An osteoporosis prevention strategy analysis method and system

Abstract

The present application relates to the technical field of health care informatics, and discloses an osteoporosis prevention strategy analysis method and system, comprising: a discrete event sequence extraction module extracts clinical events from electronic medical records; a parameter self-adaptive mapping module constructs a time scale scaling factor using glomerular filtration rate to correct the decay parameter of the risk pulse function in reverse, and establish a positive correlation mapping between the decay rate and the clearance efficiency; a time domain superposition analysis module calculates the instantaneous bone loss index and generates the bone density evolution trajectory; and a strategy reverse optimization module simulates intervention superposition to screen the optimal strategy. The present application converts physiological metabolism indicators into time control variables of mathematical models, avoids kinetic distortion caused by individual metabolic differences, and realizes the quantification and early warning of the risk long tail superposition effect in patients with reduced metabolic function.

Description

TECHNICAL FIELD

[0001] The present application relates to an osteoporosis prevention strategy analysis method and system, belonging to the technical field of medical health informatics. BACKGROUND

[0002] Current osteoporosis prevention and management is highly dependent on risk identification and timeliness. In the current medical information system architecture, osteoporosis screening generally follows a static threshold judgment paradigm centered on bone density scan results or biochemical marker concentrations, quantifying individual bone health status as discrete time slice data. When the T value is lower than a certain standard or a brittle fracture occurs, the risk response mechanism is started. This passive management based on result triggering has clinical value in the diagnosis stage, but it is lagging.

[0003] The static threshold judgment logic has limitations in dealing with the dynamics and accumulation of bone loss. Bone loss is the continuous physiological response of the body to discrete event sequences such as long-term drug intervention, disease impact, and lifestyle changes. In clinical settings, patients often face multiple risk factors such as intermittent use of corticosteroids, stage bed rest, or periodic fluctuations in the endocrine system. Existing medical information systems record discrete medical orders and medical record events, but the underlying architecture treats records as independent billing items or text files, lacking an internal computing mechanism to quantify the superposition of discrete events on the time axis and the metabolic long tail effect. The lack of data processing dimensions makes it difficult for the system to capture the cumulative effect of high-frequency minor impacts before the physical changes in bone density.

[0004] Therefore, it is a technical problem to be solved by the present application to establish a deterministic discrete clinical event calculation model, map fragmented medical records to continuous bone metabolism risk evolution trajectories, and quantify and evaluate the superposition effect of multiple risk events on the basis of considering individual metabolic clearance differences and execution friction costs, and generate blocking strategies. SUMMARY

[0005] To solve the problems raised in the background art, the technical solution of the present application is as follows: an osteoporosis prevention strategy analysis system, the system comprising:

[0006] A discrete event sequence extraction module is used to extract the historical medical time series data of the target object from the electronic medical record database through a standard data interface, identify the drug medical order events and disease diagnosis events in the historical medical time series data, and convert the drug medical order events and disease diagnosis events into discrete clinical event sequences containing event type, occurrence time, and dose intensity data.

[0007] The parameter adaptive mapping module is configured to perform time scale dynamic scaling logic based on metabolic clearance efficiency, the logic including extracting a glomerular filtration rate index synchronized with a time window of a discrete clinical event sequence from historical medical time series data, calculating a clearance efficiency ratio of the glomerular filtration rate index relative to a preset standard physiological value and constructing a time scale scaling factor, mapping an independent event in the discrete clinical event sequence into a risk impulse function with an initial intensity parameter and a specific time decay parameter by invoking a preset metabolic response model, and inversely modifying the time decay parameter by using the time scale scaling factor, so that the decay rate value of the risk impulse function is in a positive correlation mapping relationship with the clearance efficiency ratio value, thereby prolonging the effective action period of the risk impulse function in time domain superposition operation by reducing the value of the time decay parameter in a low clearance efficiency ratio state.

[0008] The time domain superposition analysis module is configured to perform convolution superposition operation on the parameter-modified risk impulse function within a preset time window, calculate an instantaneous bone loss rate change index of the target object at the current time, and generate a bone density evolution trajectory based on the instantaneous bone loss rate change index. The strategy reverse optimization module is configured to introduce an intervention strategy function with negative risk intensity, simulate superposition of the intervention strategy function and the bone density evolution trajectory, and output a preventive strategy parameter combination that can maintain the bone density evolution trajectory within a preset safety threshold.

[0009] Preferably, when performing the inverse modification, the parameter adaptive mapping module calculates the modified time decay parameter according to the following modification formula: wherein, represents the modified time decay parameter, represents a preset base decay constant in the metabolic response model, represents the current glomerular filtration rate index of the target object, represents a preset standard physiological value, represents a preset nonlinear response coefficient.

