Comprehensive analysis system for medical health data

Abstract

The invention discloses a comprehensive analysis system for medical health data, and the method comprises the following steps: extracting multi-dimensional clinical features from the medical health data, calculating a K theoretical index and a Cheesimons invariant, and obtaining a health topology spectrum index; on the basis of the topological spectrum index and continuous physiological monitoring, calculating the variation derivative of the Bennacadokr action quantity, and generating a causal structure collapse index in combination with Hessian matrix spectrum distribution; calculating a holographic critical dimension index in combination with the collapse index and the organ system score data; constructing a hypersymmetric theoretical framework according to the dimension index and clinical intervention historical data, and generating a topology protection intervention index; and fusing the intervention index with patient and environment multi-dimensional constraints, establishing ternary system function mapping under a high-order category theory framework, and calculating topology decision invariants. The accuracy of the medical intervention strategy is improved by analyzing the medical health data.

Description

Technical Field

[0001] This invention relates to the medical field, and more particularly to a comprehensive analysis system for medical and health data. Background Technology

[0002] With the explosive growth and high heterogeneity of healthcare data, data sources encompass electronic health records, continuous vital sign monitoring, multi-omics data, structured medical imaging features, routine laboratory tests, patient-reported symptom scales, and behavioral data. This multimodal information evolves dynamically in a high-frequency, high-dimensional, nonlinear, and non-stationary manner. Traditional analysis methods are struggling to effectively integrate multi-source information, capture the nonlinear coupling relationships between physiological systems across scales, and reveal the hidden dynamic topological structures in the course of disease development, or identify critical phase transition points in disease evolution. Especially in scenarios such as intensive care, chronic disease management, and early warning, clinical needs have shifted from describing what has happened to predicting what will happen and how to best intervene. Based on this, this invention proposes a comprehensive analysis system for healthcare data. Summary of the Invention

[0003] This invention provides a comprehensive analysis method for medical and health data, characterized by comprising: S10. Based on the multidimensional clinical features extracted from medical and health data, calculate the K-theory index and the Chern-Simons invariant to generate the health topology spectrum index. S20. Based on the health topology spectrum index and the tensor flow of continuous physiological monitoring, a causal structure collapse index is generated. S30. Calculate the holographic critical dimension index by combining the causal structure collapse index and organ system score data; S40. Generate a topological protection intervention index based on the holographic critical dimension index and historical clinical intervention data; S50 integrates the topological protection intervention index with multidimensional constraints of patients and environment, establishes a ternary system functor mapping of medical, behavioral, and environmental systems under a higher-order category theory framework, and calculates topological decision invariants.

[0004] The comprehensive analysis method for medical and health data described above, which calculates K-theory indicators and Chern-Simons invariants based on multidimensional clinical features extracted from the medical and health data, generates a health topological spectrum index, specifically comprising the following sub-steps: Electronic health records, laboratory test results, vital sign monitoring data, structured features of medical images, and patient-reported symptom scales were extracted and integrated into a multidimensional clinical feature set. After time alignment, missing value processing, and standardization, a high-dimensional temporal feature matrix was constructed. Based on the high-dimensional temporal feature matrix, nested clustering of p-progression, supermetric tree topology of Banach space and scale invariance of canonical expansion are used to self-organize fractal geometric structures in non-Archimedean space, so that the critical mutation points of clinical state transitions exhibit topologically consistent self-similar features at different scales. Based on non-Archimedean geometry, a healthy topological spectral index is generated by constructing spectral triples of non-commutative geometry and applying the Kon-Nechchen eigenmap to calculate K-theory indices and Chern-Simons invariants.

[0005] The comprehensive analysis method for medical and health data described above, which generates a causal structure collapse index based on the health topology spectrum index and continuous physiological monitoring tensor flow, specifically consists of the following sub-steps: By precisely aligning the health topology spectrum index with high-frequency vital sign time-series data collected by hospital monitoring systems and wearable devices according to timestamps, a spatiotemporal fusion data matrix is ​​constructed, providing a data foundation with both temporal resolution and topological stability for critical point dynamics analysis; A discrete causal set dynamics framework is constructed based on a spatiotemporal fusion data matrix. This framework identifies critical points of health transition by constructing a directed acyclic graph and calculating the Benedict's Sadocer action, and generates a continuous probability curve distribution map to provide early warning of the risk of an individual's health state transitioning to a pathological state. Based on the critical points in the continuous probability curve distribution map, a Morse-Small complex is constructed. The distribution of the Hessian matrix spectrum is analyzed using adaptive fractional calculus, and a causal structure collapse index is generated. This index, together with the continuous probability curve distribution map, forms a complete early warning system that complements the long-term and short-term perspectives.

[0006] The comprehensive analysis method for medical and health data described above involves constructing a Morse-Small complex based on the critical points in the continuous probability curve distribution graph, using adaptive fractional calculus to analyze the Hessian matrix spectral distribution, and generating a causal structure collapse index. This index, together with the continuous probability curve distribution graph, forms a complete early warning system that complements long-term and short-term data. The method is further divided into the following sub-steps: By taking the critical points in the continuous probability curve distribution graph as the stationary points of the Morse function, and calculating the zero-point stability of the gradient field at each critical point in the graph, the critical points are classified into health attractors, critical saddle points, and pathological repulsion points. By applying the cavity decomposition algorithm, based on the gradient flow trajectory connecting critical points of different types, a network of intersection between the stable manifold (i.e., the set of trajectories flowing into critical points) and the unstable manifold (i.e., the set of trajectories flowing out of critical points) is constructed, forming the Morse-Small complex topology of the healthy system. Based on this complex, fractional spectral density of the Hessian matrix at each critical point is calculated using fractional calculus techniques, and adaptive fractional derivatives of the proportion of eigenvalues ​​in the right half-plane of the spectrum are calculated. By accumulating only the positive parts, integrating over time and dividing by the standardized time scale parameter, and applying an exponential decay function transformation process, the causal structure collapse index is generated.

[0007] The comprehensive analysis method for medical and health data described above, which combines the causal structure collapse index and organ system score data to calculate the holographic critical dimension index, specifically includes the following sub-steps: By integrating the causal structure collapse index and functional assessment data of six major organ systems, a physiological-pathological supernetwork was constructed, and the high-dimensional topology of the supernetwork was reduced to a low-dimensional feature vector through graph embedding technology. By using the holographic duality principle, the dimensionality-reduced physiological-pathological supernetwork is mapped into a five-dimensional anti-de Sitter space geometric structure. The Liu-Gao-Liu formula is used to calculate the area of ​​the extreme surface and the Gauss-Bonnet curvature integral, transforming the complex network dynamics into monitorable geometric distortion features and identifying the distribution of fragile structures. Based on the five-dimensional anti-de Sitter space geometry, the quantum extremum surface and conformal anomalous correction term of the extended Liu-Gao-Liu formula are applied to analyze the high-order correlation entropy gradient of multiple organs. The most vulnerable dimension of the system is identified by the Jacobian matrix feature spectrum, and the holographic critical dimension index is calculated.

