A system and method for monitoring enteral nutrition in critically ill patients

Abstract

This invention relates to the field of medical monitoring technology, and discloses a system and method for monitoring enteral nutrition in critically ill patients. The method includes: acquiring dynamic images of the gastric antrum region of critically ill patients, identifying the contraction frequency and amplitude of the gastric antrum to calculate the gastric emptying index; calculating the intestinal transport efficiency of critically ill patients using intestinal peristalsis data and the gastric emptying index; constructing an enteral nutrition delivery protocol for critically ill patients, and collecting transport conversion data of the critically ill patients under the nutrition delivery protocol; analyzing the enteral nutritional tolerance of critically ill patients, collecting metabolic data and immune status of critically ill patients after the nutrition delivery protocol, and constructing a target nutrition delivery protocol; and monitoring the enteral nutritional status of critically ill patients in real time under the target nutrition delivery protocol to obtain an enteral nutrition monitoring report. This invention can improve the comprehensiveness of enteral nutrition monitoring for critically ill patients, thereby enabling the construction of optimized nutritional combinations.

Description

Technical Field

[0001] This invention relates to the field of medical monitoring, and more particularly to a system and method for monitoring enteral nutrition in critically ill patients. Background Technology

[0002] Critically ill patients are those whose physiological functions deteriorate rapidly due to severe illness, trauma, organ failure, or other life-threatening conditions, requiring immediate close monitoring and emergency medical intervention. These patients are typically unable to eat or digest food actively and require instruments to assist in intestinal nutrient absorption, providing them with essential energy and nutrients to enhance their immunity and improve recovery. Therefore, intestinal nutrition monitoring and scientific nutritional support are crucial for critically ill patients.

[0003] Currently, enteral nutrition monitoring for critically ill patients mostly relies on traditional observation methods. Medical staff assess patients' nutrient absorption by regularly measuring their weight, testing biochemical indicators, and monitoring their digestive status. However, this approach does not consider the patient's internal condition, such as changes in the gastric antrum and the flow of the intestinal wall. This often results in inaccurate enteral nutrition monitoring results, making it impossible to tailor the optimal enteral nutrition supplementation plan. Summary of the Invention

[0004] This invention provides a system and method for monitoring intestinal nutrition in critically ill patients. Its main purpose is to improve the comprehensiveness of intestinal nutrition monitoring in critically ill patients, and then to construct an optimal nutritional combination plan.

[0005] To achieve the above objectives, the present invention provides an enteral nutrition monitoring system for critically ill patients, comprising: a gastric antrum region observation module, an intestinal transit analysis module, a nutrient transport module, an optimized transport module, and a nutrient monitoring module;

[0006] The gastric antrum region observation module is used to acquire dynamic images of the gastric antrum region of critically ill patients, identify the contraction and relaxation cycles of the gastric antrum region using the dynamic images, identify the contraction frequency and amplitude of the gastric antrum of critically ill patients based on the contraction and relaxation cycles, and calculate the gastric emptying index of critically ill patients based on the contraction frequency and amplitude.

[0007] The intestinal transport analysis module is used to identify the changes in intestinal wall thickness and the flow trajectory of intestinal contents in the critically ill patients, obtain intestinal peristalsis data, and calculate the intestinal transport efficiency of the critically ill patients using the intestinal peristalsis data and the gastric emptying index.

[0008] The nutrient delivery module is used to query the intestinal metabolic data of the critically ill patient, construct an intestinal nutrient delivery plan for the critically ill patient using the intestinal metabolic data and the intestinal transport efficiency, and collect the transport and conversion data of the critically ill patient under the nutrient delivery plan.

[0009] The optimized transmission module is used to analyze the enteral nutrition tolerance of the critically ill patient based on the transmission conversion data, collect the metabolic data and immune status of the critically ill patient after the nutrition delivery program, and construct a personalized enteral nutrition delivery program for the critically ill patient based on the enteral nutrition tolerance, the metabolic data and the immune status to obtain the target nutrition delivery program.

[0010] The nutrition monitoring module is used to monitor the intestinal nutrition status of critically ill patients under the target nutrition delivery program in real time and obtain an intestinal nutrition monitoring report.

