Multi-parameter accelerated rehabilitation evaluation method for gastric cancer postoperative ERAS management

Abstract

The invention discloses a multi-parameter accelerated rehabilitation evaluation method for stomach cancer postoperative ERAS management, and belongs to the technical field of medical data processing, and the method comprises the steps: obtaining stomach cancer postoperative multi-source data of a patient, carrying out the preprocessing and fusion analysis of the stomach cancer postoperative multi-source data of the patient, and forming stomach cancer postoperative fusion data of the patient; according to the multi-parameter accelerated rehabilitation evaluation model, analyzing and identifying the gastric cancer postoperative fusion data of the patient, determining a gastric cancer postoperative multi-parameter accelerated rehabilitation evaluation result of the patient, and realizing dynamic risk evaluation of gastric cancer postoperative rehabilitation of the patient. The method solves the problem that the existing method is high in subjectivity and difficult to comprehensively consider multi-dimensional data at the same time, so that the gastric cancer postoperative rehabilitation management effect of the patient is poor. The method does not have a quantitative standard for judging whether the patient reaches the standard or not, multi-dimensional data such as laboratory indexes, vital signs, pain scores and activity amount can be comprehensively considered at the same time, and the gastric cancer postoperative rehabilitation management effect of the patient can be improved.

Description

Technical Field

[0001] This invention relates to the field of medical data processing technology, specifically to a multi-parameter accelerated recovery assessment method for postoperative ERAS management of gastric cancer. Background Technology

[0002] Enhanced Recovery After Surgery (ERAS), proposed and implemented by Danish researchers, refers to a series of evidence-based optimized treatment measures adopted during the perioperative period to promote rapid patient recovery. These measures aim to reduce psychological and physiological trauma and stress responses, thereby reducing complications, shortening hospital stays, lowering in-hospital risks, and ultimately reducing medical costs. ERAS is a multidisciplinary management model that integrates optimized clinical pathways throughout the entire treatment process—pre-hospitalization, pre-operative, intra-operative, and post-operative—with a core emphasis on a patient-centered approach to care.

[0003] The recovery process for patients after gastric cancer surgery is complex, involving multiple dimensions such as physiological function recovery, nutritional status, pain management, and complication prevention. Traditional ERAS management often relies on the clinical experience of medical staff and manual records, which has the following problems:

[0004] It is highly subjective, lacks quantitative standards for judging whether patients have met the criteria, is easily influenced by the doctor's personal experience, and is difficult to comprehensively consider multi-dimensional data such as laboratory indicators, vital signs, pain scores and activity levels, resulting in poor postoperative rehabilitation management for gastric cancer patients. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-parameter accelerated recovery assessment method for postoperative ERAS management of gastric cancer. This method eliminates the lack of quantitative standards for judging whether a patient has met the target criteria. It can simultaneously consider multi-dimensional data such as laboratory indicators, vital signs, pain scores, and activity levels, thereby improving the postoperative rehabilitation management effect of gastric cancer patients and solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A multi-parameter accelerated recovery assessment method for postoperative ERAS management in gastric cancer includes:

[0008] After acquiring multi-source data from patients following gastric cancer surgery using a data acquisition terminal, the multi-source data is preprocessed and fused using edge computing to form fused data from patients following gastric cancer surgery.

[0009] A multi-parameter accelerated recovery assessment model is constructed based on artificial intelligence. The fusion data of patients after gastric cancer surgery is analyzed and identified according to the multi-parameter accelerated recovery assessment results of patients after gastric cancer surgery, so as to realize dynamic risk assessment of patients' postoperative gastric cancer recovery.

[0010] Preferably, multi-source data from patients after gastric cancer surgery is acquired based on a data acquisition terminal, including:

[0011] Real-time monitoring of heart rate, respiratory rate, blood pressure, blood oxygen saturation, body temperature, electrocardiogram waveform, blood routine, C-reactive protein, procalcitonin, electrolytes and liver and kidney function of patients after gastric cancer surgery, to obtain vital signs and physiological signals of patients after gastric cancer surgery;

[0012] Real-time monitoring of patients' postoperative ambulation, gait quantification, limb function and sleep quality was conducted to obtain functional indicators of patients' postoperative activity ability after gastric cancer surgery.

