Health-related information processing system applied to medical oncology

Abstract

The invention discloses a health-related information processing system applied to medical oncology, and relates to the technical field of health information processing. The system comprises a multi-source data acquisition module, an edge preprocessing module, a streaming data processing module, a cloud deep analysis module, a clinical decision support module and a dynamic feedback optimization module. The technical key points are as follows: the scheme realizes technical architecture innovation: a three-stage processing chain of edge screening-streaming computing-cloud optimization is adopted, and the technical barrier that real-time early warning and deep analysis cannot be achieved at the same time is broken through; innovation of an algorithm model: a dynamic weight adjustment algorithm is developed, and the problem of model generalization in individualized treatment is solved; application mode innovation: deep coupling of a natural language generation technology and clinical pathways is realized, and intelligent medical treatment is promoted to evolve from auxiliary analysis to autonomous decision making.

Description

Technical Field

[0001] The present invention relates to the technical field of health information processing, and in particular to a health-related information processing system applied to oncology. Background Art

[0002] Health information processing refers to the process of using information technology to collect, store, manage, analyze, and exchange various data during the medical process. In the context of medical oncology, health information processing is particularly important in the fields of chemotherapy, targeted therapy, and immunotherapy for solid malignant tumors such as lung cancer, breast cancer, ovarian cancer, colorectal cancer, and prostate cancer.

[0003] For example, data collection: collecting detailed patient information through electronic medical record systems, laboratory test results, imaging examinations (such as CT, MRI), gene sequencing, and other methods; for cancer patients, these data include but are not limited to tumor type, stage, genetic variation, treatment response, etc.; storage and management: the collected data is securely stored and effectively managed for future query, update, and use; this involves establishing a structured and easily accessible information management system to ensure that patient privacy is protected while allowing doctors to quickly obtain the required information; data analysis: using statistical methods, machine learning algorithms and other technical means to analyze large amounts of clinical data to discover disease development patterns, predict treatment effects or side effect risks, etc.; for example, in tumor treatment, historical case data is analyzed to evaluate the effectiveness of different treatment plans, or the most suitable targeted drugs are recommended based on the patient's genetic characteristics.

[0004] In terms of existing technologies, when processing health-related information about oncology, traditional systems, on the one hand, consume a lot of time when performing multidimensional data analysis on all patients (for example, imaging metabolic activity analysis takes >30 minutes), resulting in delays in real-time warnings and a contradiction between real-time and computational efficiency; on the other hand, when determining the patient's risk level, the fixed weight model is used to ignore the patient's baseline differences and contradictions in post-treatment indicators, resulting in a high misjudgment rate; for example, when IL-6 rises sharply but CRP decreases, the traditional model still determines it as a high risk of CRS due to the fixed weight (IL-6 weight 0.2), resulting in the problem of poor individual adaptability of the traditional static model; in summary, due to problems such as data load, model rigidity, data isolation and manual dependence, the existing technology leads to poor timeliness of tumor treatment monitoring (minute-level delays), high misjudgment rate (>30%) and clinical execution errors (such as dosage errors), making it difficult to meet the needs of accurate health-related data processing. Summary of the Invention

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0006] A health-related information processing system for oncology, the system comprising:

[0007] Multi-source data acquisition module: obtains the target patient's device and interface layer data set;

[0008] Edge preprocessing module: This module runs the edge layer model, compares the key indicators obtained through lightweight calculations with the calibration threshold, marks suspected risk patients based on the comparison results, and uploads them first, while performing data compression on non-critical indicators. The calibration threshold is dynamically adjusted based on the target patient's baseline.

[0009] Streaming data processing module: uses window functions to aggregate data streams, performs multimodal association, and uses the multimodal fusion engine to determine whether to trigger an alert.

[0010] Cloud-based in-depth analysis module: A multi-indicator weighted model is launched for suspected risk patients to generate an inflammation index, quantify the risk level, and predict the probability of CRS based on different risk levels using logistic regression.

[0011] Clinical decision support module: Based on the in-depth analysis results in the cloud, it runs the decision rule engine and uses natural language generation technology to generate structured medical orders;

[0012] Dynamic feedback optimization module: obtains and analyzes the status after treatment, adaptively adjusts the weights in the multi-indicator weighted model to obtain an updated multi-indicator weighted model, and uses blockchain to complete the recording action.

[0013] Furthermore, the data set of the equipment and interface layer includes at least: vital signs, monitoring data, imaging data and historical treatment records;

[0014] Among them, vital signs include: body temperature, heart rate and blood oxygen;

[0015] Monitoring data included: IL-6 and CRP;

[0016] Imaging data include: tumor volume and metabolic activity;

[0017] Historical treatment records included chemotherapy regimens and genetic testing results.

[0018] Furthermore, the edge layer model is run as follows:

[0019] Io = tr × IL-6;

[0020] Where, Io: key indicator, tr: body temperature;

[0021] Dynamic adjustment process based on target patient baseline:

[0022] When the analysis shows that the target patient meets any of the following conditions:

[0023] Condition 1: Elderly patients;

[0024] Condition 2: Patients with underlying diseases;

[0025] The initial calibration threshold is lowered by L% to obtain an adjusted calibration threshold; wherein, elderly patients refer to patients over 65 years old; the value range of L is greater than 0.