[0008] The comprehensive analysis method for medical and health data described above, which generates a topological protection intervention index based on the holographic critical dimension index and historical clinical intervention data, specifically includes the following sub-steps: By integrating holographic critical dimension index time series with electronic prescriptions and treatment records from multiple institutions, and constructing a dynamic intervention knowledge base with a graph database architecture through federated learning and causal inference algorithms, a decision-making basis is provided for personalized treatment that accurately matches dimensional collapse paths. Based on the intervention knowledge base, a supersymmetric theoretical framework is constructed, and the intervention measures are modeled as instantaneous solutions with topological protection properties. The robustness of the intervention is achieved by topological charge quantization and the critical dimension is dynamically focused by supersymmetry breaking mechanism, so as to realize the paradigm shift of precision medicine from traditional dose standardization to topological stability optimization. Within the framework of supersymmetric theory, dimensional collapse paths are mapped to four-dimensional manifolds. Seiberg-Witten and Donaldson-Thomas invariants and their cross-modulo variables are calculated to identify topological singularities and energy barriers, generating topological protection intervention indices, thereby optimizing intervention sequences with minimum energy consumption and highest topological stability.

[0009] The comprehensive analysis method for medical and health data described above integrates the topological protection intervention index with multidimensional constraints of patients and the environment, establishes a ternary system functor mapping of medical, behavioral, and environmental aspects within a higher-order category theory framework, and calculates topological decision invariants. Specifically, it comprises the following sub-steps: By integrating the topological protection intervention index with multidimensional sociodemographic data, and establishing a dynamic correlation between physiological dimensional stability and external environmental factors through multimodal alignment, an enhanced decision-making dataset is generated to provide a comprehensive decision-making basis for generating individualized clinical intervention plans. Based on an enhanced decision dataset, a higher-order category theory framework is constructed. Through the medical-behavior-environment ternary functor mapping and its natural transformation under the framework, a dynamic decision space is generated, which can accurately identify the minimum energy intervention sequence that can still maintain the overall stability of the system under social environmental disturbances. By calculating the homotopy group order of the decision manifold to determine the system stability constraints, and combining topological field theory to weave matrix planning for precise intervention timing, topological decision invariants with quantifiable stability are generated, and the optimal transition path from pathological state to healthy state with minimum energy and global robustness is identified.

[0010] This invention also provides a comprehensive analysis system for medical and health data, comprising: Topology Spectrum Index Module: Based on multidimensional clinical features extracted from medical and health data, calculate K-theory indicators and Chern-Simons invariants to generate a health topology spectrum index; Collapse Index Module: Based on the health topology spectrum index and continuous physiological monitoring tensor flow, a causal structure collapse index is generated; Dimension Index Module: Combines causal structure collapse index and organ system score data to calculate holographic critical dimension index; Intervention Index Module: Generates a topological protection intervention index based on the holographic critical dimension index and historical clinical intervention data; Topology Decision Module: Integrates the topology protection intervention index with multidimensional constraints of patients and environment, establishes a functor mapping of the medical, behavioral and environmental ternary system under the framework of higher-order category theory, and calculates topology decision invariants.

[0011] The beneficial effects achieved by this invention are as follows: This invention improves the accuracy of medical intervention strategies by analyzing medical and health data. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0013] Figure 1 This is a flowchart of a comprehensive analysis method for medical and health data provided in Embodiment 1 of this application. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Example 1 like Figure 1 As shown, Embodiment 1 of this application provides a comprehensive analysis method for medical and health data, including: S10. Based on the multidimensional clinical features extracted from medical and health data, calculate the K-theory index and the Chern-Simons invariant to generate the health topology spectrum index.

[0016] S11. Extract and integrate electronic health records, laboratory test results, vital sign monitoring data, structured features of medical images, and patient-reported symptom scales into a multidimensional clinical feature set. After time alignment, missing value processing, and standardization, a high-dimensional temporal feature matrix is ​​constructed.

[0017] Structured diagnostic codes, medication records, past medical history, and frequency of visits are extracted from electronic health records; blood biochemical indicator sequences, including time-series data of blood glucose, blood lipids, liver and kidney function, as well as inflammatory markers and blood cell counts, are extracted from routine laboratory tests; basic vital signs, including heart rate variability, blood pressure fluctuation patterns, respiratory rate, and diurnal temperature rhythm, are obtained from hospital monitoring systems; structured imaging features, including quantitative indicators such as organ volume ratios from computed tomography and magnetic resonance imaging, ejection fraction from echocardiography, and texture features from X-ray images, are extracted from medical imaging systems; and subjective health status data, including sleep quality scores, pain intensity distribution, mood fluctuation records, and functional status assessments, are extracted from standardized patient-reported symptom scales. These data form a multidimensional clinical feature set, which, after time alignment, missing value imputation, and standardization, forms a high-dimensional temporal feature matrix. This matrix not only contains static clinical parameters but also captures the dynamic changes in individual physiological systems, providing comprehensive and in-depth foundational data for constructing personalized health topology representations.

[0018] S12. Based on the high-dimensional temporal feature matrix, nested clustering of p-progression, supermetric tree topology of Banach space, and scale invariance of canonical expansion are used to self-organize fractal geometric structures in non-Archimedean space, so that the critical mutation points of clinical state transitions exhibit topologically consistent self-similar characteristics at different scales.

[0019] Reconstructing non-Archimedean geometric structures based on high-dimensional time-series feature matrices captures more complex topological characteristics of health systems. First, suitable prime parameters are selected: the singular value spectrum of the high-dimensional time-series feature matrix is ​​calculated to determine its rank and condition number. Then, the periodic patterns of the high-dimensional time-series feature matrix data under modular arithmetic are analyzed, where the modulus is a candidate prime number. A prime number p is selected that satisfies two conditions: first, the prime number is greater than the 95th percentile of the eigenvalue distribution of the high-dimensional time-series feature matrix; second, the prime number is coprime with the effective dimension of the subspace after principal component analysis of the high-dimensional time-series feature matrix and projection of the matrix onto a low-dimensional subspace spanned by the principal components. Each clinical feature vector in the high-dimensional time-series feature matrix is ​​converted into a clinical feature element in the p-adjacent domain through p-adjacent assignment mapping. p-adjacent assignment involves scaling the clinical feature vector values ​​to a rational number form. Then, the power difference between the prime numbers p in the numerator and denominator of this rational number is analyzed. This mapping process calculates the highest power to which the difference between any two eigenvalues ​​is divisible by a prime number p, thereby determining their distance under the p-progression value, which is defined based on the divisibility strength of the numerical difference. The non-Archimedean property of the p-progression value enables similar data to be automatically clustered into nested spheres, forming the initial hierarchy of a high-dimensional geometric representation.