[0011] Optionally, the dynamic image is used to identify the contraction and relaxation cycles of the gastric antrum region, including:

[0012] The dynamic image is subjected to frame segmentation processing to obtain a framed image;

[0013] Identify the image features of the framed image;

[0014] Using the image features, a motion trajectory curve of the gastric antrum region is constructed;

[0015] The contraction and relaxation cycles of the gastric antrum region are identified using the aforementioned motion trajectory curve.

[0016] Optionally, based on the contraction cycle and the diastolic cycle, the contraction frequency and amplitude of the gastric antrum in critically ill patients are identified, including:

[0017] The contraction and relaxation cycles are time-series labeled to obtain a periodic motion sequence;

[0018] Using the aforementioned periodic motion sequence, the number of unit contractions of the gastric antrum in the critically ill patient was identified;

[0019] Based on the number of unit contractions, the contraction frequency of the gastric antrum in the critically ill patient is determined;

[0020] Using the systolic and diastolic cycles, the range of changes in the antral wall thickness of the gastric antrum in the critically ill patient was identified;

[0021] Extreme values ​​were calculated for the range of gastric antral wall thickness variation to obtain the maximum thickness difference during systole and the minimum thickness difference during diastole.

[0022] The amplitude of the gastric antrum in the critically ill patient is determined based on the difference between the maximum thickness during systole and the minimum thickness during diastole.

[0023] Optionally, the gastric emptying index of the critically ill patient is calculated based on the contraction frequency and the amplitude, including:

[0024] The contraction frequency and amplitude are standardized to obtain normalized gastric motility parameters;

[0025] The normalized gastric motility parameters were corrected for drug effects to obtain the corrected gastric motility parameters.

[0026] Using the corrected gastric motility parameters, a nonlinear contribution function of gastric emptying rate in critically ill patients was constructed.

[0027] The gastric emptying index of the critically ill patient is calculated using the nonlinear contribution function.

[0028] Optionally, the step of constructing a nonlinear contribution function for the gastric emptying rate of critically ill patients using the corrected gastric motility parameters includes:

[0029] Query the corrected antral contraction frequency and corrected antral contraction amplitude in the corrected gastric motility parameters;

[0030] Using the corrected antral contraction frequency and the corrected antral contraction amplitude, a nonlinear contribution function for the gastric emptying rate of critically ill patients is constructed in conjunction with the following formula:

[0031]

[0032] in, Let represent the nonlinear contribution function, and k represent the calibration coefficient of the nonlinear contribution function. This indicates the correction of the frequency of antral contractions. Represents the frequency weighting coefficient. This indicates correction of the amplitude of gastric antral contractions. This represents the amplitude weighting coefficient. This represents the synergistic effect coefficient.

[0033] Optionally, the intestinal transit efficiency of the critically ill patient is calculated using the intestinal peristalsis data and the gastric emptying index, including:

[0034] Based on the intestinal peristalsis data, the intestinal peristalsis frequency, average flow rate, and peristalsis coordination of the critically ill patients were identified;

[0035] Based on the intestinal peristalsis frequency, the average flow rate, and the peristalsis coordination, the intestinal transport efficiency of the critically ill patient is calculated using the following formula:

[0036]

[0037] in, Indicates intestinal transport efficiency. Indicates the gastric emptying index, denoted by , c represents the intestinal peristalsis frequency, E represents the average flow rate, E represents the calibration coefficient for transport efficiency, and t represents peristaltic coordination.

[0038] Optionally, using the intestinal metabolic data and the intestinal transport efficiency, an intestinal nutrition delivery protocol for the critically ill patient is constructed, including:

[0039] The intestinal metabolic data and the intestinal transport efficiency are dynamically weighted and fused to obtain a weighted comprehensive evaluation value.

[0040] Based on the weighted comprehensive assessment value, the critically ill patients are classified into clinical status categories to obtain their nutritional risk levels;

[0041] The patient's nutritional risk level was matched with a protocol template to obtain a preliminary nutrition delivery protocol;

[0042] The preliminary nutrient delivery scheme is subjected to safety constraint verification, and when the safety constraint verification result is safe, the enteral nutrient delivery scheme is obtained.