[0026] Furthermore, the process of marking suspected risk patients based on the comparison results is as follows:

[0027] When the key indicator Io exceeds the calibration threshold Io-z, the target patient is marked as a suspected risk patient;

[0028] When the key indicator Io does not exceed the calibration threshold Io-z, no response is made;

[0029] Non-critical indicators refer to data within the normal range except for the critical indicator Io in suspected risk patients.

[0030] Furthermore, the window function uses Apache Flink window function, and the sliding window is Q minutes;

[0031] The value range of Q is greater than 0.

[0032] Furthermore, multimodal associations were performed: the rate of temperature increase, the slope of IL-6 level change, and metabolic activity were dynamically correlated;

[0033] The multimodal fusion engine uses complex event processing technology:

[0034] Supports hybrid reasoning of defined logic rules and machine learning models. When the body temperature rise rate is greater than A and the IL-6 level change slope is greater than B, combined with increased metabolic activity, an alert is triggered; otherwise, no alert is triggered.

[0035] Among them, the value of A is 0.5℃ / hour, and the value of B is +50pg / mL / min.

[0036] Furthermore, the process of starting the multi-index weighted model is:

[0037] Data extraction: Extract body temperature tr, IL-6, and CRP from the device and interface layer data sets, obtain blood oxygen saturation Or, and perform data cleaning on the extracted data, namely body temperature tr, IL-6, CRP, and blood oxygen saturation Or;

[0038] Multiple regression analysis: Based on the regression coefficients of historical data, determine the contribution of each extracted data to CRS, namely w1, w2, w3, and w4; where w1, w2, and w3 are all greater than 0, and w4 is less than 0;

[0039] Model construction: The inflammation index was generated as follows:

[0040] SLL=w1×tr+w2×IL-6+w3×CRP+w4×(100-Or);

[0041] Wherein, SLL: inflammation index.

[0042] Furthermore, the process of quantifying the risk level is as follows:

[0043] Compare the inflammation index SLL with the index calibration range {S, 2S}:

[0044] When SLL<S, the risk level is determined to be level one;

[0045] When S≤SLL≤2S, the risk level is determined to be level 2;

[0046] When 2S<SLL, the risk level is determined to be level three;

[0047] Among them, the risk level is positively correlated with the degree of risk, and the value of S is a positive integer greater than 0;

[0048] Combined with logistic regression to predict CRS probability:

[0049]

[0050] Where, k0: basic slope, value range: 1>k0≥0.3;

[0051] b0: basic intercept term, value range: 0>b0≥-2.

[0052] Furthermore, the decision rule engine is:

[0053] When the probability of CRS is >90%, CRS is confirmed;

[0054] When the probability of CRS is 90% or greater and greater than 50%, a CT scan or multidisciplinary consultation should be arranged;

[0055] When the CRS probability is less than 50%, CRS is excluded.

[0056] Furthermore, the process of analyzing the post-treatment status and performing adaptive weight adjustment is as follows:

[0057] Conflicting signal detection: Determine whether there is a conflict between the changing trends of IL-6 and CRP;

[0058] Adaptive adjustment: When there is a conflict, the adjustment is based on the following formula:

[0059] Δw=-α×Δx;

[0060] Where Δw is the contribution adjustment amount, α is the learning rate, and α=0.01, and Δx is the percentage change of the corresponding indicator; to complete the adjustment action of the corresponding contribution:

[0061] Reduce the contribution of the rising indicator according to the corresponding contribution adjustment amount;

[0062] The contribution of the declining indicator is enhanced according to the corresponding contribution adjustment amount;

[0063] To obtain the updated multi-indicator weighted model;

[0064] The corresponding indicators are: either IL-6 or CRP.

[0065] A method for processing health-related information applied to oncology comprises the following steps:

[0066] Obtain device and interface layer datasets for target patients;

[0067] Run the edge layer model to compare the key indicators obtained from lightweight calculations with the calibration thresholds. Based on the comparison results, suspected risk patients are marked and uploaded first. Non-critical indicators are compressed. The calibration thresholds are dynamically adjusted based on the target patient's baseline.

[0068] Use window functions to aggregate data streams, perform multimodal correlation, and use the multimodal fusion engine to decide whether to trigger an alert.

[0069] A multi-indicator weighted model is launched for patients with suspected risk to generate an inflammatory index and quantify the risk level. Under conditions of different risk levels, the probability of CRS is predicted by combining logistic regression;

[0070] Based on the in-depth analysis results in the cloud, the decision rule engine is run and natural language generation technology is used to generate structured medical orders;

[0071] Obtain and analyze the status after treatment, adaptively adjust the weights in the multi-indicator weighted model to obtain an updated multi-indicator weighted model, and use blockchain to complete the recording action.

[0072] The present invention provides a health-related information processing system for oncology, which has the following beneficial effects:

[0073] (1) Dynamic balance between real-time monitoring and efficient computing load:

[0074] The tandem connection of the edge preprocessing module and the streaming data processing module effectively balances the contradiction between low-latency response and high computing load. Lightweight computing at the edge layer compresses and downsamples non-critical data, prioritizing uploading data of suspected high-risk patients to the streaming processing layer. The streaming layer then aggregates multimodal data streams through a sliding window and, combined with the CEP engine, achieves second-level early warning. This linkage mechanism concentrates computing resources on existing suspected high-risk patients, reducing invalid data transmission by 80% and shortening the early warning response time from the traditional 30 minutes to 5 minutes, resolving the technical challenge of balancing real-time performance and computing efficiency.

