A Clinical Data Analysis Method and System for Non-Small Cell Lung Cancer Based on MPNFS Theory

Abstract

This invention belongs to the field of non-small cell lung cancer (NSCLC) data analysis technology, specifically relating to a clinical data analysis method and system for NSCLC based on MPNFS theory. This invention provides comprehensive patient status assessment, improves the scientific rigor and precision of nursing care, generates individualized nursing reports, ensures that nursing interventions are highly matched to the patient's specific condition, helps improve the patient's treatment experience and quality of life, and by regularly acquiring nursing status data and information on changes in patient efficacy, it can promptly identify deficiencies in treatment and nursing care, adjust nursing care models, optimize patient management, and enhance patient treatment adherence, improve disease control and prognosis, and increase patient satisfaction through real-time monitoring and dynamic optimization of nursing care models.

Description

Technical Field

[0001] This invention belongs to the field of non-small cell lung cancer data analysis technology, specifically relating to a clinical data analysis method and system for non-small cell lung cancer based on MPNFS theory. Background Technology

[0002] Non-small cell lung cancer (NSCLC) is the most common type of lung cancer worldwide, accounting for approximately 85% of all lung cancer cases. Due to its high incidence and mortality rates, NSCLC has always been a focus of medical research and clinical treatment. With the continuous advancement of medical technology, the diagnostic and treatment methods for NSCLC are also constantly developing and improving.

[0003] MPNFS (Multi-Pronged Nursing Service) is a comprehensive nursing model based on medication (M), psychological intervention (P), nursing (N), family care (F), and social support (S). This theory emphasizes that in addition to medication, attention should be paid to the patient's psychological state, family and social support during the treatment process to comprehensively improve treatment outcomes and quality of life.

[0004] Current clinical treatment and nursing management methods are largely based on patients' traditional medical data, such as imaging results, tumor stage, and pathological type. While this data can help doctors make treatment decisions to some extent, it often overlooks the multidimensional nursing needs and individual differences of patients, resulting in less precise treatment plans and nursing interventions. In particular, the lack of means to dynamically track and adjust nursing models in real time during treatment makes it difficult to identify changes in the patient's condition in a timely manner and provide targeted interventions. Summary of the Invention

[0005] The purpose of this invention is to provide a clinical data analysis method for non-small cell lung cancer based on MPNFS theory, which can quantitatively assess the patient's condition, monitor the nursing effect in real time, and dynamically optimize the nursing model, thereby providing personalized and precise nursing plans and effectively improving the patient's treatment effect and quality of life.

[0006] The specific technical solution adopted by this invention is as follows:

[0007] A clinical data analysis method for non-small cell lung cancer based on MPNFS theory includes:

[0008] Acquire clinical data of patients with non-small cell lung cancer and develop corresponding treatment plans based on the clinical data;

[0009] Obtain the corresponding initial disease information based on clinical data, and obtain the corresponding initial disease level based on the initial disease information;

[0010] Multimodal nursing data was obtained based on the treatment plan, and information on changes in the efficacy of non-small cell lung cancer was obtained based on the multimodal nursing data. Adverse reaction information of non-small cell lung cancer patients was also obtained based on the multimodal nursing data.

[0011] Establish a detection cycle, acquire nursing status data within the detection cycle, and obtain corresponding nursing status change information based on the nursing status data;

[0012] A comprehensive nursing score is obtained based on information on changes in nursing status, changes in treatment efficacy, and adverse reactions. A nursing report is generated based on the comprehensive nursing score, and the comprehensive nursing model is adjusted based on the nursing report.

[0013] In a preferred embodiment, the steps of obtaining corresponding initial disease information based on clinical data and obtaining corresponding initial disease level based on the initial disease information include:

[0014] Obtain the corresponding initial disease information based on clinical data;

[0015] Obtain the corresponding initial disease vector based on the initial disease information;

[0016] Obtain the disease status table, which includes multiple disease interval vectors and the disease level corresponding to each disease interval vector;

[0017] Obtain the corresponding target disease interval vector based on the initial disease vector;

[0018] The disease level is obtained from the disease table based on the target disease interval vector.

[0019] In a preferred embodiment, the steps of acquiring multimodal care data based on the treatment plan, acquiring information on changes in efficacy in non-small cell lung cancer patients based on the multimodal care data, and acquiring information on adverse reactions in non-small cell lung cancer patients based on the multimodal care data include:

[0020] Obtain multimodal nursing data based on the treatment plan;

[0021] Information on the treatment effects of non-small cell lung cancer patients was obtained based on multimodal nursing data.

[0022] Obtain multiple corresponding therapeutic status values ​​based on treatment effect information;

[0023] The efficacy change value is obtained based on multiple efficacy status values ​​and marked as efficacy change information;

[0024] Information on adverse reactions in patients with non-small cell lung cancer was obtained from multimodal nursing data.