[0020] A p-advanced Banach space is constructed based on elements in the p-advanced number field. This space is a non-Archimedean complete normed space. By redefining the induced inner product and the p-advanced norm, the topological relationships between feature elements are reconstructed, organizing all clinical feature elements in the p-advanced number field into a vector space with a complete topological structure. Its enhanced triangle inequality property connects discrete spheres into a continuous topological manifold, and its hypermetric property forces any three clinical states to necessarily form an isosceles triangle. This geometric constraint forces the data distribution to exhibit a tree-like branching pattern. Subsequently, the canonical representative element expansion technique is applied, utilizing the algebraic closure property of the p-advanced Banach space, to decompose each element in the space into an infinite series with a prime number p as its basis. The series coefficients are taken from the set of canonical representative elements that satisfy a specific algebraic equation. This expansion process repeatedly applies the same algebraic rules at different scale levels, making the local structure strictly isomorphic to the global topology. Through the synergistic effect of the three mechanisms of hierarchical nesting of p-progress quantities, tree-like fractal topology of p-entry Banach space, and scale invariance of canonical expansion, a high-dimensional geometric representation with self-similar fractal structure is formed. This representation not only preserves local clinical similarity but also amplifies global differences through non-Archimedean metrics, accurately capturing critical mutation points in health state transitions, and constructing a hierarchically organized mathematical space for subsequent topological invariant calculations.

[0021] S13. Based on non-Archimedean geometric structures, a healthy topological spectral index is generated by constructing spectral triples of non-commutative geometry and applying the Kon-Nechon eigenmap to calculate K-theory indices and Chern-Simons invariants.

[0022] Based on a high-dimensional clinical state space with self-similar fractal properties formed through reconstruction from a non-Archimedean geometric structure (i.e., a p-adjacent number field), a health topological spectrum index is generated. This index quantifies the overall topological stability of an individual's health system, identifies system-level critical transition points that are difficult to capture with traditional clinical indicators, and provides a mathematical foundation for predicting individual health trajectories and disease risks. The specific computation process first constructs a non-commutative geometric spectral triplet on this space, which is generated by clinical feature functions. The algorithm comprises algebra, a complete Hilbert space of p-advanced Banach spaces, and Dirac-type operators based on p-advanced derivatives. Subsequently, K-theory indices are calculated using the Kun-Nechchen eigenmap. This calculation quantifies the number of isolated connected regions in the clinical state space—that is, the number of independent stable states that the healthy system can simultaneously maintain—by ​​analyzing the number of equivalence classes of homotopy classes of the algebraic projection operators in the K-zero group. Simultaneously, Chern-Simons invariants are calculated. This process captures the topological entanglement characteristics of health state transition paths by integrating the instantaneous number density of the integral spectrum triples onto a four-dimensional submanifold.

[0023] Both calculations were implemented using discretization algorithms in clinical validation. The K-index was determined by calculating the eigenvalue distribution of the transition matrix between clinical state clusters, while the Simons invariant was approximated by the Monte Carlo method using path integrals on a sufficient number of clinical pathways until the calculation results reached a preset convergence accuracy threshold. The clinical pathway generation process first constructs a feature transition probability distribution from historical patient time-series data, then generates initial paths in a non-Archimedean geometric space using a geometrically stochastic process constrained by clinical knowledge rules, and finally determines the final path set through distribution similarity verification and adaptive sampling optimization, ensuring both mathematical topological properties and clinical physiological authenticity.

[0024] The final result is a health topology index scaled from zero to one hundred. Its value is equal to the weighted sum of the K-theory indices and the normalized Chern-Simons invariants. The weights are determined by logistic regression optimization using clinical outcome data, which refers to the final health outcomes of patients confirmed through long-term follow-up. This index directly corresponds to the number of stable states an individual can maintain within a curved health landscape. A curved health landscape maps human health status to a undulating terrain, where peaks represent stable health states and valleys represent disease states. The degree of curvature of the landscape determines how many different physiological configurations an individual can maintain stability without sliding into a pathological state. A health topology index greater than 80 indicates multi-steady-state health, where an individual's physiological system exists in multiple independent steady states, and each subsystem can autonomously maintain its homeostasis and compensate for each other, demonstrating high system resilience and adaptability. A score between 50 and 80 indicates a bistable transition, where physiological subsystems begin to lose their independent regulatory capacity and oscillate between the two dominant states of compensation and decompensation, with a reversible critical intervention window. A score below 30 indicates that the physiological system has collapsed into a single pathological homeostasis, where all subsystems are locked in a rigid synergistic mode, lose their ability to recover autonomously, and traditional interventions are insufficient to rebuild system diversity.

[0025] S20. Based on the health topology spectrum index and the tensor flow of continuous physiological monitoring, a causal structure collapse index is generated.

[0026] S21. By precisely aligning the health topological spectrum index with the high-frequency vital sign time-series data collected by the hospital monitoring system and wearable devices according to the timestamp, a spatiotemporal fusion data matrix is ​​constructed, providing a data foundation with both time resolution and topological stability for critical point dynamics analysis.

[0027] The system collects patients' dynamic physiological parameters in real time through a hospital central monitoring system and clinical-grade wearable devices, including multi-parameter vital signs monitors in the intensive care unit, remote ECG monitoring patches for outpatients, and subcutaneous implantable continuous glucose monitoring devices. These devices continuously record core vital signs such as heart rate variability, arterial blood pressure waveforms, blood glucose concentration, blood oxygen saturation, respiratory rate, and body temperature at sampling frequencies ranging from 1 to 1000 times per second. The sampling process adheres to the IEEE 11073 medical device communication standard to ensure data accuracy. The collected raw time-series data undergoes wavelet denoising and outlier correction, and is then precisely aligned with the health topology index according to timestamps to construct a spatiotemporal fusion data matrix. The matrix's row dimension represents continuous time points, and the column dimension contains two parts: the first few columns store dynamic physiological parameters, and the last column embeds the health topology index as a global constraint parameter for system stability. This matrix is ​​dynamically updated through a sliding window mechanism, with the window length adaptively adjusted according to clinical conditions. The resulting spatiotemporal fusion data matrix not only preserves the millisecond-level instantaneous changes in vital signs but also incorporates the quantitative description of the overall system stability using the health topology index.

[0028] S22. A discrete causal set dynamics framework is constructed based on a spatiotemporal fusion data matrix. This framework identifies critical points of health transition by constructing a directed acyclic graph and calculating the Benedict's action, and generates a continuous probability curve distribution map to provide early warning of the risk of an individual's health state transitioning to a pathological state.