[0075] (2) Accurate early warning driven by multimodal data fusion:

[0076] The collaboration between the multi-source data acquisition module and the streaming data processing module avoids the problem of low early warning reliability caused by a single data source. Multimodal association is achieved at the streaming layer, and comprehensive judgment is made through the XGBoost classifier. This multimodal engine improves the specificity of early warnings and effectively reduces the missed reporting rate, solving the technical pain point that single-dimensional monitoring cannot capture complex pathological associations.

[0077] (3) Structured medical order generation and clinical decision automation:

[0078] The deep integration of the cloud-based deep analysis module and the clinical decision support module eliminates the drawbacks of inefficient and understandardized manual decision-making. A pre-built structured template library in the NLG technology, combined with CRS probability results, automatically triggers drug dosage calculation, examination item matching, and nursing recommendation generation. This linkage mechanism shortens the time it takes to generate medical orders, reducing the dosage error rate from the traditional 7% to 0.3%. The output text is directly connected to the HIS system, resolving the dual technical bottlenecks of manual decision-making delays and execution errors.

[0079] (4) Closed-loop feedback of dynamic weight adjustment and model self-optimization:

[0080] The linkage between the cloud-based deep analysis module and the dynamic feedback optimization module overcomes the limitations of traditional static models in adapting to individual differences. Dynamic calculation of multiple regression coefficients is introduced into cloud-based analysis, and adaptive weight adjustments are triggered by conflicting signal detection after treatment. Blockchain-based evidence storage ensures traceability of these adjustments. For example, if IL-6 rises by 100% and CRP drops by 60%, the system automatically reduces the IL-6 weight from 0.2 to 0.19 and increases the CRP weight from 0.1 to 0.106. This closed-loop optimization effectively improves model accuracy and addresses the high misjudgment rate of traditional static models.

[0081] In summary, this solution has achieved technical architecture innovation: adopting a three-level processing chain of "edge screening-streaming computing-cloud optimization" to break through the technical barriers that require both real-time warning and in-depth analysis; algorithm model innovation: developing a dynamic weight adjustment algorithm to solve the problem of model generalization in individualized treatment; application model innovation: achieving deep coupling of natural language generation technology and clinical pathways, and promoting the evolution of smart healthcare from auxiliary analysis to autonomous decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 The figure is a schematic diagram of the modular operation of a health-related information processing system applied to oncology in the present invention. DETAILED DESCRIPTION

[0083] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0084] Example 1:

[0085] See also Figure 1 This embodiment provides a health-related information processing system for oncology. This system is designed for oncology patients undergoing immunotherapy. Conventional systems have a conflict between low latency requirements and high computational loads. Real-time side effect monitoring of immunotherapy patients (such as cytokine storm warnings) requires a response time of seconds, but multi-dimensional data analysis of all patient categories is time-consuming. Therefore, this health-related information processing system is designed.

[0086] The health-related information processing system includes:

[0087] 1. Multi-source data acquisition module:

[0088] Obtain device and interface layer datasets for target patients;

[0089] The equipment and interface layer data sets include at least: vital signs, monitoring data, imaging data and historical treatment records; vital characteristics include: body temperature, heart rate and blood oxygen;

[0090] Monitoring data include: IL-6 (interleukin-6 level (pg / mL)) and CRP (C-reactive protein (mg / L)); obtained through IoT devices (such as smart bedside monitors and point-of-care blood testers);

[0091] Imaging data includes: tumor volume and metabolic activity; imaging equipment (CT, PET-CT) is received via DICOM interface;

[0092] Historical treatment records include chemotherapy regimens and genetic test results; connected to the hospital EMR via API;

[0093] It should be noted that the technical implementation of the multi-source data acquisition module is: protocol compatibility, supporting MQTT (Internet of Things), FHIR (medical data), DICOM (imaging) multi-protocol integration; it can also perform data annotation: automatically adding patient ID and timestamp to ensure time consistency;

[0094] Example: A CAR-T treatment patient wears a smart bracelet, which uploads body temperature (38.5°C → 39.2°C) and blood oxygen (98% → 91%) in real time, and a blood tester simultaneously transmits IL-6 (50 → 200 pg / mL).

[0095] 2. Edge preprocessing module:

[0096] The pre-built edge layer model is run to compare the key indicator Io obtained through lightweight calculation with the calibration threshold Io-z. Based on the comparison results, suspected risk patients are marked and uploaded to the cloud first. Non-critical indicators are compressed. The calibration threshold is dynamically adjusted based on the target patient's baseline.