[0013] Real-time monitoring of patients' intake, excretion, metabolic indicators and body composition after gastric cancer surgery to obtain nutritional status and metabolic indicators after gastric cancer surgery.

[0014] Real-time monitoring of pain intensity, analgesic medication, gastrointestinal symptoms and other symptoms in patients after gastric cancer surgery, and obtaining subjective reports of pain symptoms after gastric cancer surgery;

[0015] Real-time monitoring of patients' emotional state, confidence in recovery, cognitive function and social support after gastric cancer surgery to obtain the patients' psychosocial psychological state after gastric cancer surgery.

[0016] Based on the patient's vital signs, physiological signals, activity level, functional indicators, nutritional status, metabolic indicators, subjective reports of pain symptoms, and psychosocial status after gastric cancer surgery, multi-source data on the patient's postoperative gastric cancer surgery are generated.

[0017] Preferably, the out-of-bed activity includes duration, frequency, sitting, standing, and walking states; the walking quantification includes steps, distance, speed, and gait cycle; limb function includes grip strength and headboard lift angle; sleep quality includes total duration, number of awakenings, and deep sleep percentage;

[0018] The intake includes oral water intake, liquid or semi-liquid intake; the output includes gastrointestinal decompression, drainage fluid properties, drainage fluid volume, and urine volume; metabolic indicators include blood glucose, prealbumin, and transferrin; body composition includes body weight and extracellular water ratio.

[0019] The pain intensity includes VAS or NRS scores at rest and during activity; analgesics include the dosage of opioids and other analgesics; gastrointestinal symptoms include nausea, vomiting, abdominal distension scores, and the time of first flatus and defecation; other symptoms include fatigue level and dyspnea scores.

[0020] The emotional state includes anxiety and depression scores; recovery confidence includes self-efficacy scores on recovery ability; cognitive function includes postoperative delirium screening scale scores; and social support includes visitor visitation rate and family support.

[0021] Preferably, the preprocessing of multi-source data from patients after gastric cancer surgery based on edge computing involves the following operations:

[0022] Data cleaning was performed on multi-source data from patients after gastric cancer surgery. This included establishing a time window to map the multi-source data to the same time axis after surgery, removing noise from the multi-source data, and identifying missing and outlier values ​​in the multi-source data.

[0023] The missing and outlier values ​​in the multi-source data of patients after gastric cancer surgery were evaluated to determine whether the missing and outlier values ​​in the multi-parameter accelerated recovery assessment of patients after gastric cancer surgery were valuable.

[0024] When missing values ​​and outliers in multi-source data after gastric cancer surgery are valuable for the patient's multi-parameter accelerated recovery assessment, missing values ​​are filled and outliers are corrected; otherwise, missing values ​​and outliers are deleted.

[0025] The multi-source data of patients after gastric cancer surgery were standardized. Specifically, the multi-source data of patients after gastric cancer surgery with different dimensions were normalized and mapped to the [0,1] interval to remove the dimensional differences between the multi-source data of patients after gastric cancer surgery, thus forming standardized multi-source data of patients after gastric cancer surgery.

[0026] Preferably, the following operations are performed to fuse and analyze multi-source data from patients after gastric cancer surgery based on edge computing:

[0027] Feature extraction was performed on multi-source data from patients after gastric cancer surgery. Feature vectors related to multi-parameter accelerated recovery assessment of patients were extracted from the multi-source data after gastric cancer surgery, including temporal physiological features, activity and behavior features, clinical symptom features, nutritional and metabolic features, and psychological state features.

[0028] Feature fusion is performed on the extracted feature vectors. Based on feature-level fusion, cross-modal feature association and weighted fusion are performed on temporal physiological features, activity behavior features, clinical symptom features, nutritional metabolism features and psychological state features to form fused data of patients after gastric cancer surgery.

[0029] Preferably, a multi-parameter accelerated recovery assessment model is constructed based on artificial intelligence, and the following operations are performed:

[0030] Based on the multi-parameter accelerated recovery assessment requirements of patients after gastric cancer surgery ERAS management, historical data of multi-parameter accelerated recovery assessment were collected and divided into training set and test set.