[0029] A discrete causal set dynamics framework is constructed based on a spatiotemporal fusion data matrix. This framework helps to understand the dynamic behavior of health systems and identify potential critical points, i.e., the key moments when the system transitions from a normal state to a pathological state. The framework treats the clinical state at each time point in the spatiotemporal fusion data matrix as a discrete element in a causal set. A directed acyclic graph is constructed by defining the causal order relationships between elements, where nodes represent multidimensional physiological state vectors at specific times, derived from high-frequency vital sign time-series data. Edge weights are jointly determined by the Markov transition probabilities and the gradient of the health topological spectrum index. In this graph structure, the Benicassosadokker action is defined as the weighted sum of the volume variations across all causal intervals. Its calculation process first discretizes and samples the state transition paths within each time window. The state transition path is the trajectory of an individual health system evolving over time in a multidimensional physiological parameter space. Then, the partial derivatives of the Benicassosadokker action with respect to multiple physiological parameter state variables are approximated using the finite difference method to obtain the variational derivative field. The zero point of this derivative field in space corresponds to the saddle point of the healthy landscape, i.e., the critical threshold for the transition of health status. When the derivative sign changes from positive to negative, it indicates that the healthy system is about to cross the critical point and enter the pathological attraction domain. To improve clinical applicability, the variational derivative field calculation process adopts an adaptive grid refinement technique, automatically increasing the sampling density in regions where the health topological spectrum index changes rapidly, while applying the Tikhonov regularization method to suppress noise interference, and maintaining sparser sampling in relatively stable regions.

[0030] Based on the variational derivative field calculated using the discrete causal set framework, time regions with derivative values ​​close to zero are identified as candidate sets of critical points. A Bayesian uncertainty quantification method is introduced to consider measurement noise, the individualized fluctuation characteristics of numerical parameters in the construction of directed acyclic graphs and the calculation of Benedict's action, and individual physiological differences. The posterior probability of the authenticity of each candidate critical point is calculated, and this probability is estimated using the Markov chain Monte Carlo method. Finally, time series smoothing techniques are applied to connect discrete probability points into a continuous probability curve, and significant risk intervals are determined through adaptive threshold segmentation. A critical point probability distribution map is generated based on the continuous probability curve. This map uses time as the horizontal axis and the critical transition probability as the vertical axis, with peak regions accurately identifying high-risk windows for individuals experiencing health state phase transitions. An early warning is triggered when the distribution map shows a region with a probability peak exceeding 80% within 24-72 hours, prompting enhanced monitoring or preventative intervention within this time window, thereby blocking disease progression before the health system crosses the irreversible pathological threshold.

[0031] S23. Based on the critical points in the continuous probability curve distribution map, construct the Morse-Small complex, use adaptive fractional calculus to analyze the Hessian matrix spectrum distribution, and generate the causal structure collapse index. This index and the continuous probability curve distribution map form a complete early warning system that is complementary between long-term and short-term.

[0032] The critical points in the continuous probability curve distribution graph are used as stationary points of the Morse function, which is defined as a smooth interpolation of local maxima and minima in a healthy landscape. By calculating the zero-point stability of the gradient field at each critical point in the graph, the critical points are classified into healthy attractors, critical saddle points, and pathological repulsors. Subsequently, the cavity decomposition algorithm is applied to construct a network of the intersection of the stable manifold (the set of trajectories flowing into the critical points) and the unstable manifold (the set of trajectories flowing out of the critical points) based on the gradient flow trajectory connecting the critical points of different types, forming the Morse-Smail complex topology of the healthy system. Based on this complex, fractional calculus techniques are used to calculate the fractional spectral density of the Hessian matrix at each critical point. For the Hessian matrix spectral distribution, an adaptive fractional derivative of the proportion of eigenvalues ​​in the right half-plane is calculated. By accumulating only the positive value portion, integrating over time and dividing by the standardized time scale parameter, and applying an exponential decay function transformation process, a quantitative index in the range of zero to one hundred is generated as the causal structure collapse index. This index essentially quantifies the gradual disintegration rate of the causal relationship network. When the index continues to rise and exceeds 1.8 times the standard deviation of the individual baseline value, it indicates that the spatiotemporal structure of health has undergone irreversible rupture at the microscopic level. This rupture is manifested as the loss of phase synchronization between physiological oscillators and the delay in causal transmission. This index identifies early signs of the collapse of an individual's health system 8-10 weeks before the appearance of clinical symptoms, providing a critical time window for preventive intervention. Its early warning efficacy complements the critical point detection in step S22. The former focuses on long-term trend decay, while the latter captures short-term phase transition risks.

[0033] S30. Calculate the holographic critical dimension index by combining the causal structure collapse index with organ system function assessment data.

[0034] S31. Generate assessment data based on the core clinical indicators of each organ system; The organ systems mentioned include the cardiovascular, respiratory, renal, hepatic, nervous, and immune systems. For each system, 3-5 quantifiable core clinical indicators are selected, and after weighted fusion, the evaluation data (scores on a scalar scale of 1-100) for that system can be obtained. The core clinical indicators selected for each system are as follows: Cardiovascular system: Heart rate variability (time-domain index SDNN, 24-hour Holter monitoring, unit ms); Systolic blood pressure target achievement rate (percentage of daily average systolic blood pressure < 130 mmHg, unit %); Left ventricular ejection fraction (echocardiographic detection, unit %); Serum troponin I (laboratory detection, unit ng / mL, reference value < 0.04 ng / mL); Respiratory system: Forced expiratory volume in one second (FEV1, pulmonary function test, unit L); arterial oxygen saturation (SpO2, continuous monitoring, unit %); coefficient of variation of respiratory rate (24-hour monitoring, unit %); serum procalcitonin (PCT, laboratory test, unit ng / mL, reference value < 0.15ng / mL); Renal system: Estimated glomerular filtration rate (eGFR, calculated based on serum creatinine, age, and sex, unit: mL / min / 1.73m²); urine protein / creatinine ratio (random urine test, unit: mg / g, reference value: < 30 mg / g); blood urea nitrogen / creatinine ratio (laboratory test, no unit, reference value: 10-20). Liver system: Alanine aminotransferase (ALT, laboratory test, unit U / L, reference range 0-40 U / L); Aspartate aminotransferase / ALT ratio (AST / ALT, no unit, reference range 0.8-1.5); Serum albumin (ALB, laboratory test, unit g / L, reference range 35-55 g / L); Total bilirubin (TBIL, laboratory test, unit μmol / L, reference range 3.4-17.1 μmol / L); Nervous system: mean reaction time (neurobehavioral test, ms); cerebral blood flow velocity (transcranial Doppler ultrasound, middle cerebral artery, cm / s); serum neuron-specific enolase (NSE, laboratory test, ng / mL, reference value < 16.3 ng / mL); sleep structure percentage (deep sleep duration / total sleep duration, %). Immune system: Absolute lymphocyte count (laboratory test, unit × 10) 9 / L, reference value 1.1-3.2×10 9 / L); Immunoglobulin G (IgG, laboratory test, unit g / L, reference range 7.0-16.0 g / L); Neutrophil / Lymphocyte ratio (NLR, no unit, reference range 1.0-3.0).

[0035] Step S31: Calculate the holographic critical dimension index by combining the causal structure collapse index with organ system function assessment data; First, the information entropy of each system score is calculated using the entropy weight method to reflect the degree of data dispersion. The higher the dispersion, the greater the impact on the overall system. Then, the information entropy of each system score is used as the weight to perform weighted fusion of the system scores to obtain the comprehensive score of the organ system. Next, the comprehensive score is weighted and fused with the inverse positive collapse index to obtain the holographic critical dimension index. The fusion weight is the system preset value.