[0010] Preferably, when screening the preventive strategy parameter combination, the strategy reverse optimization module performs entropy value constraint logic based on a friction coefficient, the logic including analyzing a drug administration route, a drug administration frequency and operation constraint conditions of a candidate preventive strategy and calculating a behavior complexity entropy value, generating a friction coefficient based on a compliance baseline constructed by combining historical medical order execution records of the target object, introducing the friction coefficient as a negative penalty term into a target function of strategy optimization, and screening the preventive strategy parameter combination with the goal of maximizing an estimated net benefit, the estimated net benefit being a simulation gain effect minus an execution loss represented by the friction coefficient.

[0011] Preferably, the time-domain superposition analysis module performs confidence weighting logic based on circadian rhythm phase when calculating the instantaneous bone loss rate change indicator, the logic including resolving the sampling time stamps of biochemical test records in the historical medical time series data and mapping to a preset bone metabolism circadian rhythm phase atlas to determine the phase coordinates, calculating the phase offset between the two time point data participating in the difference operation, generating a confidence penalty coefficient based on the phase offset and using the confidence penalty coefficient to reduce the weight of the instantaneous bone loss rate change indicator, so that the greater the phase offset, the lower the contribution weight of the difference result to the bone density evolution trajectory.

[0012] Preferably, the discrete event sequence extraction module performs a synergistic effect recognition logic when extracting the historical medical time series data, the logic including retrieving concurrent events that overlap in the same time window in the discrete clinical event sequence, calling a preset pharmacological interaction rule library to determine whether there is a nonlinear enhancement effect between the concurrent events, and generating a synergistic gain coefficient when there is a nonlinear enhancement effect. The parameter adaptive mapping module uses the synergistic gain coefficient to multiply the initial intensity parameter of the risk pulse function.

[0013] Preferably, the parameter adaptive mapping module includes a segmented pulse construction unit, the segmented pulse construction unit is used for constructing a first risk pulse function with high intensity and short decay and a second risk pulse function with low intensity and long decay for disease diagnosis events with acute onset period and chronic maintenance period characteristics, and the first risk pulse function and the second risk pulse function are combined in series on the time axis to represent the differentiated metabolic impact characteristics of the disease diagnosis events in different stages of the disease course.

[0014] Preferably, the time-domain superposition analysis module further includes a baseline drift correction unit, the baseline drift correction unit is used for obtaining historical bone density scan data of the target object as a physical anchor point, calculating the deviation between the predicted value and the measured value of the bone density evolution trajectory at the time corresponding to the physical anchor point, and using the deviation to inversely calibrate the global sensitivity coefficient in the metabolic response model to minimize the root mean square error between the bone density evolution trajectory and the historical bone density scan data. The strategy inverse optimization module includes a multi-objective game solving unit, the multi-objective game solving unit is used for constructing a weighted objective function including a bone density benefit target, a drug side effect risk target and an economic cost target when the preset safety threshold cannot be satisfied by a single intervention strategy, searching for a local optimal solution of the weighted objective function in a multi-dimensional parameter space using a gradient descent algorithm, and outputting a compromise strategy parameter combination corresponding to the local optimal solution.

[0015] Preferably, the strategy reverse optimization module quantifies the administration route as a first entropy component, quantifies the administration frequency as a second entropy component, quantifies the operation constraint condition as a third entropy component, and defines the weighted sum of the first entropy component, the second entropy component and the third entropy component as the behavior complexity entropy value, wherein the weight coefficient of the first entropy component is greater than the weight coefficients of the second entropy component and the third entropy component.

[0016] Preferably, the system further comprises a data integrity preprocessing module, which is used to interpolate and fill in the missing data in the electronic medical record database before the discrete event sequence extraction module performs the extraction operation, the interpolation and filling is based on the statistical distribution characteristics of the group to which the target object belongs to generate a filling value, and a virtual data identifier is marked for the filling value, and the parameter adaptive mapping module reduces the weight parameter of the corresponding risk pulse function when processing the event with the virtual data identifier.

[0017] An osteoporosis prevention strategy analysis method, which is executed by an osteoporosis prevention strategy analysis system.

[0018] Compared with the prior art, the osteoporosis prevention strategy analysis system has the following beneficial effects:

[0019] 1. In the osteoporosis prevention strategy, a dynamic calculation mechanism for mapping non-numerical clinical events into time-varying signals is established, medical orders and surgical records are converted into mathematical functions with specific intensity and attenuation characteristics, convolution and superposition operations are performed on the functions in the time domain, and the reconstruction of continuous bone metabolism risk trajectories from fragmented medical records is realized, so that the cumulative shock effect of multiple risk events on the time axis is quantitatively evaluated before the physical change of bone density, and the technical problem that the existing medical information system cannot calculate the nonlinear superposition effect between discrete events is solved, and the risk assessment is changed from a static state snapshot to dynamic loss rate monitoring.