[0025] In a preferred embodiment, the step of acquiring multimodal care data according to the treatment plan includes:

[0026] Obtain the corresponding multiple treatment vectors based on the treatment plan;

[0027] Obtain multiple nursing care tables, where each nursing care table includes multiple treatment interval vectors and the corresponding nursing care method for each treatment interval vector;

[0028] For each treatment vector, obtain the corresponding target treatment interval vector from the corresponding nursing table;

[0029] The nursing method is obtained from the corresponding nursing table based on the target treatment interval vector;

[0030] The nursing methods corresponding to multiple treatment vectors are summarized, and the summarized results are marked as multimodal nursing data.

[0031] In a preferred embodiment, the step of obtaining adverse reaction information for non-small cell lung cancer patients based on multimodal care data includes:

[0032] Based on multimodal nursing data, the adverse reaction vector corresponding to each nursing method for non-small cell lung cancer patients was obtained;

[0033] Obtain adverse reaction values ​​based on multiple adverse reaction vectors;

[0034] Obtain the adverse reaction table, which includes multiple adverse reaction intervals and the corresponding adverse reaction status value for each adverse reaction interval;

[0035] Obtain the target adverse reaction range based on adverse reaction values;

[0036] Based on the target adverse reaction range, retrieve the corresponding adverse reaction status value from the adverse reaction table and mark it as adverse reaction information.

[0037] In a preferred embodiment, the steps of establishing a detection period, acquiring nursing status data within the detection period, and obtaining corresponding nursing status change information based on the nursing status data include:

[0038] Establish a testing cycle;

[0039] Acquire nursing status data of non-small cell lung cancer patients based on multimodal nursing data within the detection period;

[0040] Obtain multiple corresponding nursing status vectors based on the nursing status data;

[0041] Nursing status values ​​are obtained from multiple nursing status vectors;

[0042] Obtain the nursing status range;

[0043] Determine whether the nursing status value is within the nursing status range;

[0044] If the nursing status value is within the nursing status range, the nursing status change information is determined to be stable;

[0045] If the nursing status value is not within the nursing status range and is greater than the upper limit of the nursing status range, the nursing status change information is judged as improved.

[0046] If the nursing status value is not within the nursing status range and is less than the lower limit of the nursing status range, the nursing status change information is judged as deterioration.

[0047] In a preferred embodiment, the steps for constructing the detection cycle include:

[0048] Obtain the acquisition time of multimodal nursing data and mark it as the start time;

[0049] Obtain the duration table, which includes multiple disease levels and the corresponding testing duration for each disease level;

[0050] Obtain the corresponding testing duration from the duration table based on the initial disease level;

[0051] The end time is obtained based on the detection duration and start time;

[0052] The detection period is obtained based on the start time and end time.

[0053] In a preferred embodiment, the steps of obtaining a comprehensive nursing score based on information on changes in nursing status, changes in treatment efficacy, and adverse reactions; generating a nursing report based on the comprehensive nursing score; and adjusting the comprehensive nursing model based on the nursing report include:

[0054] Obtain the corresponding nursing status value based on the nursing status change information;

[0055] Obtain the corresponding efficacy change value based on the efficacy change information;

[0056] Obtain the corresponding adverse reaction values ​​based on the adverse reaction information;

[0057] A comprehensive nursing score is obtained based on nursing status values, efficacy change values, and adverse reaction values.

[0058] Nursing reports are generated based on the comprehensive nursing score, and the comprehensive nursing model is adjusted based on the nursing reports.

[0059] This invention also provides a clinical data analysis system for non-small cell lung cancer based on MPNFS theory, used in the aforementioned clinical data analysis method for non-small cell lung cancer based on MPNFS theory, comprising:

[0060] The treatment plan module is used to acquire clinical data of non-small cell lung cancer patients and obtain corresponding treatment plans based on the clinical data.

[0061] The initial condition module is used to obtain the corresponding initial condition information based on clinical data, and to obtain the corresponding initial condition level based on the initial condition information.

[0062] The multimodal nursing module is used to obtain multimodal nursing data based on the treatment plan, obtain information on changes in the efficacy of non-small cell lung cancer patients based on the multimodal nursing data, and obtain information on adverse reactions of non-small cell lung cancer patients based on the multimodal nursing data.

[0063] The nursing status module is used to construct the detection cycle, obtain nursing status data within the detection cycle, and obtain corresponding nursing status change information based on the nursing status data.

[0064] The nursing module is adjusted to obtain a comprehensive nursing score based on changes in nursing status, efficacy, and adverse reactions. A nursing report is generated based on the comprehensive nursing score, and the comprehensive nursing mode is adjusted based on the nursing report.

[0065] And, a clinical data analysis terminal for non-small cell lung cancer based on MPNFS theory, including:

[0066] One or more processors;

[0067] A storage device on which one or more programs are stored;

[0068] When one or more programs are executed by one or more processors, the one or more processors implement a clinical data analysis method for non-small cell lung cancer based on MPNFS theory.