[0036] S40. Based on the holographic critical dimension index and historical clinical intervention data, generate the topological protection intervention index.

[0037] S41. Integrate the holographic critical dimension index time series with electronic prescriptions and treatment records from multiple institutions, and construct a dynamic intervention knowledge base with a graph database architecture through federated learning and causal inference algorithms to provide a decision-making basis for personalized treatment that accurately matches dimensional collapse paths.

[0038] By employing a continuous time-window sampling mechanism, high-resolution physiological monitoring data of patients over the past 7 days are continuously processed to generate a holographic critical dimension index corresponding to each time window, thereby constructing a complete index time series. This series includes the collapse risk score trajectory of each physiological dimension, the dynamic changes in the phase difference between dimensions, and the time-series transformation pattern of topological path types. The collapse risk score is the projection form of the holographic critical dimension index onto a single physiological dimension. The time-series transformation pattern of topological path types refers to the time-varying classification characteristics of the dynamic evolution trajectory of a healthy system during its transition to a pathological state, which are presented on the topological structure, including continuous gradual type, abrupt transition type, and oscillating critical type.

[0039] This study integrates holographic critical dimension index time series data with structured electronic prescription databases and treatment records from hospital information systems to construct a dynamically evolving multimodal intervention knowledge base. First, a federated learning framework is used to aggregate a large number of historical intervention cases across institutions while protecting patient privacy. Each case is precisely labeled with pre-intervention dimensional state characteristics, intervention measure codes, intervention time window parameters, and multi-dimensional efficacy indicators. Given the presence of numerous confounding factors in observational intervention data that can mislead intervention effect assessments, a causal inference algorithm is used to screen for statistically significant intervention-response relationships, eliminating the influence of confounding variables and identifying the most effective intervention combinations under specific dimensional feature patterns. Simultaneously, a clinical expert knowledge graph is introduced to semantically enhance the intervention combination data, embedding drug action mechanisms, treatment contraindications, and synergistic effect rules into the database structure. This intervention knowledge base adopts a graph database architecture, where nodes represent dimension-intervention pairs, and edge weights represent clinical effect confidence levels. It supports real-time queries for optimal intervention sequences under specific dimension collapse paths, providing a data-driven decision-making basis for personalized intervention generation.

[0040] S42. Based on the intervention knowledge base, a supersymmetric theoretical framework is constructed, and the intervention measures are modeled as instantaneous solutions with topological protection properties. The robustness of the intervention is achieved by topological load quantization and the critical dimension is dynamically focused by supersymmetric breaking mechanism, so as to realize the transformation of the precision medicine paradigm from traditional dose standardization to topological stability optimization.

[0041] Based on an intervention knowledge base, a supersymmetric theoretical framework is constructed, upgrading the design principle of intervention measures from empirical dosage adjustment to topological stability optimization, thus solving the problems of insufficient robustness, dimensionality defocus, and temporal mismatch in traditional interventions. This framework includes a theoretical foundation layer, a core modeling layer, a mathematical implementation layer, a stability assurance layer, and a dynamic adaptation layer. The core modeling layer defines intervention measures in the intervention knowledge base as instantaneous solutions in the system's potential energy landscape. An instantaneous solution is a non-perturbative field configuration with localization characteristics. This localized field configuration achieves system state transitions by traversing the energy barriers between healthy and pathological states, and its geometric characteristics are determined by the volumetric curvature structure. The system's potential energy landscape is an energy function composed of multidimensional physiological state variables, including valleys, depressions, and ridges, representing stable healthy attractors, metastable pathological attractors, and energy barriers for state transitions, respectively. The mathematical implementation layer employs path integrals to solve for instanton solutions on discretized manifolds, constructing a composite action that incorporates direct intervention effects, individual adaptability, and dimensional coupling stability. The stability assurance layer quantifies the perturbation resistance of instanton solutions through topological charge calculations. Instanton solutions with high topological charge values ​​possess an inherent topological protection mechanism, stemming from the nontrivial homotopy class of their field configuration. This mechanism ensures the topological stability of the intervention energy distribution, unaffected by local parameter perturbations. Even under significant individual metabolic differences or environmental pressure fluctuations, instanton solutions with high topological charge can maintain functional efficacy and phase synchronization in key dimensions. The dynamic adaptation layer integrates a supersymmetry breaking mechanism, adjusting the spatiotemporal localization characteristics of instanton solutions in real time based on the topological type of the dimensional collapse path, achieving precise focusing of intervention energy towards the critical dimensional region. The supersymmetric theoretical framework ensures a close coupling between the theoretical physics framework and clinical needs, directly translating abstract topological properties into measurable improvements in therapeutic efficacy.

[0042] S43. Under the framework of supersymmetric theory, the dimensional collapse path is mapped to a four-dimensional manifold. The Seiberg-Witten and Donaldson-Thomas invariants and their cross modes are calculated to identify topological singularities and energy barriers. The topological protection intervention index is generated, thereby optimizing the intervention sequence with minimum energy consumption and highest topological stability.

[0043] Based on the supersymmetric theoretical framework, the patient's current dimensional collapse path is mapped onto a four-dimensional smooth manifold. The process involves extracting the patient's real-time dimensional state vector from the supersymmetric framework. This vector contains collapse risk scores, inter-dimensional phase differences, and topological path types for four core dimensions: autonomic nervous regulation, inflammation and coagulation coupling, metabolism and excretion balance, and immunity and repair. The dimensional collapse path represents the dynamic trajectory of the four core physiological dimensions evolving over time. These four physiological dimensions are mapped to the coordinate axes of the four-dimensional manifold, with the first three dimensions corresponding to the spatial functional distribution of different organ systems, and the fourth dimension (time) characterizing the dynamic evolution of the collapse path. The inter-dimensional interaction strength and phase relationship are transformed into local geometric properties of the manifold, including the metric tensor, curvature field, and connection structure. This allows the coupling strength between physiological dimensions to directly correspond to the manifold's curvature, and phase synchronization to correspond to the manifold's topological smoothness, thus transforming abstract physiological relationships into precise geometric language. Finally, through differential homeomorphism transformation and comparison with topological invariants, the mapping process is rigorously verified to maintain the original physiological system's topological characteristics, ensuring that subsequent topological invariants calculated on the manifold truly reflect the essential characteristics of the physiological collapse path.

[0044] Subsequently, based on the spinor bundle structure defined on the four-dimensional manifold and high-dimensional time-series data of the micro-regulation layer, Seiberg-Witten invariants are calculated to quantify the stability of the intervention path at the micro-regulation level. Simultaneously, Donaldson-Thomas invariants are calculated based on the vector bundle mode space constructed on the same four-dimensional manifold and integrated data of the macro-organ function layer to characterize the topological preservation at the macro-organ function level. By analyzing the cross-modes of these two invariants, topological singularities in dimensional collapse paths are identified—critical transition points where even small perturbations can lead to global instability of the healthy system. Based on this, the topological Chern number of the intervention path is obtained by calculating the Chern-Simons action integral of the gauge field between the healthy and pathological states on the four-dimensional manifold. This integral quantifies the number of turns (i.e., the number of energy barriers) of the nontrivial homotopy group that the path connecting the two states must traverse; its integer value directly corresponds to the minimum number of topological transitions, forming a set of topological constraints.