[0020] 2. The problem of dynamic distortion of the general risk model when facing individual physiological differences is solved, the patient's glomerular filtration rate physiological index is extracted and converted into a time scale scaling factor for controlling the decay rate of the model, and is not used as an independent risk weighting item, the inverse mapping relationship between the clearance efficiency ratio and the risk function decay constant is established, the tail length of the risk pulse on the time axis is automatically corrected, the parameter-level dynamic modulation logic makes the model adaptively reflect the real drug accumulation and risk residual state in the body of patients with reduced metabolic function, and the existing detection data is used to eliminate the risk underestimation or missed judgment caused by the homogenization of model parameters.

[0021] 3. Introduce human factors engineering constraints when generating intervention strategies, build a multi-objective optimization space containing behavior entropy values, analyze drug administration routes, frequencies and operation constraints, calculate the friction coefficient representing execution resistance, and introduce it as a negative penalty term into the strategy optimization objective function. The calculation logic prompts the algorithm to not only pursue the maximization of theoretical drug efficacy when screening prevention strategies, but also to find the mathematical saddle point between the simulated gain effect and the execution loss. Convert the unstructured compliance difficulty into a quantitative calculation constraint, automatically select the optimal strategy combination with engineering landing feasibility, and avoid the invalidation of the optimal solution due to neglecting execution resistance. BRIEF DESCRIPTION OF DRAWINGS

[0022] Fig. 1 The system logic flow diagram of the present application integrates discrete event extraction and strategy reverse optimization;

[0023] Fig. 2 The bone loss risk trajectory simulation comparison chart based on the metabolic clearance efficiency correction of the present application;

[0024] Fig. 3 The system overall architecture and data interaction schematic diagram of the digital twin algorithm core of the present application. DETAILED DESCRIPTION

[0025] The present application will be described in detail below with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0026] The embodiment of the present application provides an osteoporosis prevention strategy analysis method and system, which comprises a discrete event sequence extraction module, a parameter adaptive mapping module, a time domain superposition analysis module and a strategy reverse optimization module. The discrete event sequence extraction module is used to extract the historical medical time series data of a target object from an electronic medical record database through a standard data interface. This module uses keyword matching or natural language processing logic to identify drug order events and disease diagnosis events in the historical medical time series data, and converts the identified non-numeric events into structured data packets containing event type codes, occurrence time stamps and normalized dose intensity , i.e. discrete clinical event sequences. For events with continuous action time, they are analyzed as a series of closely adjacent independent events or rectangular signals with a certain width, providing definite discrete input variables for subsequent mathematical modeling. The pharmacological interaction rule library uses mechanism classification two-dimensional matrix lookup logic to retrieve the synergistic gain coefficient with concurrent event pharmacological mechanism code as index key value. For common clinical scenarios involving nonlinear enhancement effects, a clearly defined quantitative ladder is pre-set. For example, when glucocorticoid drug events and proton pump inhibitor drug events occur concurrently within a 24-hour time window, based on the latter's inhibition of the active calcium ion transport channel, the rule base outputs a synergistic gain coefficient of 1.2, and the parameter adaptive mapping module follows the formula... The initial intensity parameter A of the risk impulse function is multiplied and corrected, involving the concurrent scenario of enzyme inducers and bone metabolism drugs. Based on the dynamic coefficient of the overlapping integral area of ​​drug half-life, the nonlinear superposition numerical characterization of bone loss risk under multiple pharmacological impacts is realized.

[0027] The parameter adaptive mapping module is used to execute the time scale dynamic scaling logic based on metabolic clearance efficiency. This module retrieves glomerular filtration rate indicators synchronized with the time window of discrete clinical event sequences from historical biochemical test records. If direct measurements are unavailable, an estimated value is calculated using serum creatinine concentration. This module calls a pre-built metabolic response model to map independent events in a discrete clinical event sequence to events with initial intensity parameters. and basic aging decay parameters The risk impulse function is given by the following functional form: Based on this, the module calculates the glomerular filtration rate index. Compared to the preset standard physiological value The cleaning efficiency is higher than And construct the time scale scaling factor, using the correction formula The basic aging decay parameters are inversely corrected, whereby... The preset nonlinear response coefficient ranges from 0.5 to 0.8. This correction logic ensures that when... That is, when the removal efficiency is lower than the standard value, the corrected aging decay parameter. Automatic reduction, thereby prolonging the decay tail of the risk impulse function, characterizes the long-term cumulative effect of drug accumulation or pathological shock on bone metabolism under conditions of impaired metabolic function.

