameters, emotional stress parameters, and voice emotion parameters. The sleep quality parameters are input into the kernel principal component analysis algorithm to obtain the dimensionality-reduced feature vector. The dimensionality-reduced feature vector and the voice emotion parameters are input into the nonlinear mapping model to obtain cross-modal association features. The correlation coefficient of the feature is calculated. If it exceeds the threshold, the kernel function parameters are adjusted in combination with the correlation coefficient and the emotional stress parameters, and the cross-modal association features are re-extracted. The evaluation generation module determines dynamic weight coefficients based on cross-modal association features and weight allocation mechanisms. It then uses these dynamic weight coefficients to perform weighted calculations on standardized physiological parameters to obtain a treatment efficacy index. Anomaly detection algorithms are used to identify abnormal patterns in the treatment efficacy index, generating a personalized treatment efficacy evaluation report.

9. A device for evaluating the treatment effect of mental illness using data analysis, characterized in that, It includes a memory and a processor, the memory storing a computer program that can run on the processor, and the processor executing the computer program to implement the method for evaluating the treatment effect of mental illness using data analysis as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by a processor, the processor performs the method for evaluating the effectiveness of mental illness treatment using data analysis as described in any one of claims 1 to 7.

Images