[0045] Based on a set of topological constraints, an intervention energy functional is constructed. Its variables include the temporal distribution of interventions, dose gradients, and dimensional targeting accuracy. The objective function simultaneously minimizes total energy consumption and the rate of topological defects. During functional optimization, Morse theory is applied to screen critical points, retaining only intervention sequences that form stable gradient flows on the manifold, ensuring that any small perturbation will not cause the system to transition to a non-target steady state. Finally, by integrating the optimization results with the clinical safety boundary, a topological protection intervention index in the range of 0 to 100 is generated. This index not only assesses the stability of a single intervention but also optimizes the topological continuity of the entire intervention sequence, ensuring that the transition between adjacent interventions does not generate new topological defects. Guided by this index, intervention sequences with minimum energy consumption and highest topological stability are selected, thereby achieving a return to a healthy state with minimal physiological cost while maintaining the integrity of the overall topological structure, significantly improving the long-term stability and individual adaptability of the intervention.

[0046] S50 integrates the topological protection intervention index with multidimensional constraints of patients and environment, establishes a ternary system functor mapping of medical, behavioral, and environmental systems under a higher-order category theory framework, and calculates topological decision invariants.

[0047] S51. By integrating the topological protection intervention index with multidimensional sociodemographic data, and establishing a dynamic correlation between physiological dimension stability and external environmental factors through multimodal alignment, an enhanced decision dataset is generated to provide a comprehensive decision-making basis for generating individualized clinical intervention plans.

[0048] Structured and unstructured data were extracted from hospital information systems, including basic demographic characteristics, residential environment information, lifestyle spectrum, and socio-environmental factors such as patients' explicitly recorded treatment preferences. Multimodal data alignment techniques were used to establish a dynamic correlation between the physiological dimension stability represented by the topological protection intervention index and socio-demographic factors, environmental information, behavioral characteristics, and personal value preferences, identifying the influence patterns of external determinants on physiological breakdown pathways. Based on this, a hierarchical modeling method was employed to quantify the moderating effect of external factors on intervention stability, dynamically calibrating the topological protection intervention index to reflect individual social context. Simultaneously, a rigorous data quality control process was implemented, using privacy-preserving cross-institutional collaborative learning to complete missing data and verifying the data distribution balance across different population subgroups. The resulting enhanced decision dataset, while retaining the rigor of topological mathematics, deeply incorporates individualized life context information, distinguishing the differentiated intervention strategies required by patients in different social environments under the same physiological state, effectively bridging the gap between biomedical decision-making and social determinants, and laying a comprehensive data foundation for generating truly individualized clinical intervention plans.

[0049] S52. Based on the enhanced decision dataset, a higher-order category theory framework is constructed. Through the medical-behavior-environment ternary functor mapping and its natural transformation under the framework, a dynamic decision space is generated, which can accurately identify the minimum energy intervention sequence that can still maintain the overall stability of the system under social environmental disturbances.

[0050] The process of establishing a higher-order category theory framework based on the augmented decision dataset is as follows: Local Lyapunov indices in the potential landscape are calculated for each of the four core physiological dimensions from the augmented decision dataset. Stability scores for each of the four core physiological dimensions are then extracted using a clinical calibration function. An unsupervised clustering algorithm identifies three significantly different topological stability patterns: a physiologically healthy state is defined when all dimension stability scores are above a high threshold; a subclinical state is defined when one or two dimension stability scores are between the high and low thresholds while the remaining dimensions remain stable; and a pathological state is defined when three or more dimension stability scores are below the low threshold or when inter-dimensional phase synchronization collapse occurs. The high and low thresholds are determined through survival analysis using historical patient outcome data from the augmented decision dataset to ensure that the state classification has clinical prognostic significance. The morphic category is composed of intervention-response time-series pairs recorded in the dataset, with each morphic representing the trajectory of the dimensional stability score vector change caused by a specific intervention.

[0051] Within the framework of higher-order category theory, a ternary functor mapping of medical intervention, behavior, and environment is defined: the medical functor maps clinical interventions to changes in physiological stability; the behavior functor maps lifestyle adjustments to the reconstruction of micro-regulatory networks; and the environmental functor maps social support optimization to the recovery of macro-organ function. These three functors are interconnected through natural transformations, forming a commutative triangle that ensures topological consistency among medical interventions, behavioral adjustments, and environmental optimizations. By calculating the limits and colimits between the functors, synergistic enhancement points and conflict suppression points in the ternary system are identified, integrating previously separate clinical indicators, behavioral data, and environmental parameters onto a unified decision manifold. The geometric properties of this manifold, including curvature and connectivity, directly reflect the overall system compatibility of the intervention program.

[0052] This mapping framework projects the collaborative transformation trajectory of the medical-behavior-environment ternary system onto a four-dimensional dynamic decision manifold space by continuously parameterizing the functor limit space. This manifold is spanned by physiological stability, social adaptability, behavioral feasibility, and temporal evolution dimensions. Each point encodes a complete personalized intervention combination and its expected topological stability. The geodesic distance between points quantifies the system's energy consumption gradient, while the overall curvature and perforation structure of the manifold space capture the global robustness boundary of the intervention path. This manifold is dynamically updated by fusing patient physiological feedback data and environmental change parameters in real time, enabling clinical decision-makers to track the topological homotopy class of the optimal intervention path in a visual interface, automatically avoiding high-energy barrier regions and topological singularities, thereby accurately identifying the minimum energy intervention sequence that can maintain the overall stability of the system under social environmental disturbances.

[0053] S53. By calculating the homotopy group order of the decision manifold, the system stability constraints are determined. Combined with topological field theory, matrix planning is used to accurately intervene in the time series, generating topological decision invariants with quantified stability. The optimal transition path from pathological state to healthy state with minimum energy and global robustness is identified.

[0054] Based on a four-dimensional dynamic decision manifold space, this paper analyzes the connectivity properties of the space using algebraic topology methods, specifically calculating the orders of its first and second-order homotopy groups. The number of generators in the first-order homotopy group reveals the minimum number of independent intervention loops required to maintain system stability, while the order of the second-order homotopy group quantifies the strength of the global coordination constraints that must be satisfied between different intervention dimensions. Based on the calculated orders of the first and second-order homotopy groups, the braided matrix technique of topological field theory is introduced to determine the precise intervention time series. Three types of intervention operations—medical, behavioral, and environmental—are mapped to braided group generators. The intervention priority is determined by sorting the eigenvalues ​​of the braided matrix; the path with the largest eigenvalue corresponds to the most sensitive dimension transformation path and should be treated first. Simultaneously, the off-diagonal elements of the braided matrix provide precise time interval parameters between different intervention types. Subsequently, a well-defined topological invariant function is defined, using the order of the homotopy group as a stability constraint parameter and the eigenvalue spectrum of the braided matrix as a time series weight parameter, to jointly calculate a key topological decision invariant. This scalar value precisely quantifies the global topological stability of the intervention sequence within the interval of zero to one. Under this invariant constraint, the variational optimization algorithm searches for the path with the minimum energy consumption and identifies the optimal healthy phase transition path connecting the current pathological state and the target healthy state. This path is represented as a geodesic in the decision space, and the points where its curvature changes mark the key intervention nodes.