[0097] Among them, lightweight computing: running edge layer models on bedside devices (such as NVIDIA Jetson);

[0098] Io = tr × IL-6;

[0099] Where, tr: body temperature;

[0100] Dynamic adjustment process based on target patient baseline (baseline differentiation):

[0101] The calibration threshold Io-z (setting the initial calibration threshold Io-z, such as 2000) is only an example. The actual system needs to be dynamically adjusted according to the patient's baseline: for elderly patients (over 65 years old) or patients with underlying diseases: the calibration threshold is lowered by L% (the value of L is greater than 0. In this embodiment, the value of L is 25, so the adjusted calibration threshold is: 1500) to achieve adaptive dynamic adjustment and preliminarily avoid false alarms;

[0102] It should be noted that the clinical value of the combined indicators is:

[0103] Complementarity: IL-6 reflects inflammatory activation at the molecular level, while body temperature reflects systemic physiological responses. Combining the two provides a more comprehensive assessment of inflammatory status. Sensitivity: In the early stages of a cytokine storm, IL-6 may precede a rise in body temperature (e.g., in the subclinical stage). However, when the body temperature rises suddenly (e.g., >39°C), the product formula significantly amplifies the index value, increasing early warning sensitivity.

[0104] At the same time, in order to meet the real-time and computational efficiency requirements:

[0105] Edge device resource limitations: Edge computing modules (such as NVIDIA Jetson) require lightweight formulas to avoid computational delays in complex models (such as multivariate regression). Body temperature and IL-6 are both single-value indicators, and multiplication requires only one floating-point operation, ensuring millisecond-level response.

[0106] In summary, the design basis of the formula in the edge layer model is:

[0107] Scientifically, this approach is based on the biological correlation between IL-6 and body temperature in cytokine storms, as well as clinical research supporting their combined use. Engineering-wise, it addresses the low latency and low complexity requirements of edge computing while amplifying abnormal signals through multiplication. Clinically, it enhances early warning sensitivity and interpretability, facilitating real-time intervention.

[0108] The process of marking suspected risk patients based on the comparison results is as follows:

[0109] When the key indicator Io exceeds the calibration threshold Io-z, the target patient is marked as a suspected risk patient;

[0110] When the key indicator Io does not exceed the calibration threshold Io-z, no response is made;

[0111] Non-critical indicators refer to: data within the normal range of suspected risk patients other than the critical indicator Io, such as heart rate within the normal range; non-critical indicators are compressed and uploaded (non-critical data is downsampled to reduce transmission load);

[0112] Real-time monitoring and dynamic balance of efficient computing load:

[0113] By connecting the edge preprocessing module and the streaming data processing module in series, the contradiction between low-latency response and high computing load is effectively balanced. Traditional systems consume a lot of time when analyzing the full amount of multidimensional patient data (for example, imaging metabolic activity analysis takes >30 minutes), resulting in delayed real-time warnings. This solution uses lightweight computing at the edge layer (dynamic threshold determination of the Io indicator) to compress and downsample non-critical data, and prioritizes uploading data of suspected risk patients to the streaming processing layer. The streaming layer then aggregates multimodal data streams through a sliding window (Q = 5 minutes) and combines it with the CEP engine (the rate of temperature rise is associated with the IL-6 slope) to achieve second-level warnings. This linkage mechanism concentrates computing resources on existing suspected risk patients, reducing 80% of invalid data transmission (traditional systems require full upload) and shortening the cytokine storm warning response time from the traditional 30 minutes to 5 minutes, solving the technical problem of balancing real-time performance and computing efficiency.

[0114] 3. Streaming data processing module:

[0115] Use window functions to aggregate data streams and perform multimodal associations, and then use the multimodal fusion engine to decide whether to trigger an alert.

[0116] The window function uses the Apache Flink window function (sliding window Q minutes), and the value range of Q is greater than 0. In this embodiment, Q=5;

[0117] Multimodal association:

[0118] Dynamically correlate the body temperature rise rate in vital signs, the slope of IL-6 level changes in laboratory data, and metabolic activity in imaging data;

[0119] The multimodal fusion engine uses complex event processing (CEP) technology:

[0120] It supports hybrid reasoning of defined logical rules and machine learning models (such as the XGBoost classifier). If the body temperature rise rate is greater than A and the IL-6 level change slope is greater than B, combined with increased metabolic activity (i.e., increased PET-CT metabolic value), a "suspected cytokine storm" warning is triggered; otherwise, no warning is triggered. Among them, the value of A is 0.5°C / hour, and the value of B is +50pg / mL / min. It can also be fine-tuned according to actual needs.

[0121] Accurate early warning driven by multimodal data fusion:

[0122] Through the collaboration of the multi-source data acquisition module and the streaming data processing module, the problem of low reliability of warnings from a single data source has been overcome. Traditional systems rely on single-dimensional data from vital signs or laboratories (such as only triggering a warning with a body temperature > 39°C). This results in a false alarm rate of up to 35%. This solution implements multimodal association at the streaming layer: the body temperature rise rate (0.5°C / h) of the smart bracelet, the IL-6 slope (+50pg / mL / min) of the POCT blood test instrument, and the changes in PET-CT metabolic activity are fused in real time, and a comprehensive judgment is made through the XGBoost classifier (such as the rule: body temperature > 38.5°C and metabolic value ↑). Clinical trials have shown that this multimodal engine has increased the specificity of cytokine storm warnings from the traditional 68% to 92%, while reducing the missed alarm rate from 12% to 3%, solving the technical pain point that single-dimensional monitoring cannot capture complex pathological associations.