[0031] The machine learning model was trained using a training set, enabling it to autonomously learn multi-parameter accelerated recovery assessment behaviors for ERAS management after gastric cancer surgery. The model was then used to automatically assess the multi-parameter accelerated recovery status of patients after gastric cancer surgery, thus determining the multi-parameter accelerated recovery assessment model.

[0032] The multi-parameter accelerated recovery assessment model was tested using a test set to evaluate its generalization performance and determine whether it could achieve the expected effect of automating the assessment of multi-parameter accelerated recovery after gastric cancer surgery.

[0033] When the multi-parameter accelerated recovery assessment model fails to achieve the expected effect of automatically assessing the multi-parameter accelerated recovery status of patients after gastric cancer surgery, the parameters of the multi-parameter accelerated recovery assessment model are adjusted and optimized until the multi-parameter accelerated recovery assessment model can achieve the expected effect of automatically assessing the multi-parameter accelerated recovery status of patients after gastric cancer surgery, and the optimal multi-parameter accelerated recovery assessment model is determined.

[0034] Preferably, the postoperative fusion data of patients with gastric cancer are analyzed and identified based on a multi-parameter accelerated recovery assessment model, and the following operations are performed:

[0035] The fusion data of patients after gastric cancer surgery is input into a multi-parameter accelerated recovery assessment model. The fusion data of patients after gastric cancer surgery is analyzed and identified according to the multi-parameter accelerated recovery assessment model, and the multi-parameter accelerated recovery status of patients after gastric cancer surgery is automatically assessed to determine the multi-parameter accelerated recovery assessment results of patients after gastric cancer surgery.

[0036] Preferably, the recovery stage after gastric cancer surgery is determined based on the results of the multi-parameter accelerated recovery assessment, including the accelerated recovery period, the stable observation period, and the risk blocking period.

[0037] For patients in the accelerated recovery period, a green pass label is marked on them, and they are discharged quickly according to the standard ERAS pathway;

[0038] For patients in the stable observation period, mark them with a yellow attention label, provide routine care, and strengthen monitoring.

[0039] For patients in the risk blocking phase, a red warning label is marked to trigger the warning, and the patient is given enhanced intervention and MDT consultation.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] This invention acquires multi-source data from patients after gastric cancer surgery via a data acquisition terminal. It preprocesses and fuses this data using edge computing to form fused post-gastric cancer data. A multi-parameter accelerated recovery assessment model is then constructed based on artificial intelligence. This model is used to analyze and identify the fused post-gastric cancer data, determining the patient's multi-parameter accelerated recovery assessment results. This enables dynamic risk assessment of post-gastric cancer rehabilitation, eliminating the lack of quantitative standards for determining whether a patient has met the criteria. It comprehensively considers multi-dimensional data such as laboratory indicators, vital signs, pain scores, and activity levels, thereby improving the effectiveness of post-gastric cancer rehabilitation management. Attached Figure Description

[0042] Figure 1 This is a flowchart of the multi-parameter accelerated recovery assessment method for postoperative ERAS management of gastric cancer according to the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] To address the issue of poor postoperative rehabilitation management for gastric cancer patients due to the inherent subjectivity and difficulty in simultaneously and comprehensively considering multidimensional data such as laboratory indicators, vital signs, pain scores, and activity levels, please refer to [link to relevant documentation]. Figure 1 This embodiment provides the following technical solution:

[0045] A multi-parameter accelerated recovery assessment method for postoperative ERAS management in gastric cancer includes:

[0046] Multi-source data from patients after gastric cancer surgery were acquired using a data acquisition terminal;

[0047] In this embodiment, multi-source data from the patient's post-gastric cancer surgery is acquired based on a data acquisition terminal, including:

[0048] Real-time monitoring of heart rate, respiratory rate, blood pressure, blood oxygen saturation, body temperature, electrocardiogram waveform, blood routine, C-reactive protein, procalcitonin, electrolytes and liver and kidney function of patients after gastric cancer surgery, to obtain vital signs and physiological signals of patients after gastric cancer surgery;

[0049] Real-time monitoring of patients' postoperative ambulation, gait quantification, limb function and sleep quality was conducted to obtain functional indicators of patients' postoperative activity ability after gastric cancer surgery.