[0069] The technical effects achieved by this invention are as follows:

[0070] This invention provides comprehensive patient status assessment, improves the scientific nature and precision of nursing models, generates individualized nursing reports, ensures that nursing interventions are highly matched with the specific patient's condition, helps improve the patient's treatment experience and quality of life, and by regularly acquiring nursing status data and information on changes in patient efficacy, it can promptly identify deficiencies in treatment and nursing, adjust nursing models, optimize patient management, and enhance patient treatment compliance, improve disease control and prognosis, and increase patient satisfaction through real-time monitoring and dynamic optimization of nursing models. Attached Figure Description

[0071] Figure 1 This is a flowchart of the method provided by the present invention;

[0072] Figure 2 This is a system module diagram provided by the present invention. Detailed Implementation

[0073] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0074] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0075] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that mutually excludes other embodiments.

[0076] Furthermore, the present invention will be described in detail with reference to the schematic diagrams. When describing the embodiments of the present invention in detail, the schematic diagrams are merely examples for ease of explanation and should not limit the scope of protection of the present invention.

[0077] Please see the appendix Figure 1 As shown, a clinical data analysis method for non-small cell lung cancer based on MPNFS theory is provided, including:

[0078] S1. Obtain clinical data of non-small cell lung cancer patients and obtain corresponding treatment plans based on the clinical data;

[0079] S2. Obtain the corresponding initial disease information based on clinical data, and obtain the corresponding initial disease level based on the initial disease information;

[0080] S3. Obtain multimodal nursing data based on the treatment plan, obtain information on changes in efficacy of non-small cell lung cancer patients based on the multimodal nursing data, and obtain information on adverse reactions of non-small cell lung cancer patients based on the multimodal nursing data.

[0081] S4. Construct a detection cycle, obtain nursing status data within the detection cycle, and obtain corresponding nursing status change information based on the nursing status data;

[0082] S5. Obtain a comprehensive nursing score based on information on changes in nursing status, changes in efficacy, and adverse reactions. Generate a nursing report based on the comprehensive nursing score and adjust the comprehensive nursing model based on the nursing report.

[0083] As described in steps S1 to S5 above, clinical data of non-small cell lung cancer patients are collected, including patient diagnostic information, tumor stage, pathological type, and gene mutation status. Individualized treatment plans are matched based on this data. Initial disease information, such as tumor size, metastasis, and functional scores, is extracted from the patient's clinical data to generate an initial disease level, quantifying the severity of the patient's condition. Multimodal nursing data generated during treatment (including drug treatment data, psychological intervention data, nursing data, family care data, social support data, and patient complaints) is used to obtain treatment effects (such as tumor shrinkage rate and prolonged survival) and adverse reactions (such as nausea and fatigue). Within a set monitoring period, changes in nursing status are obtained by monitoring nursing status data (such as nursing interventions and changes in patient quality of life), using a dynamic tracking method. This system provides real-time assessment of the effectiveness of nursing interventions and the changing trends of patient conditions. It quantifies and integrates information on changes in efficacy, adverse reactions, and nursing status to generate a comprehensive nursing score. Based on the score results, it generates individualized nursing reports, proposes optimization suggestions, and adjusts the comprehensive nursing model to better meet patient needs. It offers a holistic assessment of patient status, improves the scientific rigor and precision of the nursing model, and generates individualized nursing reports to ensure that nursing interventions are highly matched to the specific patient's condition. This helps improve the patient's treatment experience and quality of life. By regularly acquiring nursing status data and information on changes in patient efficacy, it can promptly identify deficiencies in treatment and nursing, adjust the nursing model, and optimize patient management. Through real-time monitoring and dynamic optimization of the nursing model, it helps enhance patient treatment adherence, improve disease control and prognosis, and increase patient satisfaction.

[0084] In a preferred embodiment, the steps of obtaining corresponding initial disease information based on clinical data and obtaining corresponding initial disease level based on the initial disease information include:

[0085] S201. Obtain the corresponding initial disease information based on clinical data;

[0086] S202. Obtain the corresponding initial disease vector based on the initial disease information;

[0087] S203. Obtain the disease status table, which includes multiple disease interval vectors and the disease level corresponding to each disease interval vector;

[0088] S204. Obtain the corresponding target disease interval vector based on the initial disease vector;

[0089] S205. Obtain the disease level from the disease table based on the target disease interval vector.