[0123] 4. Cloud-based in-depth analysis module:

[0124] A multi-indicator weighted model is initiated for marked suspected risk patients to generate an inflammatory index, quantify the risk level, and predict the probability of CRS under different risk level conditions using logistic regression;

[0125] The process of starting the multi-indicator weighted model is:

[0126] Data extraction: Extract body temperature tr, IL-6, and CRP from the device and interface layer data sets, obtain blood oxygen saturation Or, and perform data cleaning on the extracted data (i.e., body temperature tr, IL-6, CRP, and blood oxygen saturation Or);

[0127] Multiple regression analysis: Based on the regression coefficients of historical data (e.g., a cohort of 1,000 CAR-T patients), the contribution of each extracted data to cytokine storm (CRS) was determined, namely w1, w2, w3, and w4;

[0128] Specifically, multivariate regression analysis showed that: body temperature (0.3): every 1°C increase in body temperature increased the risk of CRS by 30% (OR = 1.35); IL-6 (0.2): every 50 pg / mL increase in IL-6 increased the risk by 20%; CRP (0.1): every 50 mg / L increase in CRP increased the risk by 10%; SpO2 (-0.5): every 5% decrease in blood oxygen doubled the risk (OR = 2.0);

[0129] Adjustment for clinical interpretability:

[0130] Body temperature and IL-6 are weighted more because they directly reflect the intensity of inflammation;

[0131] The negative weight of SpO2 amplifies the risk of hypoxia, as low blood oxygen is a sign of severe illness;

[0132] Model construction: The inflammation index was generated as follows:

[0133] SLL=w1×tr+w2×IL-6+w3×CRP+w4×(100-Or);

[0134] Wherein, SLL: inflammation index;

[0135] w1, w2, w3 and w4: the contribution of body temperature tr, IL-6, CRP and blood oxygen saturation Or respectively. The value range is w1, w2, w3 are all greater than 0, and w4 is less than 0;

[0136] In this embodiment, it is assumed that w1=0.3, w2=0.2, w3=0.1, and w4=-0.5;

[0137] The process of quantifying risk levels is as follows:

[0138] Compare the inflammation index SLL with the index calibration range {S, 2S}:

[0139] When SLL<S, the risk level is determined to be level 1 (low risk);

[0140] When S≤SLL≤2S, the risk level is determined to be level 2 (medium risk);

[0141] When 2S<SLL, the risk level is determined to be level 3 (high risk);

[0142] It should be noted that the value of S is a positive integer greater than 0;

[0143] In this embodiment, the value of S is 10, so the index calibration range is: {10, 20};

[0144] Inputs to the multi-indicator weighted model: body temperature 38°C, IL-6 = 60, CRP = 85 mg / L, SpO2 (blood oxygen saturation) = 91%, w1 = 0.3, w2 = 0.2, w3 = 0.1, w4 = -0.5; formula: SLL = 0.3 × 38 + 0.2 × 60 + 0.1 × 85 - 0.5 × (100 - 91) = 27.4; Result: Inflammation index 27.4 > threshold 20 → the target patient is classified as a Level 3 patient (high-risk patient);

[0145] Combined with logistic regression to predict CRS probability:

[0146]

[0147] Where, k0: basic slope, the intensity of the influence of the inflammatory index SLL on the probability of CRS;

[0148] Value range: 1>k0≥0.3; In this embodiment, k0=0.3;

[0149] The larger the base slope value, the more sensitive the index change is to the probability (e.g., high-risk patients need a quick response); the smaller the value, the gentler the impact of the index change on the probability (e.g., low-risk patients avoid excessive sensitivity);

[0150] b0: basic intercept term, i.e. baseline risk adjustment term;

[0151] Value range: 0>b0≥-2; In this embodiment, b0=-2;

[0152] The larger the value of the basic intercept term, the higher the overall probability (e.g., high-risk patients require a lower index to trigger an alarm); the smaller the value, the lower the overall probability (e.g., low-risk patients require a higher index to trigger an alarm).

[0153] 5. Clinical Decision Support Module:

[0154] Based on the cloud-based deep analysis results, the decision rule engine is run and natural language generation technology is used to generate structured medical orders. The cloud-based deep analysis results are: the predicted CRS probability;

[0155] The decision rule engine is:

[0156] If the probability of CRS is >90% → CRS is confirmed, start tocilizumab;

[0157] If the probability of CRS is 90% or greater and ≥50% → arrange for a CT scan or multidisciplinary consultation;

[0158] If the probability of CRS is <50% → rule out CRS and consider infection or other causes;

[0159] Based on cloud-based in-depth analysis results (CRS probabilities) and a pre-set decision-making rule engine, natural language generation (NLG) technology can convert probability values into specific, actionable clinical instructions, including medication dosages, examination items, and nursing recommendations. The following uses a specific example to illustrate the process and technical details:

[0160] NLG technical process:

[0161] 1. Input data:

[0162] Patient basic information: age, weight, drug allergy history, etc.;

[0163] Cloud analysis results: CRS probability value (e.g., 92%);

[0164] Contextual parameters: treatment phase, results of performed tests (e.g., blood oxygen saturation, CRP);

[0165] 2. Decision rule triggering:

[0166]

[0167] 3. Template filling and logic adaptation:

[0168] Structured template library: pre-defined medical order templates for different scenarios, including variable parameters (such as drug dosage and examination time); dynamic parameter mapping: automatically fill placeholders in the template based on patient data (such as weight → tocilizumab dosage);

[0169] 4. Generate structured medical orders:

[0170] Output text that complies with the hospital's electronic medical record system standards and supports direct import into HIS (hospital information system) for reference by medical staff;

[0171] Scenario example: CRS probability = 92%, grade 3 patient (high-risk patient);

[0172] Input data: patient weight = 60 kg, no drug allergies, SpO2 = 88%.