[0043] Optionally, based on the transmission and conversion data, the enteral nutrition tolerance of the critically ill patients is analyzed, including:

[0044] Based on the transmission and conversion data, the tolerance of the nutritional formula components, infusion rate, volume overload, and adverse reactions of the critically ill patients were analyzed to obtain multidimensional tolerance data.

[0045] The multidimensional tolerance data are subjected to index quantification to obtain tolerance index;

[0046] Based on the tolerance index, the enteral nutrition tolerance of the critically ill patients was determined.

[0047] Optionally, the enteral nutritional status of critically ill patients under the target nutrient delivery protocol is monitored in real time to obtain an enteral nutritional monitoring report, including:

[0048] Feature extraction is performed on the execution parameters of the target nutrient delivery scheme to obtain target feature parameters;

[0049] Synchronous multimodal data of the target feature parameters are collected to obtain time-aligned monitoring data;

[0050] Identify the patient's feedback status under the time-aligned monitoring data to obtain feedback data;

[0051] Using the target feature parameters, the time-aligned monitoring data, and the feedback data, a gut nutrition monitoring report for the critically ill patient is constructed.

[0052] A method for monitoring enteral nutrition in critically ill patients, characterized in that the method includes:

[0053] Dynamic images of the gastric antrum region of critically ill patients are acquired. The contraction and relaxation cycles of the gastric antrum region are identified using the dynamic images. Based on the contraction and relaxation cycles, the contraction frequency and amplitude of the gastric antrum of critically ill patients are identified. Based on the contraction frequency and amplitude, the gastric emptying index of the critically ill patients is calculated.

[0054] The intestinal wall thickness variation and intestinal contents flow trajectory of the critically ill patients are identified to obtain intestinal peristalsis data. The intestinal peristalsis data and the gastric emptying index are used to calculate the intestinal transport efficiency of the critically ill patients.

[0055] The intestinal metabolic data of the critically ill patients were queried, and an intestinal nutrient delivery plan for the critically ill patients was constructed using the intestinal metabolic data and the intestinal transport efficiency. Transport and conversion data of the critically ill patients under the nutrient delivery plan were collected.

[0056] Based on the transmitted and converted data, the enteral nutrition tolerance of the critically ill patients is analyzed, and the metabolic data and immune status of the critically ill patients after the nutrition delivery program are collected. Based on the enteral nutrition tolerance, the metabolic data and the immune status, a personalized enteral nutrition delivery program for the critically ill patients is constructed to obtain the target nutrition delivery program.

[0057] Real-time monitoring of the enteral nutritional status of critically ill patients under the target nutrient delivery protocol yields an enteral nutrition monitoring report.

[0058] Compared to existing technologies, this invention first uses CT, endoscopy, or ultrasound to capture real-time images of muscle contraction / relaxation in the gastric antrum region, obtaining dynamic sequences including changes in gastric wall thickness and lumen morphology, thus visualizing gastric antrum motility and providing an intuitive data foundation for gastric motility assessment. Then, it dynamically tracks the gastric antrum wall movement trajectory, accurately dividing the contraction and relaxation cycles, and automatically identifying abnormal cycles to achieve early warning of gastric motility disorders. Furthermore, it abstracts gastric motility into two core indicators: contraction frequency and amplitude, obtaining a quantitative index for comprehensive assessment of gastric emptying capacity. This invention also measures the dynamic changes in intestinal wall thickness and the flow trajectory of contents to obtain peristaltic frequency, average flow velocity, and peristaltic coordination characteristics. By combining the gastric emptying index with intestinal peristalsis data, it forms a comprehensive assessment of gastric emptying capacity from stomach to intestine. This invention provides a continuous dynamic assessment system. By utilizing the Kalman filter algorithm to dynamically adjust metabolic data and transport efficiency, it generates a weighted comprehensive assessment value, integrating physiological function and metabolic status to achieve stratified patient management and match differentiated nutritional strategies. The invention assesses formulation components, infusion rate, volume overload tolerance, and adverse reactions through metabolic indicators (blood glucose, electrolytes), gastrointestinal symptoms (abdominal distension, diarrhea), and imaging data (gastric residual volume). It identifies individual-specific responses to nutritional preparations, achieving a systemic assessment of the nutritional regimen's effect from a systemic perspective. For example, for sepsis patients, it focuses on immune indicators (weight 0.4) to guide the application of immune-enhancing formulations. The invention provides visualized, structured, real-time monitoring results by establishing precise data over a time dimension. Therefore, this invention can improve the comprehensiveness of enteral nutrition monitoring for critically ill patients, thereby constructing optimal nutritional regimens. Attached Figure Description