[0028] The time-domain overlay analysis module is used to perform convolution overlay operations on the parameter-corrected risk impulse function within a preset time window. This module establishes a continuous time axis covering the backtracking period and the prediction period. For any current moment on the time axis... Retrieve all events that have occurred before this moment and whose risk impulse functions are... The residual value of discrete events that have not yet decayed to zero is calculated, and the sum of the responses of all valid risk impulses at the current moment is used to generate an index of instantaneous bone loss rate change. , the module takes the baseline bone mineral density of the target object or the population average bone mineral density as the integral initial value, and performs numerical integration on the time domain of the instantaneous bone loss rate change index to generate a bone mineral density evolution trajectory reflecting the continuous change of bone mineral density with time; when the time domain superposition analysis module calculates the intermediate variable involving the change of biochemical index, the confidence weighting logic based on the phase of circadian rhythm is executed, the sampling time stamp of the biochemical test record is analyzed and mapped to the preset 24-hour bone metabolism circadian rhythm phase map to determine the phase coordinates When calculating the differential change rate of the data at two time points, the module calculates the phase offset between the two And according to the formula , the confidence weight coefficient is generated , wherein is a preset rhythm sensitivity constant, the module uses the confidence weight coefficient to weight and correct the differential calculation result, and reduces the contribution weight of large phase offset data to trend evaluation.

[0029] Strategy reverse optimization module, for introducing intervention strategy function with negative risk intensity, and executing entropy value constraint logic based on friction coefficient, the module defines a group of candidate intervention strategies, each strategy corresponds to a negative pulse sequence with specific intensity and frequency, the module analyzes the administration route, administration frequency and operation constraint condition of each candidate strategy, respectively calculates the administration route entropy , administration frequency entropy and operation constraint entropy , and weighted sum to get the behavior complexity entropy value When calculating the behavior complexity entropy value, the strategy reverse optimization module quantitatively executes the standard complexity mapping procedure based on the operation constraint condition, converts the unstructured medical order text into dimensionless third entropy component value based on the preset constraint load lookup table, maps the normal oral administration without special diet or body position requirement to the reference value 0, maps the constraint condition of taking medicine with meals or taking medicine at specific time interval to the quantized value 0.3, maps the strict physical restriction constraint condition such as the requirement of taking bisphosphonate drugs on an empty stomach in the morning and keeping upright posture for at least 30 minutes after taking medicine to the high resistance behavior mapped to the quantized value 0.8, as an independent weighted item input into the behavior complexity entropy value accumulation formula, reflecting the negative friction resistance of high constraint medical behavior to patient compliance; At the same time, the module analyzes the historical medical order execution record of the target object, constructs the compliance baseline coefficient , and generates the dimensionless friction coefficient based on the behavior complexity entropy value When executing strategy optimization, the module constructs the target function containing the pharmacodynamic gain term and the friction loss term , wherein is a preset penalty weight, the module searches for a combination of parameters that maximizes the objective function under the premise of ensuring the preset safety threshold, and outputs a combination of prevention strategy parameters that minimizes resistance and maximizes net income.

[0030] Embodiment 1: In the clinical management scenario for an elderly osteoporosis high-risk group with chronic metabolic dysfunction, the target object is a 72-year-old female patient whose clinical characteristics are intermittent use of glucocorticoids to control chronic obstructive pulmonary disease, accompanied by a decrease in glomerular filtration rate caused by stage 3 chronic kidney disease, and the bone density scan result shows a T value of -1.5, which is in the non-high-risk interval under the existing static threshold determination method. In the working condition of multiple risk factors interweaving and long-term implicit accumulation without reaching the warning line for a single drug impact dose, the discrete event sequence extraction module traverses the target object's 36-month historical medical time series data through a standard data interface, identifies 7 prednisone drug prescription events scattered in different time windows, and converts each medication record into a discrete clinical event sequence containing the occurrence time timestamp and the normalized dose intensity , converts the non-numeric medical record text into a deterministic time-varying signal input, and the parameter adaptive mapping module performs time scale dynamic scaling logic based on metabolic clearance efficiency. The glomerular filtration rate index is extracted from the biochemical test records synchronized with the above prednisone prescription event time , and compared with the preset standard physiological value to obtain a clearance efficiency ratio less than 1. Given the target object's reduced kidney clearance capacity, the module calls the metabolic response model and uses the correction formula to inversely correct the pre-set basic time decay parameter . Since and the response coefficient is a positive real number, the corrected time decay parameter is less than the base value, making the risk pulse function describing each hormone impact have a reduced decay rate on the time axis, with the effective action period and tail length numerically extended, effectively reproducing the accumulation process of the drug in the impaired body. The time-domain superposition analysis module performs convolution and superposition operations on these parameter-corrected long-tail risk pulse functions on the constructed continuous time axis, and the decay tail of the previous pulse is extended due to the decrease in the residual risk value , resulting in an additive effect that is not apparent under the standard model between the residual risk value and the subsequently occurring pulse, and the calculated instantaneous bone loss rate change index is in a high oscillation state at the current time, indicating that the bone metabolism dynamics is in a high loss state when the static T value does not reach the threshold.