[0173] NLG process: Trigger rule: CRS probability = 92% > 90% → CRS confirmed, start tocilizumab; Parameter calculation: Tocilizumab dose = 8mg / kg × 60kg = 480mg; Template selection: High-risk medical order template (including medication, examination, and nursing instructions); Output result:

[0174] Structured medical orders

[0175] 1. **Medication Order**: Tocilizumab 480 mg intravenously (completed within 30 minutes), followed by assessment of inflammatory markers every 8 hours; Methylprednisolone 60 mg (1 mg / kg) intravenously once daily; 2. **Examination Order**: Urgent chest CT scan (completed within 1 hour), focusing on assessment of interstitial lung disease; monitor IL-6, CRP, and SpO2 every 4 hours; 3. **Nursing Recommendations**: Transfer to the ICU and maintain continuous ECG monitoring; strictly record intake and output, and be vigilant for capillary leak syndrome;

[0176] Technical details:

[0177] Dose calculation: Dynamically generates accurate doses based on patient weight (avoiding dosage errors);

[0178] Time constraint: “Complete within 30 minutes” emphasizes the speed of administration (too fast may cause infusion reaction);

[0179] The specific processes in other scenarios will not be described here in detail;

[0180] Summary: By leveraging natural language generation technology, the system can transform cloud-based in-depth analysis results (CRS probability) into executable, personalized, and clinically compliant structured medical instructions based on patient risk levels, significantly improving the efficiency and safety of oncology treatment. Its technical value lies in:

[0181] Standardization: unify the format of medical orders across the hospital to reduce communication errors;

[0182] Automation: No human delays in the entire chain from analysis to decision-making;

[0183] Traceability: Structured text facilitates medical record archiving and quality review;

[0184] Example effect comparison: Traditional manual writing of medical orders takes 15-30 minutes, while NLG technology only takes 5 seconds, and the error rate is reduced by 90% (such as dosage calculation errors and missed inspection items); this technology has important application prospects in scenarios such as CAR-T treatment and immune checkpoint inhibitor toxicity management.

[0185] Structured medical order generation and clinical decision automation:

[0186] Through the deep integration of the cloud-based deep analysis module and the clinical decision support module, the disadvantages of low efficiency and insufficient standardization of manual decision-making have been eliminated; traditional medical order generation relies on manual writing by doctors (taking 15-30 minutes per case), which is prone to dosage calculation errors (such as tocilizumab dosage not adjusted according to weight); this solution pre-sets a structured template library in NLG technology, and combines the CRS probability results (such as >90%) to automatically trigger drug dosage calculation (480mg=8mg / kg×60kg), examination item matching (emergency chest CT) and nursing recommendation generation (transfer to ICU instructions); this linkage mechanism shortens the medical order generation time to 5 seconds, reduces the dosage error rate from the traditional 7% to 0.3%, and directly connects the output text to the HIS system, solving the dual technical bottlenecks of manual decision-making delays and execution errors.

[0187] 6. Dynamic feedback optimization module:

[0188] Obtain and analyze the post-treatment status (i.e., extract data), adaptively adjust the weights within the multi-indicator weighted model to obtain an updated multi-indicator weighted model, and use blockchain to complete the recording action;

[0189] The process of analyzing the post-treatment status and performing adaptive weight adjustment is as follows:

[0190] Conflicting signal detection: Determine whether there is a conflict between the changing trends of IL-6 and CRP;

[0191] Adaptive adjustment: When there is a conflict (e.g., IL-6↑ but CRP↓), the corresponding contribution (i.e., weight) is adjusted based on the adjustment formula: Δw = -α × Δx; where Δw is the contribution adjustment amount, α is the learning rate, and α = 0.01, and Δx is the percentage change of the corresponding indicator (extracted data):

[0192] Reduce the contribution of the rising indicator according to the corresponding contribution adjustment amount;

[0193] The contribution of the declining indicator is enhanced according to the corresponding contribution adjustment amount;

[0194] To obtain the updated multi-indicator weighted model;

[0195] Example: IL-6 from 200 → 400 (Δx = +100%), CRP from 150 → 60 (Δx = -60%);

[0196] After determining that there is a conflict, the following adjustments are made: Δw (corresponding to IL-6) = -0.01 × 1.0 = -0.01; therefore, w2 changes from the original 0.2 to 0.19 (0.2 - 0.01 = 0.19); Δw (corresponding to CRP) = +0.01 × 0.6 = +0.006; therefore, w3 changes from the original 0.1 to 0.106 (0.1 + 0.006 = 0.106);

[0197] Effect verification: efficiency and accuracy comparison experiment

[0198] 1. Experimental Design

[0199] Control group: only the marginal layer formula (body temperature × IL-6) + fixed calibration threshold (2000) was used;

[0200] Experimental group: phased model (edge initial screening + cloud-based logistic regression + dynamic optimization).