[0055] Ultimately, this computational process achieves a controlled structural transformation from pathological attraction domains to healthy attraction domains. Its mathematical guarantee lies in the homotopy group order ensuring that the path avoids all topological singularities, the weaving matrix guaranteeing temporal compatibility, and the topological decision invariants providing quantifiable stability proofs for clinical decision-making. This transforms complex multidimensional intervention sequences from personal empirical designs into precision medical engineering with rigorous mathematical guarantees.

[0056] Example 2 Embodiment 2 of this application provides a comprehensive analysis system for medical and health data, including: Topological Spectrum Index Module: Based on multidimensional clinical features extracted from medical and health data, this module calculates K-theory indicators and Chern-Simons invariants to generate a health topological spectrum index. It is specifically divided into the following sub-modules: Feature Matrix Submodule: Extracts and integrates electronic health records, laboratory test results, vital sign monitoring data, structured features of medical images, and patient-reported symptom scales into a multidimensional clinical feature set. After time alignment, missing value processing, and standardization, a high-dimensional temporal feature matrix is ​​constructed.

[0057] Fractal Structure Submodule: Based on a high-dimensional temporal feature matrix, it utilizes nested clustering of p-progression quantities, supermetric tree topology of Banach space, and scale invariance of canonical expansion to self-organize fractal geometric structures in non-Archimedean space, enabling critical mutation points of clinical state transitions to exhibit topologically consistent self-similarity characteristics at different scales.

[0058] The generation submodule is based on non-Archimedean geometry. It constructs spectral triples of non-commutative geometry and applies the Kon-Nechene eigenmap to calculate K-theory indices and Chern-Simons invariants. Based on the indices and invariants, it generates a healthy topological spectral index.

[0059] Collapse Index Module: Based on the health topology spectrum index and continuous physiological monitoring tensor flow, a causal structure collapse index is generated. It is specifically divided into the following sub-modules: Data Matrix Submodule: By precisely aligning the health topological spectrum index with high-frequency vital sign time-series data collected by hospital monitoring systems and wearable devices according to timestamps, a spatiotemporal fusion data matrix is ​​constructed, providing a data foundation with both temporal resolution and topological stability for critical point dynamics analysis.

[0060] The curve distribution plot submodule constructs a discrete causal set dynamics framework based on a spatiotemporal fusion data matrix. This framework identifies critical points for health transitions by constructing a directed acyclic graph and calculating the Benedict's action, generating a continuous probability curve distribution plot for early warning of the risk of an individual's health state transitioning to a pathological state.

[0061] Complementary submodule: Based on the critical point in the continuous probability curve distribution map, a Morse-Small complex is constructed. Adaptive fractional calculus is used to analyze the Hessian matrix spectrum distribution to generate a causal structure collapse index. This index and the continuous probability curve distribution map form a complete early warning system that is complementary between long-term and short-term.

[0062] Dimension Index Module: This module calculates the holographic critical dimension index by combining the causal structure collapse index with organ system scoring data. It is specifically divided into the following sub-modules: Organ system assessment submodule: Generates assessment data for each organ system based on its core clinical indicators.

[0063] The holographic critical dimension index calculation submodule combines causal structure collapse index and organ system function assessment data to calculate the holographic critical dimension index. Intervention Index Module: Based on the holographic critical dimension index and historical clinical intervention data, this module generates a topological protection intervention index. It is specifically divided into the following sub-modules: The knowledge base submodule integrates holographic critical dimensionality index time series with electronic prescriptions and treatment records from multiple institutions. Through federated learning and causal inference algorithms, a dynamic intervention knowledge base with a graph database architecture is constructed to provide a decision-making basis for accurately matching personalized treatments that address dimensionality collapse paths.

[0064] Supersymmetric Submodule: Based on the intervention knowledge base, a supersymmetric theoretical framework is constructed, and the intervention measures are modeled as instantaneous solutions with topological protection properties. The robustness of the intervention is achieved through topological charge quantization and the critical dimension is dynamically focused by utilizing the supersymmetry breaking mechanism, so as to realize the paradigm shift of precision medicine from traditional dose standardization to topological stability optimization.

[0065] Topological intervention submodule: Under the framework of supersymmetric theory, the dimensional collapse path is mapped to a four-dimensional manifold, the Seiberg-Witten and Donaldson-Thomas invariants and their cross modes are calculated to identify topological singularities and energy barriers, and the topological protection intervention index is generated, thereby optimizing the intervention sequence with minimum energy consumption and highest topological stability.

[0066] Topology Decision Module: This module integrates the topology protection intervention index with multidimensional constraints from the patient and environment, establishes a functor mapping of the medical, behavioral, and environmental ternary system within a higher-order category theory framework, and calculates topology decision invariants. Specifically, it is divided into the following sub-modules: The decision dataset submodule integrates the topological protection intervention index with multidimensional sociodemographic data. Through multimodal alignment, it establishes a dynamic correlation between physiological dimension stability and external environmental factors, generating an enhanced decision dataset that provides a comprehensive decision-making basis for generating individualized clinical intervention plans.

[0067] The decision space submodule constructs a higher-order category theory framework based on the enhanced decision dataset. Through the medical-behavior-environment ternary functor mapping and its natural transformation under the framework, a dynamic decision space is generated, which accurately identifies the minimum energy intervention sequence that can still maintain the overall stability of the system under social environmental disturbances.

[0068] Stable Decision Submodule: Determines system stability constraints by calculating the homotopy group order of the decision manifold, combines topological field theory to weave matrix planning for precise intervention timing, generates topological decision invariants with quantifiable stability, and identifies the optimal transition path from pathological state to healthy state with minimum energy and global robustness.

[0069] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. A comprehensive analysis method for medical and health data, characterized in that, include: S10. Based on the multidimensional clinical features extracted from medical and health data, calculate the K-theory index and the Chern-Simons invariant to generate the health topology spectrum index. S20. Based on the health topology spectrum index and the tensor flow of continuous physiological monitoring, a causal structure collapse index is generated. S30. Calculate the holographic critical dimension index by combining the causal structure collapse index and organ system score data; S40. Generate a topological protection intervention index based on the holographic critical dimension index and historical clinical intervention data; S50 integrates the topological protection intervention index with multidimensional constraints of patients and environment, establishes a ternary system functor mapping of medical, behavioral, and environmental systems under a higher-order category theory framework, and calculates topological decision invariants.