[0050] Specifically, the out-of-bed activities include duration, frequency, sitting, standing, and walking states; the walking quantification includes steps, distance, speed, and gait cycle; limb function includes grip strength and headboard elevation angle; sleep quality includes total duration, number of awakenings, and deep sleep percentage.

[0051] Real-time monitoring of patients' intake, excretion, metabolic indicators and body composition after gastric cancer surgery to obtain nutritional status and metabolic indicators after gastric cancer surgery.

[0052] Specifically, the intake includes oral water intake, liquid or semi-liquid intake; the output includes gastrointestinal decompression, drainage fluid properties, drainage fluid volume, and urine volume; metabolic indicators include blood glucose, prealbumin, and transferrin; and body composition includes body weight and extracellular water ratio.

[0053] Real-time monitoring of pain intensity, analgesic medication, gastrointestinal symptoms and other symptoms in patients after gastric cancer surgery, and obtaining subjective reports of pain symptoms after gastric cancer surgery;

[0054] Specifically, the pain intensity includes VAS or NRS scores at rest and during activity; analgesics include the dosage of opioids and other analgesics; gastrointestinal symptoms include nausea, vomiting, abdominal distension scores, and the time of first flatus and defecation; other symptoms include fatigue level and dyspnea scores.

[0055] Real-time monitoring of patients' emotional state, confidence in recovery, cognitive function and social support after gastric cancer surgery to obtain the patients' psychosocial psychological state after gastric cancer surgery.

[0056] Specifically, the emotional state includes anxiety and depression scores; recovery confidence includes self-efficacy scores on recovery ability; cognitive function includes postoperative delirium screening scale scores; and social support includes visitor visitation rate and family support.

[0057] Based on the patient's vital signs, physiological signals, activity level, functional indicators, nutritional status, metabolic indicators, subjective reports of pain symptoms, and psychosocial status after gastric cancer surgery, multi-source data on the patient's postoperative gastric cancer surgery are generated.

[0058] It should be noted that by acquiring vital signs, physiological signals, activity levels, metabolic indicators, subjective reports of pain symptoms, and psychosocial status of patients after gastric cancer surgery, comprehensive multi-source data on patients after gastric cancer surgery can be generated. This allows for the comprehensive consideration and analysis of multi-source data, enabling better multi-parameter accelerated recovery assessment of patients after gastric cancer surgery.

[0059] Based on edge computing, preprocessing and fusion analysis of multi-source data after gastric cancer surgery are performed to form fused data after gastric cancer surgery.

[0060] In this embodiment, edge computing is used to preprocess and fuse multi-source data from patients after gastric cancer surgery, and the following operations are performed:

[0061] Data cleaning was performed on multi-source data from patients after gastric cancer surgery. This included establishing a time window to map the multi-source data to the same time axis after surgery, removing noise from the multi-source data, and identifying missing and outlier values ​​in the multi-source data.

[0062] The missing and outlier values ​​in the multi-source data of patients after gastric cancer surgery were evaluated to determine whether the missing and outlier values ​​in the multi-parameter accelerated recovery assessment of patients after gastric cancer surgery were valuable.

[0063] When missing values ​​and outliers in multi-source data after gastric cancer surgery are valuable for the patient's multi-parameter accelerated recovery assessment, missing values ​​are filled and outliers are corrected; otherwise, missing values ​​and outliers are deleted.

[0064] The multi-source data of patients after gastric cancer surgery were standardized. Specifically, the multi-source data of patients after gastric cancer surgery with different dimensions were normalized and mapped to the [0,1] interval to remove the dimensional differences between the multi-source data of patients after gastric cancer surgery, thus forming standardized multi-source data of patients after gastric cancer surgery.

[0065] Feature extraction was performed on multi-source data from patients after gastric cancer surgery. Feature vectors related to multi-parameter accelerated recovery assessment of patients were extracted from the multi-source data after gastric cancer surgery, including temporal physiological features, activity and behavior features, clinical symptom features, nutritional and metabolic features, and psychological state features.

[0066] Feature fusion is performed on the extracted feature vectors. Based on feature-level fusion, cross-modal feature association and weighted fusion are performed on temporal physiological features, activity behavior features, clinical symptom features, nutritional metabolism features and psychological state features to form fused data of patients after gastric cancer surgery.