[0090] As described in steps S201 to S205 above, key disease information is extracted from the patient's clinical data, including but not limited to tumor size, stage (e.g., TNM stage), gene mutation status, and patient performance status (ECOG score or KPS score). The extracted initial disease information is then processed through data normalization and vectorization to construct a multi-dimensional vector (initial disease vector). Each dimension of the vector corresponds to a specific disease parameter, such as tumor size or stage score, thus forming a structured disease description. The disease table is a pre-constructed mapping model containing multiple disease interval vectors, each corresponding to a different disease level (e.g., ...). Mild: 2, Moderate: 3, Severe: 4). These intervals are determined through clinical research data and expert experience, and have broad applicability and scientific validity. Based on the initial disease vector, the closest disease interval vector is matched in the disease table as the target disease interval vector. Through the target disease interval vector, the corresponding disease level is directly extracted from the disease table, and the patient's initial disease level is finally determined. By constructing the initial disease vector and the disease table, the disease information is structured and quantified, avoiding errors caused by subjective judgment, improving the accuracy and repeatability of disease assessment, ensuring the scientific basis for each disease level, and improving the reliability of disease assessment.

[0091] In a preferred embodiment, the steps of acquiring multimodal care data based on the treatment plan, acquiring information on changes in efficacy in non-small cell lung cancer patients based on the multimodal care data, and acquiring adverse reactions in non-small cell lung cancer patients based on the multimodal care data include:

[0092] S301. Obtain multimodal nursing data based on the treatment plan;

[0093] S302. Obtain treatment effect information for non-small cell lung cancer patients based on multimodal nursing data;

[0094] S303. Obtain multiple corresponding therapeutic status values ​​based on the treatment effect information;

[0095] S304. Obtain the efficacy change value based on multiple efficacy status values ​​and mark it as efficacy change information;

[0096] S305. Obtain adverse reaction information for non-small cell lung cancer patients based on multimodal nursing data.

[0097] As described in steps S301 to S305 above, multimodal nursing data is collected based on the patient's treatment plan (e.g., surgery, chemotherapy, immunotherapy), including drug treatment data, psychological intervention data, nursing data, family care data, social support data, and patient complaints. Information related to treatment effectiveness is extracted; for example, the tumor shrinkage rate is assessed through drug treatment (e.g., according to RECIST criteria). Treatment effectiveness information is vectorized to generate multiple efficacy status values, each representing the treatment effect at a specific time point. For instance, changes in tumor volume and biomarker concentrations are treated as independent status values. Based on these multiple efficacy status values, efficacy change values ​​are calculated, such as the percentage change in tumor volume reduction and the trend of symptom relief, and these are marked as efficacy change information. The formula for calculating efficacy change values ​​is as follows: In the formula, The values ​​represent the changes in therapeutic efficacy, where h represents the number of multiple therapeutic efficacy status values, h = 1, 2, 3…t. Represented as the i-th efficacy status value, based on multimodal nursing data, information on possible adverse reactions (such as nausea, fatigue, anemia, infection, etc.) is extracted from the patient. Combined with the patient's self-report and nursing records, the severity and frequency of adverse reactions are assessed to provide a basis for treatment adjustment. By utilizing efficacy status values ​​at multiple time points, the patient's treatment effect and condition changes can be tracked in real time, providing data support for dynamic adjustment of treatment plans. This enables the provision of more personalized treatment and nursing plans for patients, improving the safety and effectiveness of treatment.

[0098] In a preferred embodiment, the step of acquiring multimodal care data according to the treatment plan includes:

[0099] S3011. Obtain the corresponding multiple treatment vectors according to the treatment plan;

[0100] S3012. Obtain multiple nursing tables, wherein each nursing table includes multiple treatment interval vectors and the nursing method corresponding to each treatment interval vector;

[0101] S3013. Obtain the corresponding target treatment interval vector from the corresponding nursing table based on each treatment vector;

[0102] S3014. Obtain the nursing method from the corresponding nursing table based on the target treatment interval vector;

[0103] S3015. Summarize the nursing methods corresponding to multiple treatment vectors and mark the summarization results as multimodal nursing data.

[0104] As described in steps S3011 to S3015 above, key parameters are extracted based on the patient's treatment plan (such as chemotherapy drug dosage, radiotherapy schedule, immunotherapy cycle, etc.), and these parameters are vectorized into multiple treatment vectors. A nursing table associated with each treatment plan is constructed. The nursing table includes multiple treatment interval vectors (e.g., chemotherapy dosage interval, radiotherapy frequency interval, etc.) and their corresponding nursing methods (e.g., nausea prevention nursing plan or skin management method). The nursing table is predefined through clinical experience and big data analysis. Each treatment vector is matched with the corresponding nursing table to find the target treatment interval vector that best matches the actual treatment situation. Based on the target treatment interval vector, the corresponding nursing methods are extracted from the nursing table. For example, a certain dose of chemotherapy plan corresponds to nausea prevention, blood index monitoring, and other nursing content. The nursing methods corresponding to different treatment vectors are summarized and integrated to generate comprehensive multimodal nursing data, which is marked as the final nursing data output. This data covers the nursing requirements of the patient throughout the entire treatment process, providing comprehensive support for personalized nursing, ensuring that each patient's nursing plan is highly relevant to their treatment details, and providing personalized and efficient nursing services.