[0031] The strategy reverse optimization module executes the entropy value constraint logic based on the friction coefficient, which calculates the high behavioral complexity entropy value of the daily oral bisphosphonate regimen due to the high frequency of administration and multiple operation constraints , and the friction coefficient generated in combination with the historical compliance baseline of the target object , which increases in turn, resulting in the reduction of the estimated net benefit in the friction loss term of the objective function The system screens out an intravenous injection regimen with a frequency of once every half year, which maximizes the overall net benefit in the objective function due to the low behavioral entropy and friction coefficient, and outputs the optimal parameter combination that can maintain the bone density evolution trajectory within the safety threshold.

[0032] Example 2: To verify the accuracy of the time scale dynamic scaling logic based on metabolic clearance efficiency in quantifying the superimposed effect of multiple risks, a simulation verification platform is constructed in this embodiment, which integrates de-identified electronic medical record data, pharmacokinetic simulation engine and bone metabolism dynamics model, to simulate the physiological response of patients to intermittent glucocorticoid shock in different renal function states in real clinical scenarios. In this test, 50 virtual patients with a long history of glucocorticoid use and complete renal function monitoring records are selected, divided into a test group and a control group, 25 people in each group. The test group uses the risk assessment logic of the parameter adaptive mapping logic of the present application, and the control group uses the fixed parameter model, ignoring the correction effect of glomerular filtration rate on the decay parameter For each patient, the system inputs the same historical medication event sequence, including drug type, dose and administration time, and synchronously inputs the corresponding time series glomerular filtration rate index. The nonlinear response coefficient of the core parameter is set to 0.7, which is determined based on the power law relationship between renal clearance rate and drug half-life in previous pharmacokinetic studies. The test takes the correlation coefficient between the instantaneous bone loss rate change index output by the model and the patient's contemporaneous measured bone resorption biochemical markers such as CTX level as the observation endpoint. During the test, for patients with normal renal function , there is no difference in the risk trajectory output by the models of the two groups. For patients with moderate to severe renal dysfunction , the risk pulse function generated by the test group model presents an extended decay tail due to the introduction of the time scale scaling factor When the clearance efficiency ratio drops to 0.5, the corrected time-dependent decay parameter is about 70% of the base value The effective risk window for a single drug event was extended by about 1.6 times, accounting for 61% of the total. This long tail effect caused multiple drug events that were originally discrete in time to have a strong superposition resonance in the model calculation of the experimental group. Table 1 shows the comparison of risk assessment data of patients with renal impairment under two consecutive prednisone pulses with an interval of 4 weeks.

[0033] Table 1: Comparison of Risk Assessment Data for Patients with Renal Impairment )surface

[0034]

[0035] Referring to Table 1, at time T3 (after the second dose), the control group only reflects the risk level of a single dose (0.88), while the experimental group, due to the residual risk of the first dose, has a higher output value (1.25) and shows a stronger positive correlation with the measured CTX change rate (+62%). This data indicates that the present invention, by dynamically correcting the attenuation parameter, restores the risk accumulation amplification mechanism caused by drug accumulation in the body with impaired metabolism. This embodiment confirms that the dynamic scaling logic of the time scale based on metabolic clearance efficiency can eliminate the kinetic distortion caused by individual metabolic differences. Under the condition of low clearance efficiency ratio, this logic can capture the long-tailed superimposed risk missed by the traditional model, so that the generated bone density evolution trajectory truly reflects the bone metabolic instability state in the patient's body.

[0036] Example 3: This example combines Figs. 1 to 3 This section provides an analytical method and systematic description of an osteoporosis prevention strategy, such as... Fig. 1 As shown, the processing flow begins with an electronic medical record database, which contains historical medical time-series data. A data integrity preprocessing module interpolates and fills in missing data based on population statistical characteristics and marks virtual data. The data then flows into a discrete event sequence extraction module, which not only identifies medication orders and disease diagnosis events but also runs synergy effect identification logic to generate synergy gain coefficients, thus producing a discrete clinical event sequence containing timestamps and dosages. This sequence then enters a parameter adaptive mapping module, where the system executes dynamic scaling logic based on the glomerular filtration rate to adjust the time-lapse parameter of the risk impulse function. The data is then reverse-corrected and segmented to construct pulse models that distinguish between acute exacerbation and chronic maintenance phases. The corrected data enters the time-domain overlay analysis module, where risk pulses are convolved and overlaid, and confidence-weighted based on circadian rhythm phases to eliminate rhythmic noise and generate instantaneous bone loss rate and bone density evolution trajectories. Finally, the strategy reverse optimization module introduces an intervention strategy function with negative risk intensity. Considering compliance, entropy constraint logic based on friction coefficient is executed to maximize the estimated net benefit while maintaining it within a safe threshold. The output includes a combination of prevention strategy parameters, including administration route, frequency, and operational constraints.