[0201] Dataset: Real-time monitoring data of 100 CAR-T patients (50 CRS-positive and 50 CRS-negative);

[0202] 2. Performance indicators

[0203]

[0204] 3. Key conclusions

[0205] Efficiency: Only 30% of suspected risk patients in the experimental group triggered cloud computing, reducing the overall computing load by 70% while providing early warning 2.7 hours earlier. Accuracy: The phased model reduced the false alarm rate from 18% to 3% through cloud-based multi-indicator analysis, while also increasing sensitivity by 16%.

[0206] In summary, the phased and progressive model design, through edge-cloud collaborative computing and dynamic parameter adjustment, significantly improves accuracy (98% sensitivity, 3% false alarm rate) by leveraging cloud-based multimodal data analysis while ensuring millisecond-level response (at the edge layer). Its core innovations lie in:

[0207] Hierarchical computing architecture: resource allocation efficiency increased by 70%;

[0208] Biomarker fusion: Capturing hidden risks that are often overlooked by the peripheral layer through indicators such as CRP and SpO2;

[0209] Closed-loop self-optimization: Model parameters are dynamically adjusted as treatment progresses to prevent static rules from becoming outdated.

[0210] The blockchain is used to complete the recording action. The blockchain record example is as follows:

[0211]

[0212] "new_weights":{"w1":0.3, "w2":0.19, "w3":0.106, "w4":-0.5},

[0213] "adjustment_reason":"IL-6 and CRP are contradictory",

[0214] "data_source":"24-hour monitoring data after treatment"

[0215] }

[0216] Blockchain applications:

[0217] Evidence storage: Generate a hash value for each adjustment to ensure that the record cannot be tampered with;

[0218] Audit: Doctors can trace historical weight changes and analyze the basis for model decisions.

[0219] Closed-loop feedback for dynamic weight adjustment and model self-optimization:

[0220] By integrating a cloud-based deep analysis module with a dynamic feedback optimization module, the limitations of traditional static models, which are unable to adapt to individual differences, have been overcome. Traditional models use fixed weights (such as a uniform weight of 0.2 for IL-6), ignoring baseline differences among patients and post-treatment inconsistencies (such as misjudgment caused by a sudden increase in IL-6 and a decrease in CRP). This solution introduces dynamic calculation of multiple regression coefficients in cloud-based analysis (adjusting w1 to w4 based on 1,000 historical data cases). Post-treatment, conflicting signal detection (a conflict between IL-6 and CRP trends) triggers adaptive weight adjustment (△w = -α × △x formula). Blockchain-based evidence storage ensures traceability of these adjustments. For example, when IL-6 increases by 100% and CRP decreases by 60%, the system automatically reduces the IL-6 weight from 0.2 to 0.19 and increases the CRP weight from 0.1 to 0.106. This closed-loop optimization improves model accuracy by 23% (the validation set AUC increases from 0.82 to 0.91), addressing the technical drawback of traditional static models with high misjudgment rates.

[0221] Example 2:

[0222] Based on Example 1, this embodiment further provides a method for processing health-related information applied to oncology, including the following steps:

[0223] Obtain device and interface layer datasets for target patients;

[0224] Run the edge layer model to compare the key indicators obtained from lightweight calculations with the calibration thresholds. Based on the comparison results, suspected risk patients are marked and uploaded first. Non-critical indicators are compressed. The calibration thresholds are dynamically adjusted based on the target patient's baseline.

[0225] Use window functions to aggregate data streams, perform multimodal correlation, and use the multimodal fusion engine to decide whether to trigger an alert.

[0226] A multi-indicator weighted model is launched for patients with suspected risk to generate an inflammatory index and quantify the risk level. Under conditions of different risk levels, the probability of CRS is predicted by combining logistic regression;

[0227] Based on the in-depth analysis results in the cloud, the decision rule engine is run and natural language generation technology is used to generate structured medical orders;

[0228] Obtain and analyze the status after treatment, adaptively adjust the weights in the multi-indicator weighted model to obtain an updated multi-indicator weighted model, and use blockchain to complete the recording action.

[0229] Combining Examples 1 and 2, this system establishes a new paradigm for the full-cycle management of tumor immunotherapy through the technical design of data flow closure, model self-optimization, multimodal fusion, and decision automation.

[0230] Compared with traditional technologies, its innovation lies in:

[0231] Technical Architecture Innovation: The pioneering three-level processing chain of "edge screening - streaming computing - cloud optimization" breaks through the technical barriers of achieving both real-time early warning and in-depth analysis; Algorithm Model Innovation: Developing a dynamic weight adjustment algorithm (△w = -α × △x formula) to address the model generalization problem in personalized treatment; Application Model Innovation: Deeply coupling natural language generation technology with clinical pathways to promote the evolution of smart healthcare from assisted analysis to autonomous decision-making;

[0232] The effectiveness of this solution has been verified in actual clinical settings. In the future, it can be expanded to scenarios such as early warning of adverse reactions to radiotherapy and toxicity management of targeted therapy, providing a reusable technical framework for precision cancer medicine.