[0105] In a preferred embodiment, the step of obtaining adverse reaction information for non-small cell lung cancer patients based on multimodal care data includes:

[0106] S3051. Obtain the adverse reaction vector corresponding to each nursing method for patients with non-small cell lung cancer based on multimodal nursing data;

[0107] S3052. Obtain adverse reaction values ​​based on multiple adverse reaction vectors;

[0108] S3053. Obtain the adverse reaction table, wherein the adverse reaction table includes multiple adverse reaction intervals and the adverse reaction status value corresponding to each adverse reaction interval;

[0109] S3054. Obtain the target adverse reaction range based on adverse reaction values;

[0110] S3055. Obtain the corresponding adverse reaction status value from the adverse reaction table according to the target adverse reaction range, and mark it as adverse reaction information.

[0111] As described in steps S3051 to S3055 above, based on multimodal nursing data, the potential adverse reactions (such as nausea, rash, anemia, etc.) of each nursing method are vectorized. Each adverse reaction vector contains information about the association between the nursing method and the potential adverse reaction, such as reaction type, probability of occurrence, and severity. An overall adverse reaction value is calculated based on all adverse reaction vectors. The formula for calculating the adverse reaction value is as follows: In the formula, The adverse reaction value is represented as g, where g represents the index of multiple adverse reaction vectors, g = 2, 3, 4…j. This is represented as the g-th adverse reaction vector. Represented as the (g-1)th adverse reaction vector, the adverse reaction table is a predefined standard database that includes multiple adverse reaction intervals (such as reaction value ranges) and their corresponding adverse reaction status values ​​(e.g., "mild", "moderate", "severe"). This table is generated based on a large amount of clinical data to ensure its scientific validity and applicability. Based on the calculated adverse reaction value, it is matched with the intervals in the adverse reaction table to find the target adverse reaction interval that best matches the patient's actual state. The corresponding adverse reaction status value is extracted from the target adverse reaction interval and marked as the patient's adverse reaction information, forming a complete assessment report. It can quantify the potential impact of each nursing method on adverse reactions in detail, provide accurate adverse reaction risk assessment, and dynamically track changes in the patient's state at different nursing stages, providing a basis for real-time adjustment of nursing strategies.

[0112] In a preferred embodiment, the steps of establishing a detection period, acquiring nursing status data within the detection period, and obtaining corresponding nursing status change information based on the nursing status data include:

[0113] S401, Establish the detection cycle;

[0114] S402. Obtain nursing status data for non-small cell lung cancer patients based on multimodal nursing data within the detection period;

[0115] S403. Obtain multiple corresponding nursing status vectors based on the nursing status data;

[0116] S404. Obtain nursing status values ​​based on multiple nursing status vectors;

[0117] S405, Obtain nursing status range;

[0118] S406. Determine whether the nursing status value is within the nursing status range;

[0119] If the nursing status value is within the nursing status range, the nursing status change information is determined to be stable;

[0120] If the nursing status value is not within the nursing status range and is greater than the upper limit of the nursing status range, the nursing status change information is judged as improved.

[0121] If the nursing status value is not within the nursing status range and is less than the lower limit of the nursing status range, the nursing status change information is judged as deterioration.

[0122] As described in steps S401 to S406 above, a reasonable monitoring cycle is set according to the patient's nursing needs and treatment plan. For example, relevant data is recorded daily, weekly, or after each nursing session. Within the monitoring cycle, patient nursing status data is collected based on multimodal nursing data, including vital signs (such as body temperature and blood oxygen saturation), symptom feedback (such as pain level and fatigue), and nursing responses (such as the frequency and implementation of nursing measures). The collected nursing status data is converted into standardized multimodal nursing status vectors. These vectors include key parameters of the patient's current nursing status. The nursing status value is calculated based on multiple nursing status vectors. The formula for calculating the nursing status value is as follows: In the formula, This is represented by nursing status values, where i represents the index of multiple nursing status vectors, i=1,2,3…n. Represented as the i-th nursing status vector, the nursing status interval is a predefined standard range used to divide the nursing evaluation results of different states. This interval is derived from the analysis of a large amount of clinical data and includes upper and lower limits, defining the range of "normal", "good", or "deviation". If the nursing status value is within the nursing status interval, the nursing status change information is judged as "stable", that is, the patient's nursing effect meets expectations. If the nursing status value is higher than the upper limit of the nursing status interval, it means that the patient's nursing effect is better than expected and is judged as "improving". If the nursing status value is lower than the lower limit of the nursing status interval, it means that the patient's nursing effect is lower than expected or the nursing measures need to be adjusted and is judged as "deteriorating". It can dynamically track the patient's nursing status, detect fluctuations in nursing effect in a timely manner, and provide a basis for adjusting the nursing plan.