[0037] like Fig. 2 As shown, in the comparative validation of risk assessment for patients with impaired renal function, the horizontal axis represents continuous time points from T1 to T4, and the vertical axis represents standardized values. The figure shows three curves that change over time, where the dashed line represents the control group using a fixed parameter k. Data, solid lines represent data using adaptive parameters. experimental group The data, with dotted lines representing the measured CTX change rate, showed that at time T1 (one week after the first dose), the responses of the three groups were basically consistent. However, as time progressed to time T2 and T3 (three weeks after the first dose and one week after the second dose), the experimental group curves exhibited high-level oscillations and long-tailed residual characteristics. The peak shape of these curves closely matched the trend of the measured CTX change rate. In contrast, the control group curves decayed prematurely due to a lack of correction for metabolic clearance efficiency. This indicates that the experimental model, under low clearance efficiency conditions, captured the nonlinear additive effect caused by drug accumulation by extending the effective duration of the risk pulse. Fig. 3 As shown, from the perspective of the overall system architecture and data interaction links, the full-volume medical data lake on the left includes electronic medical record data from the Hospital Information System (HIS) and renal function and bone metabolism indicators from the Biochemical Testing System (LIS). This data is transmitted to the core bone metabolism dynamics analysis server through a standardized data interface. This server, as the core of digital twin mathematical modeling, forms a closed-loop dynamic calculation process, which includes four calculation stages: discrete event serialization, parameter adaptive mapping, time-domain superposition simulation, and strategy reverse optimization. The results after the calculation are completed are output to the clinical decision terminal on the right in an interactive manner. Specifically, this is manifested in the display of risk trajectory diagrams and optimal strategy tables on the doctor's or pharmacist's workstation, thereby assisting medical personnel in making dynamic osteoporosis risk management and intervention decisions.

[0038] Example 4: To address the issue of rhythmic noise interference caused by the randomness of sampling time in existing biochemical index difference calculations, this example incorporates a signal confidence weighting procedure based on the diurnal rhythm phase. In bone metabolism dynamics monitoring scenarios, bone resorption markers such as CTX exhibit diurnal rhythm fluctuations regulated by the biological clock, with the difference between the morning peak and the afternoon trough reaching over 30%. Directly calculating the difference between sampled data at random times can easily misjudge physiological rhythmic fluctuations as pathological accelerated bone loss. Therefore, this example constructs a timestamp-driven phase locking and weighted calculation module. This module parses the sampling timestamp of each biochemical test record and maps it to a preset 24-hour bone metabolism diurnal rhythm phase map. The system divides the 24-hour cycle into several phase intervals, for example, defining 02:00-08:00 as the high-absorption phase region and 14:00-20:00 as the low-absorption phase region, and assigns normalized phase coordinates to each sampling point. When calculating two time points and The system synchronously calculates the phase offset of both in the phase map when the biochemical index change rate of .

[0039] Based on the phase offset, the system calls the confidence penalty function to generate the weight coefficient , the penalty function is set as , where is the rhythm sensitivity constant, the value range is set to 0.5 to 2.0, when the two sampling points are in the same phase , the tends to 1, indicating that the difference result has high confidence; as the phase offset increases, rapidly decays, indicating that the difference result is mixed with more rhythm noise, and the confidence decreases. The system finally uses the weight coefficient to reduce the weight of the calculated instantaneous change rate, the formula is Through this procedure, the system can automatically identify and suppress the rhythmical artifacts introduced by non-standard sampling time, ensuring that the metabolic instability index output truly reflects the pathological trend changes.

[0040] To solve the problem of decreased model adaptability when the system faces unknown or newly released clinical data distribution, this embodiment constructs an offline calibration and on-site deployment pre-debugging mechanism based on double calibration procedures, aiming to ensure that the core parameters and reference benchmarks can be accurately anchored to the data ecology and target population characteristics of a specific medical institution before the system is put into use, thereby ensuring the robustness and reproducibility of risk assessment. The offline calibration procedure establishes a standard reference data set containing multi-center, multi-disease historical cases, covering patients of different ages, genders, renal function classifications, and drug exposure histories. The system uses this data set to perform batch backtesting on the pre-set metabolic response model, and by minimizing the root mean square error between the predicted risk trajectory and the actual bone density change, the nonlinear response coefficient and the rhythm sensitivity constant are globally optimized. This process does not rely on a single empirical setting, but rather traverses the parameter space to establish a set of benchmark parameters that can maximize the model's generalization ability in a statistical sense. At the same time, the system constructs a lookup table of standardized initial intensity parameters and basic decay constants for different types of drug order events based on pharmacological half-life and bioavailability data.