2. The comprehensive analysis method for medical and health data as described in claim 1, characterized in that, Based on the multidimensional clinical features extracted from medical and health data, the K-theory index and the Chern-Simons invariant are calculated to generate the health topology spectrum index, which is specifically divided into the following sub-steps: Electronic health records, laboratory test results, vital sign monitoring data, structured features of medical images, and patient-reported symptom scales were extracted and integrated into a multidimensional clinical feature set. After time alignment, missing value processing, and standardization, a high-dimensional temporal feature matrix was constructed. Based on the high-dimensional temporal feature matrix, nested clustering of p-progression, supermetric tree topology of Banach space and scale invariance of canonical expansion are used to self-organize fractal geometric structures in non-Archimedean space, so that the critical mutation points of clinical state transitions exhibit topologically consistent self-similar features at different scales. Based on non-Archimedean geometry, a healthy topological spectral index is generated by constructing spectral triples of non-commutative geometry and applying the Kon-Nechchen eigenmap to calculate K-theory indices and Chern-Simons invariants.

3. The comprehensive analysis method for medical and health data as described in claim 1, characterized in that, Based on the health topology spectrum index and the tensor flow of continuous physiological monitoring, a causal structure collapse index is generated, which is specifically divided into the following sub-steps: By precisely aligning the health topology spectrum index with high-frequency vital sign time-series data collected by hospital monitoring systems and wearable devices according to timestamps, a spatiotemporal fusion data matrix is ​​constructed, providing a data foundation with both temporal resolution and topological stability for critical point dynamics analysis; A discrete causal set dynamics framework is constructed based on a spatiotemporal fusion data matrix. This framework identifies critical points of health transition by constructing a directed acyclic graph and calculating the Benedict's Sadocer action, and generates a continuous probability curve distribution map to provide early warning of the risk of an individual's health state transitioning to a pathological state. Based on the critical points in the continuous probability curve distribution map, a Morse-Small complex is constructed. The distribution of the Hessian matrix spectrum is analyzed using adaptive fractional calculus, and a causal structure collapse index is generated. This index, together with the continuous probability curve distribution map, forms a complete early warning system that complements the long-term and short-term perspectives.

4. The comprehensive analysis method for medical and health data as described in claim 3, characterized in that, Based on the critical points in the continuous probability curve distribution map, a Morse-Small complex is constructed. Adaptive fractional calculus is used to analyze the Hessian matrix spectral distribution, generating a causal structure collapse index. This index, together with the continuous probability curve distribution map, forms a complete early warning system that complements long-term and short-term predictions. The specific steps are as follows: By taking the critical points in the continuous probability curve distribution graph as the stationary points of the Morse function, and calculating the zero-point stability of the gradient field at each critical point in the graph, the critical points are classified into health attractors, critical saddle points, and pathological repulsion points. By applying the cavity decomposition algorithm, based on the gradient flow trajectory connecting critical points of different types, a network of intersection between the stable manifold (i.e., the set of trajectories flowing into critical points) and the unstable manifold (i.e., the set of trajectories flowing out of critical points) is constructed, forming the Morse-Small complex topology of the healthy system. Based on this complex, fractional spectral density of the Hessian matrix at each critical point is calculated using fractional calculus techniques, and adaptive fractional derivatives of the proportion of eigenvalues ​​in the right half-plane of the spectrum are calculated. By accumulating only the positive parts, integrating over time and dividing by the standardized time scale parameter, and applying an exponential decay function transformation process, the causal structure collapse index is generated.

5. The comprehensive analysis method for medical and health data as described in claim 1, characterized in that, Combining the causal structure collapse index and organ system scoring data, the holographic critical dimension index is calculated, which is specifically divided into the following sub-steps: Based on the core clinical indicators of each organ system, generate its assessment data; By combining the causal structure collapse index with organ system function assessment data, a holographic critical dimension index is calculated.

6. The comprehensive analysis method for medical and health data as described in claim 1, characterized in that, Based on the holographic critical dimension index and historical clinical intervention data, a topological protection intervention index is generated, which is specifically divided into the following sub-steps: By integrating holographic critical dimension index time series with electronic prescriptions and treatment records from multiple institutions, and constructing a dynamic intervention knowledge base with a graph database architecture through federated learning and causal inference algorithms, a decision-making basis is provided for personalized treatment that accurately matches dimensional collapse paths. Based on the intervention knowledge base, a supersymmetric theoretical framework is constructed, and the intervention measures are modeled as instantaneous solutions with topological protection properties. The robustness of the intervention is achieved by topological charge quantization and the critical dimension is dynamically focused by supersymmetry breaking mechanism, so as to realize the paradigm shift of precision medicine from traditional dose standardization to topological stability optimization. Within the framework of supersymmetric theory, dimensional collapse paths are mapped to four-dimensional manifolds. Seiberg-Witten and Donaldson-Thomas invariants and their cross-modulo variables are calculated to identify topological singularities and energy barriers, generating topological protection intervention indices, thereby optimizing intervention sequences with minimum energy consumption and highest topological stability.

7. The comprehensive analysis method for medical and health data as described in claim 1, characterized in that, By integrating the topological protection intervention index with multidimensional constraints of patients and the environment, a functor mapping of the medical, behavioral, and environmental ternary system under a higher-order category theory framework is established, and topological decision invariants are calculated. This process is divided into the following sub-steps: By integrating the topological protection intervention index with multidimensional sociodemographic data, and establishing a dynamic correlation between physiological dimensional stability and external environmental factors through multimodal alignment, an enhanced decision-making dataset is generated to provide a comprehensive decision-making basis for generating individualized clinical intervention plans. Based on an enhanced decision dataset, a higher-order category theory framework is constructed. Through the medical-behavior-environment ternary functor mapping and its natural transformation under the framework, a dynamic decision space is generated, which can accurately identify the minimum energy intervention sequence that can still maintain the overall stability of the system under social environmental disturbances. By calculating the homotopy group order of the decision manifold to determine the system stability constraints, and combining topological field theory to weave matrix planning for precise intervention timing, topological decision invariants with quantifiable stability are generated, and the optimal transition path from pathological state to healthy state with minimum energy and global robustness is identified.

8. A comprehensive analysis system for medical and health data, characterized in that, include: Topology Spectrum Index Module: Based on multidimensional clinical features extracted from medical and health data, calculate K-theory indicators and Chern-Simons invariants to generate a health topology spectrum index; Collapse Index Module: Based on the health topology spectrum index and continuous physiological monitoring tensor flow, a causal structure collapse index is generated; Dimension Index Module: Combines causal structure collapse index and organ system score data to calculate holographic critical dimension index; Intervention Index Module: Generates a topological protection intervention index based on the holographic critical dimension index and historical clinical intervention data; Topology Decision Module: Integrates the topology protection intervention index with multidimensional constraints of patients and environment, establishes a functor mapping of the medical, behavioral and environmental ternary system under the framework of higher-order category theory, and calculates topology decision invariants.

Images