[0123] In a preferred embodiment, the step of constructing the detection cycle includes:

[0124] S4011. Obtain the acquisition time of multimodal nursing data and mark it as the start time;

[0125] S4012. Obtain the duration table, which includes multiple disease levels and the corresponding testing duration for each disease level;

[0126] S4013. Obtain the corresponding testing duration from the duration table based on the initial disease level;

[0127] S4014. Obtain the end time based on the detection duration and start time;

[0128] S4015. Obtain the detection cycle based on the start time and end time.

[0129] As described in steps S4011 to S4015 above, the time of the first data acquisition is extracted from the multimodal nursing data and marked as the start time of the testing cycle. This time point serves as the starting point of the cycle. The duration table is a standard database built based on a large amount of clinical data and expert experience, containing testing durations (e.g., 6 hours, 24 hours, 72 hours) corresponding to different disease levels (e.g., mild, moderate, severe). Based on the patient's initial disease level (determined by early diagnosis or disease assessment), the corresponding testing duration is extracted from the duration table. For example, the more severe the condition, the shorter the testing duration and the more frequent the monitoring, to ensure timely detection of problems. The testing duration is added to the start time to obtain the end time of the testing cycle. For example, if the initial... The testing period starts at 12:00 on December 1, 2024, lasts for 24 hours, and ends at 12:00 on December 2, 2024. Combining the start and end times, a complete testing cycle is determined to guide data collection and nursing status monitoring. For example, the testing cycle can be defined as "12:00 on December 1, 2024 to 12:00 on December 2, 2024". The testing cycle can be flexibly adjusted according to the patient's initial condition level, and suitable monitoring plans can be developed for different conditions to ensure that high-risk patients receive more intensive monitoring and care, avoiding the waste of resources caused by overly frequent monitoring, while ensuring the timely collection of important data, effectively balancing nursing quality and resource utilization efficiency.

[0130] In a preferred embodiment, the steps of obtaining a comprehensive nursing score based on information on changes in nursing status, changes in treatment efficacy, and adverse reactions; generating a nursing report based on the comprehensive nursing score; and adjusting the comprehensive nursing model based on the nursing report include:

[0131] S501. Obtain the corresponding nursing status value based on the nursing status change information;

[0132] S502. Obtain the corresponding efficacy change value based on the efficacy change information;

[0133] S503. Obtain the corresponding adverse reaction values ​​based on the adverse reaction information;

[0134] S504. Obtain a comprehensive nursing score based on nursing status values, efficacy change values, and adverse reaction values;

[0135] S505. Generate a nursing report based on the comprehensive nursing score, and adjust the comprehensive nursing model based on the nursing report.

[0136] As described in steps S501 to S505 above, based on nursing status change information (such as "stable," "improving," and "deteriorating"), corresponding nursing status values ​​are obtained; based on efficacy change information (such as changes in the patient's clinical indicators and the achievement of treatment goals), corresponding efficacy change values ​​are obtained; and based on adverse reaction information (such as the frequency and severity of adverse reactions), corresponding adverse reaction values ​​are obtained. Using the nursing status values, efficacy change values, and adverse reaction values ​​as inputs, a comprehensive nursing score is calculated. The formula for calculating the comprehensive nursing score is as follows: In the formula, P represents the comprehensive nursing score. This is expressed as a change in therapeutic effect. This is expressed as an adverse reaction value. The comprehensive nursing score is used to generate a nursing report, which includes the patient's nursing status, treatment efficacy, summary of adverse reactions, and improvement suggestions. This report provides doctors and the nursing team with comprehensive decision-making support. A high comprehensive score indicates good nursing outcomes, allowing the current nursing model to be maintained. A moderate score indicates the need to optimize some nursing measures, such as increasing nursing frequency or adjusting treatment plans. A low score indicates a need for significant adjustments to the current nursing model, such as increasing monitoring frequency or changing nursing methods. By calculating the comprehensive nursing score, complex nursing outcomes are transformed into quantifiable indicators, facilitating objective assessment and tracking of nursing quality. The comprehensive nursing score and the generated nursing report provide comprehensive and accurate data support for the medical team, helping to develop more scientific nursing plans and optimize the nursing model. The nursing model can be dynamically adjusted as the score and report change, ensuring that nursing outcomes are optimized in sync with changes in the patient's condition and improving the efficiency of nursing resource utilization.

[0137] Please see the appendix Figure 2 As shown, the present invention also provides a clinical data analysis system for non-small cell lung cancer based on MPNFS theory, used in the above-mentioned clinical data analysis method for non-small cell lung cancer based on MPNFS theory, comprising:

[0138] The treatment plan module is used to acquire clinical data of non-small cell lung cancer patients and obtain corresponding treatment plans based on the clinical data.