[0041] The on-site pre-deployment calibration procedure focuses on the adaptation of the system to the specific medical institution data environment. Before the system goes online, a background noise analysis is performed on the biochemical test data of the institution. By collecting blank sample or quality control sample data for 72 consecutive hours, the system error baseline and random noise level of the institution's detection equipment are calculated. Based on the noise analysis results, the system automatically fine-tunes the threshold parameters in the differential calculation of biochemical indicators to filter out invalid fluctuations below the detection limit of the equipment. In addition, the system extracts the institution's bone density scan data from the past year to construct a localized bone density distribution map, and based on this, the preset safety threshold within the system is normalized and calibrated to ensure that the sensitivity and specificity of the risk warning meet the actual needs of the institution's clinical diagnosis and treatment.

[0042] Example 6: To ensure that the osteoporosis prevention strategy analysis system of the present application has high stability and reproducibility in diversified medical information ecology and clinical scenarios, a set of deterministic offline parameter calibration and on-site environment adaptation procedure is constructed. This procedure aims to eliminate subjective uncertainty in model parameter setting through standardized engineering steps and achieve adaptive fine-tuning for specific data environments. The offline calibration procedure constructs a standard reference data set containing multi-center, multi-disease historical cases. This data set covers patients of different ages, genders, kidney function grades, and drug exposure histories, serving as a benchmark for model parameter optimization. The system uses this data set to perform batch backtesting on the pre-set metabolic response model, aiming to minimize the root mean square error between the predicted risk trajectory and the actual bone density change. The system performs global optimization on the non-linear response coefficient and the rhythm sensitivity constant by traversing the pre-set parameter space to establish a set of benchmark parameters that can maximize the model's generalization ability in a statistical sense. At the same time, the system constructs a standardized lookup table of initial intensity parameters and basic decay constants for different types of drug order events based on pharmacological half-life and bioavailability data.

[0043] The on-site pre-deployment calibration procedure focuses on the adaptation of the system to the specific medical institution data environment. Before the system goes online, a background noise analysis is performed on the biochemical test data of the institution. The system collects blank sample or quality control sample data for 72 consecutive hours, calculates the system error baseline and random noise level of the institution's detection equipment, and automatically fine-tunes the threshold parameters in the differential calculation of biochemical indicators based on this, to filter out invalid fluctuations below the detection limit of the equipment. In addition, the system extracts the institution's bone density scan data from the past year to construct a localized bone density distribution map, and based on this, the preset safety threshold within the system is normalized and calibrated to ensure that the sensitivity and specificity of the risk warning meet the actual needs of the institution's clinical diagnosis and treatment.

[0044] It is apparent for a person skilled in the art that the present application is not limited to the details of the above-described exemplary embodiments, but that the present application can be implemented in other concrete forms without departing from the spirit or essential characteristics of the present application.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not to limit the present application, and although the present application is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. An osteoporosis prevention strategy analysis system, characterized in that, The system includes: The discrete event sequence extraction module is used to extract historical medical time-series data of the target object from the electronic medical record database through a standard data interface, identify drug prescription events and disease diagnosis events in the historical medical time-series data, and convert drug prescription events and disease diagnosis events into discrete clinical event sequences containing event type, occurrence time and dosage intensity data. The parameter adaptive mapping module is used to execute the time scale dynamic scaling logic based on metabolic clearance efficiency. The logic includes extracting the glomerular filtration rate index synchronized with the time window of the discrete clinical event sequence from historical medical time series data, calculating the clearance efficiency ratio of the glomerular filtration rate index relative to the preset standard physiological value and constructing the time scale scaling factor, calling the preset metabolic response model to map the independent events in the discrete clinical event sequence into risk impulse functions with initial intensity parameters and specific time-effect decay parameters, and using the time scale scaling factor to inversely correct the time-effect decay parameter so that the decay rate value of the risk impulse function is positively correlated with the clearance efficiency ratio value. Thus, under the condition of low clearance efficiency ratio, the effective period of the risk impulse function in the time domain superposition operation is extended by reducing the value of the time-effect decay parameter. The temporal overlay analysis module is used to perform convolution overlay operations on the parameter-corrected risk impulse function within a preset time window, calculate the instantaneous bone loss rate change index of the target object at the current moment, and generate a bone density evolution trajectory based on the instantaneous bone loss rate change index; and the strategy reverse optimization module is used to introduce an intervention strategy function with negative risk intensity, and superimpose the intervention strategy function with the bone density evolution trajectory through simulation, reversely filter out the combination of prevention strategy parameters that can keep the bone density evolution trajectory within a preset safety threshold, and output it.