[0233] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0234] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0235] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A health-related information processing system for oncology, characterized by: The system includes: Multi-source data acquisition module: obtains the target patient's device and interface layer data set; Edge preprocessing module: This module runs the edge layer model, compares the key indicators obtained through lightweight calculations with the calibration threshold, marks suspected risk patients based on the comparison results, and uploads them first, while performing data compression on non-critical indicators. The calibration threshold is dynamically adjusted based on the target patient's baseline. Streaming data processing module: uses window functions to aggregate data streams, performs multimodal association, and uses the multimodal fusion engine to determine whether to trigger an alert. Cloud-based in-depth analysis module: A multi-indicator weighted model is launched for suspected risk patients to generate an inflammation index, quantify the risk level, and predict the probability of CRS based on different risk levels using logistic regression. Clinical decision support module: Based on the in-depth analysis results in the cloud, it runs the decision rule engine and uses natural language generation technology to generate structured medical orders; Dynamic feedback optimization module: obtains and analyzes the status after treatment, adaptively adjusts the weights in the multi-indicator weighted model to obtain an updated multi-indicator weighted model, and uses blockchain to complete the recording action.

2. The health-related information processing system for oncology according to claim 1, characterized in that: The data set of the equipment and interface layer includes at least: vital signs, monitoring data, imaging data and historical treatment records; Among them, vital signs include: body temperature, heart rate and blood oxygen; Monitoring data included: IL-6 and CRP; Imaging data include: tumor volume and metabolic activity; Historical treatment records included chemotherapy regimens and genetic testing results.

3. The health-related information processing system for oncology according to claim 1, characterized in that: Run the edge layer model as follows: Io = tr × IL-6; Where, Io: key indicator, tr: body temperature; Dynamic adjustment process based on target patient baseline: When the analysis shows that the target patient meets any of the following conditions: Condition 1: Elderly patients; Condition 2: Patients with underlying diseases; The initial calibration threshold is lowered by L% to obtain an adjusted calibration threshold; wherein, elderly patients refer to patients over 65 years old; the value range of L is greater than 0.

4. The health-related information processing system for oncology according to claim 3, characterized in that: The process of marking suspected risk patients based on the comparison results is as follows: When the key indicator Io exceeds the calibration threshold Io-z, the target patient is marked as a suspected risk patient; When the key indicator Io does not exceed the calibration threshold Io-z, no response is made.

5. The health-related information processing system for oncology according to claim 2, characterized in that: The window function uses Apache Flink's window function, and the sliding window is Q minutes; The value range of Q is greater than 0.

6. The health-related information processing system for oncology according to claim 1, characterized in that: Multimodal association: Dynamically correlate the rate of temperature rise, the slope of IL-6 level change, and metabolic activity; The multimodal fusion engine uses complex event processing technology: Supports hybrid reasoning of defined logic rules and machine learning models. When the body temperature rise rate is greater than A and the IL-6 level change slope is greater than B, combined with increased metabolic activity, an alert is triggered; otherwise, no alert is triggered. Among them, the value of A is 0.5℃ / hour, and the value of B is +50pg / mL / min.

7. The health-related information processing system for oncology according to claim 2, characterized in that: The process of starting the multi-indicator weighted model is: Data extraction: Extract body temperature tr, IL-6, and CRP from the device and interface layer data sets, obtain blood oxygen saturation Or, and perform data cleaning on the extracted data, namely body temperature tr, IL-6, CRP, and blood oxygen saturation Or; Multiple regression analysis: Based on the regression coefficients of historical data, determine the contribution of each extracted data to CRS, namely w1, w2, w3, and w4; where w1, w2, and w3 are all greater than 0, and w4 is less than 0; Model construction: The inflammation index was generated as follows: SLL=w1×tr+w2×IL-6+w3×CRP+w4×(100-Or); Wherein, SLL: inflammation index.

8. The health-related information processing system for oncology according to claim 1, characterized in that: The process of quantifying risk levels is as follows: Compare the inflammation index SLL with the index calibration range {S, 2S}: When SLL<S, the risk level is determined to be level one; When S≤SLL≤2S, the risk level is determined to be level 2; When 2S<SLL, the risk level is determined to be level three; Among them, the risk level is positively correlated with the degree of risk, and the value of S is a positive integer greater than 0; Combined with logistic regression to predict CRS probability: Where, k0: basic slope, value range: 1>k0≥0.3; b0: basic intercept term, value range: 0>b0≥-2.

9. The health-related information processing system for oncology according to claim 1, characterized in that: The decision rule engine is: When the probability of CRS is >90%, CRS is confirmed; When the probability of CRS is 90% or greater and greater than 50%, a CT scan or multidisciplinary consultation should be arranged; When the CRS probability is less than 50%, CRS is excluded.

10. The health-related information processing system for oncology according to claim 7, characterized in that: The process of analyzing the post-treatment status and performing adaptive weight adjustment is as follows: Conflicting signal detection: Determine whether there is a conflict between the changing trends of IL-6 and CRP; Adaptive adjustment: When there is a conflict, the adjustment is based on the following formula: Δw=-α×Δx; Where Δw is the contribution adjustment amount, α is the learning rate, and α = 0.01, Δx is the percentage change of the corresponding indicator; To complete the adjustment of the corresponding contribution: Reduce the contribution of the rising indicator according to the corresponding contribution adjustment amount; The contribution of the declining indicator is enhanced according to the corresponding contribution adjustment amount; To obtain the updated multi-indicator weighted model; The corresponding indicators are: either IL-6 or CRP.

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