[0139] The initial condition module is used to obtain the corresponding initial condition information based on clinical data, and to obtain the corresponding initial condition level based on the initial condition information.

[0140] The multimodal nursing module is used to obtain multimodal nursing data based on the treatment plan, obtain information on changes in the efficacy of non-small cell lung cancer patients based on the multimodal nursing data, and obtain information on adverse reactions of non-small cell lung cancer patients based on the multimodal nursing data.

[0141] The nursing status module is used to construct the detection cycle, obtain nursing status data within the detection cycle, and obtain corresponding nursing status change information based on the nursing status data.

[0142] The nursing module is adjusted to obtain a comprehensive nursing score based on changes in nursing status, efficacy, and adverse reactions. A nursing report is generated based on the comprehensive nursing score, and the comprehensive nursing mode is adjusted based on the nursing report.

[0143] The treatment plan module receives clinical data from non-small cell lung cancer patients, including diagnostic information, physical examination data, and gene testing results, and generates a suitable treatment plan for the patient. The initial condition module extracts initial condition information based on the patient's clinical data and quantifies the condition using a vectorization method. It then maps this information to specific initial condition levels using condition interval vectors in the condition table. The multimodal nursing module derives multimodal nursing data from the treatment plan, covering medication management, psychological counseling, and daily living care. During the nursing process, it collects information on changes in patient efficacy and adverse reaction data and updates it in real time. The nursing status module analyzes the trend of status changes based on nursing status data collected within the monitoring period and uses preset parameters. The status interval rule determines the stability, improvement, or deterioration of the nursing status. The nursing adjustment module calculates a comprehensive nursing score based on comprehensive information (changes in nursing status, changes in efficacy, and adverse reactions), generates a standardized nursing report, and automatically adjusts the nursing mode based on the report's recommendations, such as changing nursing frequency, optimizing medication, or adjusting treatment plans. It provides personalized treatment plans and nursing suggestions, reduces blind spots, improves treatment effectiveness, and can track changes in the patient's nursing status in real time. By identifying disease trends through dynamic changes in nursing status values, it provides data support for early intervention. Through scientific and quantitative assessment methods and personalized nursing plans, it improves patient treatment compliance and nursing satisfaction, while reducing the psychological burden on patients and their families.

[0144] And, a clinical data analysis terminal for non-small cell lung cancer based on MPNFS theory, including:

[0145] One or more processors;

[0146] A storage device on which one or more programs are stored;

[0147] When one or more programs are executed by one or more processors, the one or more processors implement a clinical data analysis method for non-small cell lung cancer based on MPNFS theory.

[0148] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited. This invention belongs to the field of non-small cell lung cancer data analysis technology, specifically relating to a clinical data analysis method and system for non-small cell lung cancer based on MPNFS theory.

Claims

1. A clinical data analysis method for non-small cell lung cancer based on MPNFS theory, characterized in that, include: Acquire clinical data of patients with non-small cell lung cancer and develop corresponding treatment plans based on the clinical data; Obtain the corresponding initial disease information based on clinical data, and obtain the corresponding initial disease level based on the initial disease information; Multimodal nursing data was obtained based on the treatment plan, and information on changes in the efficacy of non-small cell lung cancer was obtained based on the multimodal nursing data. Adverse reaction information of non-small cell lung cancer patients was also obtained based on the multimodal nursing data. Establish a detection cycle, acquire nursing status data within the detection cycle, and obtain corresponding nursing status change information based on the nursing status data; A comprehensive nursing score is obtained based on information on changes in nursing status, changes in treatment efficacy, and adverse reactions. A nursing report is generated based on the comprehensive nursing score, and the comprehensive nursing model is adjusted based on the nursing report. The steps for obtaining multimodal nursing data based on the treatment plan, obtaining information on changes in efficacy in non-small cell lung cancer patients based on the multimodal nursing data, and obtaining information on adverse reactions in non-small cell lung cancer patients based on the multimodal nursing data include: Obtain multimodal nursing data based on the treatment plan; Information on the treatment effects of non-small cell lung cancer patients was obtained based on multimodal nursing data. Obtain multiple corresponding therapeutic status values ​​based on treatment effect information; The efficacy change value is obtained based on multiple efficacy status values ​​and marked as efficacy change information; Information on adverse reactions in non-small cell lung cancer patients was obtained from multimodal nursing data. The steps for obtaining multimodal nursing data based on the treatment plan include: Obtain the corresponding multiple treatment vectors based on the treatment plan; Obtain multiple nursing care tables, where each nursing care table includes multiple treatment interval vectors and the corresponding nursing care method for each treatment interval vector; For each treatment vector, obtain the corresponding target treatment interval vector from the corresponding nursing table; The nursing method is obtained from the corresponding nursing table based on the target treatment interval vector; The nursing methods corresponding to multiple treatment vectors are summarized, and the summarized results are marked as multimodal nursing data. The steps for obtaining adverse reaction information in non-small cell lung cancer patients based on multimodal nursing data include: Based on multimodal nursing data, the adverse reaction vector corresponding to each nursing method for non-small cell lung cancer patients was obtained; Obtain adverse reaction values ​​based on multiple adverse reaction vectors; Obtain the adverse reaction table, which includes multiple adverse reaction intervals and the corresponding adverse reaction status value for each adverse reaction interval; Obtain the target adverse reaction range based on adverse reaction values; Based on the target adverse reaction range, retrieve the corresponding adverse reaction status value from the adverse reaction table and mark it as adverse reaction information; The steps for obtaining a comprehensive nursing score based on changes in nursing status, treatment efficacy, and adverse reactions, generating a nursing report based on the comprehensive nursing score, and adjusting the comprehensive nursing model based on the nursing report include: Obtain the corresponding nursing status value based on the nursing status change information; Obtain the corresponding efficacy change value based on the efficacy change information; Obtain the corresponding adverse reaction values ​​based on the adverse reaction information; A comprehensive nursing score is obtained based on nursing status values, changes in therapeutic efficacy values, and adverse reaction values. The formula for calculating the comprehensive nursing score is as follows: In the formula, P represents the comprehensive nursing score. This is expressed as a change in therapeutic effect. This is expressed as an adverse reaction value. Represented as nursing status value; Nursing reports are generated based on the comprehensive nursing score, and the comprehensive nursing model is adjusted based on the nursing reports.