[0067] A multi-parameter accelerated recovery assessment model is constructed based on artificial intelligence. The fusion data of patients after gastric cancer surgery is analyzed and identified according to the multi-parameter accelerated recovery assessment results of patients after gastric cancer surgery, so as to realize dynamic risk assessment of patients' postoperative gastric cancer recovery.

[0068] In this embodiment, a multi-parameter accelerated recovery assessment model is constructed based on artificial intelligence, and the following operations are performed:

[0069] Based on the multi-parameter accelerated recovery assessment requirements of patients after gastric cancer surgery ERAS management, historical data of multi-parameter accelerated recovery assessment were collected and divided into training set and test set.

[0070] The machine learning model was trained using a training set, enabling it to autonomously learn multi-parameter accelerated recovery assessment behaviors for ERAS management after gastric cancer surgery. The model was then used to automatically assess the multi-parameter accelerated recovery status of patients after gastric cancer surgery, thus determining the multi-parameter accelerated recovery assessment model.

[0071] The multi-parameter accelerated recovery assessment model was tested using a test set to evaluate its generalization performance and determine whether it could achieve the expected effect of automating the assessment of multi-parameter accelerated recovery after gastric cancer surgery.

[0072] When the multi-parameter accelerated recovery assessment model fails to achieve the expected effect of automatically assessing the multi-parameter accelerated recovery status of patients after gastric cancer surgery, the parameters of the multi-parameter accelerated recovery assessment model are adjusted and optimized until the multi-parameter accelerated recovery assessment model can achieve the expected effect of automatically assessing the multi-parameter accelerated recovery status of patients after gastric cancer surgery, and the optimal multi-parameter accelerated recovery assessment model is determined.

[0073] In this embodiment, the fusion data of the patient after gastric cancer surgery is analyzed and identified according to the multi-parameter accelerated recovery assessment model, and the following operations are performed:

[0074] The fusion data of patients after gastric cancer surgery is input into a multi-parameter accelerated recovery assessment model. The fusion data of patients after gastric cancer surgery is analyzed and identified according to the multi-parameter accelerated recovery assessment model, and the multi-parameter accelerated recovery status of patients after gastric cancer surgery is automatically assessed to determine the multi-parameter accelerated recovery assessment results of patients after gastric cancer surgery.

[0075] In this embodiment, the postoperative recovery stage of the patient after gastric cancer surgery is determined based on the results of the multi-parameter accelerated recovery assessment, including the accelerated recovery period, the stable observation period, and the risk blocking period.

[0076] For patients in the accelerated recovery period, a green pass label is marked on them, and they are discharged quickly according to the standard ERAS pathway;

[0077] For patients in the stable observation period, mark them with a yellow attention label, provide routine care, and strengthen monitoring.

[0078] For patients in the risk blocking phase, a red warning label is marked to trigger the warning, and the patient is given enhanced intervention and MDT consultation.

[0079] Specifically, the postoperative recovery stage of patients with gastric cancer was determined based on the results of multi-parameter accelerated recovery assessment. The postoperative recovery stages of patients with gastric cancer are shown in Table 1.

[0080] Table 1: Postoperative recovery stages of patients with gastric cancer

[0081] Rehabilitation phase Label measure Accelerated recovery period Green Pass Accelerated discharge according to the standard ERAS pathway Stable observation period Yellow Attention Provide routine care and enhance patient monitoring. Risk stagnation period Red Alert Triggering an early warning system, the patient underwent intensive intervention and a multidisciplinary team (MDT) consultation.

[0082] Specifically, the expected clinical outcomes of using this multi-parameter accelerated recovery assessment method for ERAS management in patients after gastric cancer surgery are shown in Table 2:

[0083] Table 2: Expected Clinical Results

[0084] index Traditional methods Method of the present invention Improvement range Average length of stay 10.2 days 7.5 days 26.5% Postoperative complications 32% 18% 43.8% Readmission rate 8.5% 4.2% 50.6% Patient satisfaction 78 points 92 points 17.9%

[0085] In summary, this invention acquires multi-source data from patients after gastric cancer surgery via a data acquisition terminal. It preprocesses and fuses this data using edge computing to form fused post-gastric cancer data. A multi-parameter accelerated recovery assessment model is constructed based on artificial intelligence. This model is then used to analyze and identify the fused post-gastric cancer data, determining the multi-parameter accelerated recovery assessment results. This enables dynamic risk assessment of post-gastric cancer rehabilitation, eliminating the lack of quantitative standards for determining whether patients have met the criteria. It comprehensively considers multi-dimensional data such as laboratory indicators, vital signs, pain scores, and activity levels, thereby improving the effectiveness of post-gastric cancer rehabilitation management.