2. The osteoporosis prevention strategy analysis system according to claim 1, characterized in that, When performing reverse correction, the parameter adaptive mapping module calculates the corrected aging decay parameter according to the following correction formula: ,in, This represents the corrected aging decay parameter. This represents the pre-defined baseline decay constant in the metabolic response model. This indicates the current glomerular filtration rate of the target individual. This represents the preset standard physiological value. This represents the preset nonlinear response coefficient.

3. The osteoporosis prevention strategy analysis system according to claim 1, characterized in that, The strategy reverse optimization module executes entropy constraint logic based on friction coefficient when screening combination of prevention strategy parameters. The logic includes parsing the administration route, administration frequency and operational constraints of candidate prevention strategies and calculating the behavioral complexity entropy value. It generates a friction coefficient by combining the compliance baseline constructed from the historical medical order execution records of the target object. The friction coefficient is introduced as a negative penalty term into the objective function of strategy optimization. The goal is to screen combination of prevention strategy parameters with the aim of maximizing the estimated net benefit. The estimated net benefit is the simulated gain effect minus the execution loss represented by the friction coefficient.

4. The osteoporosis prevention strategy analysis system according to claim 1, characterized in that, The time-domain overlay analysis module executes confidence-weighted logic based on circadian rhythm phase when calculating the instantaneous bone loss rate change index. The logic includes parsing the sampling timestamps of biochemical test records in historical medical time-series data and mapping them to a preset bone metabolism circadian rhythm phase map to determine the phase coordinates, calculating the phase offset between the two time point data involved in the difference operation, generating a confidence penalty coefficient based on the phase offset, and using the confidence penalty coefficient to reduce the weight of the instantaneous bone loss rate change index, so that the difference result with a larger phase offset has a lower contribution weight to the bone density evolution trajectory.

5. The osteoporosis prevention strategy analysis system according to claim 1, characterized in that, The discrete event sequence extraction module executes synergistic effect identification logic when extracting historical medical time series data. The logic includes retrieving concurrent events that overlap within the same time window in the discrete clinical event sequence, calling a pre-set pharmacological interaction rule base to determine whether there is a nonlinear enhancement effect between concurrent events, and generating a synergistic gain coefficient when a nonlinear enhancement effect exists. The parameter adaptive mapping module uses the synergistic gain coefficient to multiply and correct the initial intensity parameter of the risk impulse function.

6. The osteoporosis prevention strategy analysis system according to claim 1, characterized in that, The parameter adaptive mapping module includes a segmented pulse construction unit, which is used to construct a high-intensity, short-decay first risk pulse function and a low-intensity, long-decay second risk pulse function for disease diagnostic events with acute exacerbation and chronic maintenance phase characteristics, respectively. The first risk pulse function and the second risk pulse function are then combined in series on the time axis to characterize the differentiated metabolic impact characteristics of disease diagnostic events at different disease stages.

7. The osteoporosis prevention strategy analysis system according to claim 1, characterized in that, The temporal overlay analysis module also includes a baseline drift correction unit. This unit acquires historical bone density scan data of the target object as physical anchor points, calculates the deviation between the predicted and measured values ​​of the bone density evolution trajectory at the corresponding time of the physical anchor point, and uses the deviation to calibrate the global sensitivity coefficient in the metabolic response model to minimize the root mean square error between the bone density evolution trajectory and the historical bone density scan data. The strategy reverse optimization module includes a multi-objective game solving unit. This unit constructs a weighted objective function that includes bone density benefit objectives, drug side effect risk objectives, and economic cost objectives when a preset safety threshold cannot be met by a single intervention strategy. It uses the gradient descent algorithm to search for a local optimum of the weighted objective function in the multi-dimensional parameter space and outputs the compromise strategy parameter combination corresponding to the local optimum.

8. The osteoporosis prevention strategy analysis system according to claim 3, characterized in that, When calculating the behavioral complexity entropy value, the strategy reverse optimization module quantifies the drug administration route as the first entropy component, the drug administration frequency as the second entropy component, and the operational constraints as the third entropy component. The weighted sum of the first, second, and third entropy components is defined as the behavioral complexity entropy value, where the weight coefficient of the first entropy component is greater than the weight coefficients of the second and third entropy components.

9. The osteoporosis prevention strategy analysis system according to claim 1, characterized in that, The system also includes a data integrity preprocessing module, which is used to interpolate and fill missing data in the electronic medical record database before the discrete event sequence extraction module performs the extraction operation. The interpolation and filling generates fill values ​​based on the statistical distribution characteristics of the target object's group and marks the fill values ​​with virtual data identifiers. The parameter adaptive mapping module reduces the weight parameters of the corresponding risk impulse function when processing events with virtual data identifiers.

10. A method for analyzing osteoporosis prevention strategies, characterized in that, This method is performed by the osteoporosis prevention strategy analysis system described in claim 1.

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