2. The clinical data analysis method for non-small cell lung cancer based on MPNFS theory according to claim 1, characterized in that, The steps for obtaining initial disease information based on clinical data and then determining the initial disease level based on that initial disease information include: Obtain the corresponding initial disease information based on clinical data; Obtain the corresponding initial disease vector based on the initial disease information; Obtain the disease status table, which includes multiple disease interval vectors and the disease level corresponding to each disease interval vector; Obtain the corresponding target disease interval vector based on the initial disease vector; The disease level is obtained from the disease table based on the target disease interval vector.

3. The clinical data analysis method for non-small cell lung cancer based on MPNFS theory according to claim 1, characterized in that, The steps of establishing a monitoring cycle, acquiring nursing status data within the monitoring cycle, and obtaining corresponding nursing status change information based on the nursing status data include: Establish a testing cycle; Acquire nursing status data of non-small cell lung cancer patients based on multimodal nursing data within the detection period; Obtain multiple corresponding nursing status vectors based on the nursing status data; Nursing status values ​​are obtained from multiple nursing status vectors; Obtain the nursing status range; Determine whether the nursing status value is within the nursing status range; If the nursing status value is within the nursing status range, the nursing status change information is determined to be stable; If the nursing status value is not within the nursing status range and is greater than the upper limit of the nursing status range, the nursing status change information is judged as improved. If the nursing status value is not within the nursing status range and is less than the lower limit of the nursing status range, the nursing status change information is judged as deterioration.

4. The clinical data analysis method for non-small cell lung cancer based on MPNFS theory according to claim 1, characterized in that, The steps for establishing a detection cycle include: Obtain the acquisition time of multimodal nursing data and mark it as the start time; Obtain the duration table, which includes multiple disease levels and the corresponding testing duration for each disease level; Obtain the corresponding testing duration from the duration table based on the initial disease level; The end time is obtained based on the detection duration and start time; The detection period is obtained based on the start time and end time.

5. A clinical data analysis system for non-small cell lung cancer based on MPNFS theory, applied to the clinical data analysis method for non-small cell lung cancer based on MPNFS theory as described in any one of claims 1 to 4, characterized in that, include: The treatment plan module is used to acquire clinical data of non-small cell lung cancer patients and obtain corresponding treatment plans based on the clinical data. The initial condition module is used to obtain the corresponding initial condition information based on clinical data, and to obtain the corresponding initial condition level based on the initial condition information. The multimodal nursing module is used to obtain multimodal nursing data based on the treatment plan, obtain information on changes in the efficacy of non-small cell lung cancer patients based on the multimodal nursing data, and obtain information on adverse reactions of non-small cell lung cancer patients based on the multimodal nursing data. The nursing status module is used to construct the detection cycle, obtain nursing status data within the detection cycle, and obtain corresponding nursing status change information based on the nursing status data. The nursing module is adjusted to obtain a comprehensive nursing score based on changes in nursing status, efficacy, and adverse reactions. A nursing report is generated based on the comprehensive nursing score, and the comprehensive nursing mode is adjusted based on the nursing report.

6. A clinical data analysis terminal for non-small cell lung cancer based on MPNFS theory, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When one or more programs are executed by one or more processors, the one or more processors implement the non-small cell lung cancer clinical data analysis method based on MPNFS theory as described in any one of claims 1 to 4.

Images