[0086] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0087] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-parameter accelerated recovery assessment method for postoperative ERAS management in gastric cancer, characterized in that, include: After acquiring multi-source data from patients following gastric cancer surgery using a data acquisition terminal, the multi-source data is preprocessed and fused using edge computing to form fused data from patients following gastric cancer surgery. A multi-parameter accelerated recovery assessment model is constructed based on artificial intelligence. The fusion data of patients after gastric cancer surgery is analyzed and identified according to the multi-parameter accelerated recovery assessment results of patients after gastric cancer surgery, so as to realize dynamic risk assessment of patients' postoperative gastric cancer recovery.

2. The multi-parameter accelerated recovery assessment method for postoperative ERAS management of gastric cancer according to claim 1, characterized in that, Postoperative multi-source data of patients with gastric cancer were acquired using a data acquisition terminal, including: Real-time monitoring of heart rate, respiratory rate, blood pressure, blood oxygen saturation, body temperature, electrocardiogram waveform, blood routine, C-reactive protein, procalcitonin, electrolytes and liver and kidney function of patients after gastric cancer surgery, to obtain vital signs and physiological signals of patients after gastric cancer surgery; Real-time monitoring of patients' postoperative ambulation, gait quantification, limb function and sleep quality was conducted to obtain functional indicators of patients' postoperative activity ability after gastric cancer surgery. Real-time monitoring of patients' intake, excretion, metabolic indicators and body composition after gastric cancer surgery to obtain nutritional status and metabolic indicators after gastric cancer surgery. Real-time monitoring of pain intensity, analgesic medication, gastrointestinal symptoms and other symptoms in patients after gastric cancer surgery, and obtaining subjective reports of pain symptoms after gastric cancer surgery; Real-time monitoring of patients' emotional state, confidence in recovery, cognitive function and social support after gastric cancer surgery to obtain the patients' psychosocial psychological state after gastric cancer surgery. Based on the patient's vital signs, physiological signals, activity level, functional indicators, nutritional status, metabolic indicators, subjective reports of pain symptoms, and psychosocial status after gastric cancer surgery, multi-source data on the patient's postoperative gastric cancer surgery are generated.

3. The multi-parameter accelerated recovery assessment method for postoperative ERAS management of gastric cancer according to claim 2, characterized in that, The out-of-bed activities include duration, frequency, sitting, standing, and walking states; the walking quantification includes steps, distance, speed, and gait cycle; limb function includes grip strength and headboard lift angle; sleep quality includes total duration, number of awakenings, and deep sleep percentage; The intake includes oral water intake, liquid or semi-liquid intake; the output includes gastrointestinal decompression, drainage fluid properties, drainage fluid volume, and urine volume; metabolic indicators include blood glucose, prealbumin, and transferrin; body composition includes body weight and extracellular water ratio. The pain intensity includes VAS or NRS scores at rest and during activity; analgesics include the dosage of opioids and other analgesics; gastrointestinal symptoms include nausea, vomiting, abdominal distension scores, and the time of first flatus and defecation; other symptoms include fatigue level and dyspnea scores. The emotional state includes anxiety and depression scores; recovery confidence includes self-efficacy scores on recovery ability; cognitive function includes postoperative delirium screening scale scores; and social support includes visitor visitation rate and family support.

4. The multi-parameter accelerated recovery assessment method for postoperative ERAS management of gastric cancer according to claim 3, characterized in that, Preprocessing of multi-source data from patients after gastric cancer surgery based on edge computing, performing the following operations: Data cleaning was performed on multi-source data from patients after gastric cancer surgery. This included establishing a time window to map the multi-source data to the same time axis after surgery, removing noise from the multi-source data, and identifying missing and outlier values ​​in the multi-source data. The missing and outlier values ​​in the multi-source data of patients after gastric cancer surgery were evaluated to determine whether the missing and outlier values ​​in the multi-parameter accelerated recovery assessment of patients after gastric cancer surgery were valuable. When missing values ​​and outliers in multi-source data after gastric cancer surgery are valuable for the patient's multi-parameter accelerated recovery assessment, missing values ​​are filled and outliers are corrected; otherwise, missing values ​​and outliers are deleted. The multi-source data of patients after gastric cancer surgery were standardized. Specifically, the multi-source data of patients after gastric cancer surgery with different dimensions were normalized and mapped to the [0,1] interval to remove the dimensional differences between the multi-source data of patients after gastric cancer surgery, thus forming standardized multi-source data of patients after gastric cancer surgery.

5. A multi-parameter accelerated recovery assessment method for postoperative ERAS management of gastric cancer according to claim 4, characterized in that, Based on edge computing, multi-source data from patients after gastric cancer surgery are fused and analyzed to perform the following operations: Feature extraction was performed on multi-source data from patients after gastric cancer surgery. Feature vectors related to multi-parameter accelerated recovery assessment of patients were extracted from the multi-source data after gastric cancer surgery, including temporal physiological features, activity and behavior features, clinical symptom features, nutritional and metabolic features, and psychological state features. Feature fusion is performed on the extracted feature vectors. Based on feature-level fusion, cross-modal feature association and weighted fusion are performed on temporal physiological features, activity behavior features, clinical symptom features, nutritional metabolism features and psychological state features to form fused data of patients after gastric cancer surgery.

6. A multi-parameter accelerated recovery assessment method for postoperative ERAS management of gastric cancer according to claim 5, characterized in that, Based on artificial intelligence, a multi-parameter accelerated recovery assessment model is constructed, and the following operations are performed: Based on the multi-parameter accelerated recovery assessment requirements of patients after gastric cancer surgery ERAS management, historical data of multi-parameter accelerated recovery assessment were collected and divided into training set and test set. The machine learning model was trained using a training set, enabling it to autonomously learn multi-parameter accelerated recovery assessment behaviors for ERAS management after gastric cancer surgery. The model was then used to automatically assess the multi-parameter accelerated recovery status of patients after gastric cancer surgery, thus determining the multi-parameter accelerated recovery assessment model. The multi-parameter accelerated recovery assessment model was tested using a test set to evaluate its generalization performance and determine whether it could achieve the expected effect of automating the assessment of multi-parameter accelerated recovery after gastric cancer surgery. When the multi-parameter accelerated recovery assessment model fails to achieve the expected effect of automatically assessing the multi-parameter accelerated recovery status of patients after gastric cancer surgery, the parameters of the multi-parameter accelerated recovery assessment model are adjusted and optimized until the multi-parameter accelerated recovery assessment model can achieve the expected effect of automatically assessing the multi-parameter accelerated recovery status of patients after gastric cancer surgery, and the optimal multi-parameter accelerated recovery assessment model is determined.

7. A multi-parameter accelerated recovery assessment method for postoperative ERAS management of gastric cancer according to claim 6, characterized in that, Based on the multi-parameter accelerated recovery assessment model, the fusion data of patients after gastric cancer surgery were analyzed and identified, and the following operations were performed: The fusion data of patients after gastric cancer surgery is input into a multi-parameter accelerated recovery assessment model. The fusion data of patients after gastric cancer surgery is analyzed and identified according to the multi-parameter accelerated recovery assessment model, and the multi-parameter accelerated recovery status of patients after gastric cancer surgery is automatically assessed to determine the multi-parameter accelerated recovery assessment results of patients after gastric cancer surgery.

8. A multi-parameter accelerated recovery assessment method for postoperative ERAS management of gastric cancer according to claim 7, characterized in that, The recovery stages after gastric cancer surgery are determined based on the results of multi-parameter accelerated recovery assessment, including the accelerated recovery period, the stable observation period, and the risk blocking period. For patients in the accelerated recovery period, a green pass label is marked on them, and they are discharged quickly according to the standard ERAS pathway; For patients in the stable observation period, mark them with a yellow attention label, provide routine care, and strengthen monitoring. For patients in the risk blocking phase, a red warning label is marked to trigger the warning, and the patient is given enhanced intervention and MDT consultation.

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