[0059] Figure 1 A functional block diagram of an enteral nutrition monitoring system for critically ill patients provided in an embodiment of the present invention;

[0060] Figure 2 This is a flowchart illustrating a method for monitoring enteral nutrition in critically ill patients according to an embodiment of the present invention.

[0061] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0063] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0064] In practice, the server-side equipment deployed in the enteral nutrition monitoring system for critically ill patients may consist of one or more devices. This system can be implemented as a business instance, a virtual machine, or hardware devices. For example, it can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, this system can be understood as software deployed on a cloud node, providing enteral nutrition monitoring services for critically ill patients to various users. Alternatively, it can be implemented as a virtual machine deployed on one or more devices in a cloud node, with application software installed to manage various users. Or, it can also be implemented as a server composed of numerous identical or different types of hardware devices, with one or more devices configured to provide enteral nutrition monitoring services for critically ill patients to various users.

[0065] In terms of implementation, the enteral nutrition monitoring system for critically ill patients and the user terminal are mutually compatible. That is, if the enteral nutrition monitoring system for critically ill patients is implemented as an application installed on a cloud service platform, then the user terminal is implemented as a client that establishes a communication connection with the application; or if the enteral nutrition monitoring system for critically ill patients is implemented as a website, then the user terminal is implemented as a webpage; or if the enteral nutrition monitoring system for critically ill patients is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0066] Reference Figure 1 The diagram shown is a functional block diagram of an intestinal nutrition monitoring system for critically ill patients provided in an embodiment of the present invention.

[0067] The enteral nutrition monitoring system 100 for critically ill patients described in this invention can be set up in a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed in the cloud (e.g., a server for enteral nutrition monitoring of critically ill patients, a server cluster, etc.), or it can be developed as a website. Depending on the functions implemented, the enteral nutrition monitoring system 100 for critically ill patients includes a gastric antrum region observation module 101, an intestinal transit analysis module 102, a nutrient transport module 103, an optimized transport module 104, and a nutrient monitoring module 105.

[0068] In this embodiment of the invention, in the tracking of enteral nutrition monitoring for critically ill patients, each of the above modules can be implemented independently and called upon other modules. Here, "called upon" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the enteral nutrition monitoring system for critically ill patients provided by this embodiment of the invention, the applicable scope of the enteral nutrition monitoring architecture for critically ill patients can be adjusted by adding modules and directly calling them without modifying the program code, achieving cluster-based horizontal expansion to quickly and flexibly expand the enteral nutrition monitoring system for critically ill patients. In practical applications, the above modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0069] The following describes the components and specific workflow of the enteral nutrition monitoring system for critically ill patients, using specific embodiments as examples.

[0070] The gastric antrum region observation module 101 is used to acquire dynamic images of the gastric antrum region of critically ill patients, identify the contraction and relaxation cycles of the gastric antrum region using the dynamic images, identify the contraction frequency and amplitude of the gastric antrum of critically ill patients based on the contraction and relaxation cycles, and calculate the gastric emptying index of critically ill patients based on the contraction frequency and amplitude.

[0071] This invention provides a method to acquire real-time motion images of the gastric antrum region by collecting dynamic images of the gastric antrum region in critically ill patients. This helps users analyze the patient's gastric activity and, consequently, aids in the analysis of intestinal nutrient absorption.

[0072] The critically ill patients refer to those whose physiological functions are in a critical state due to serious illness, trauma or organ failure and who need to receive life support treatment in the intensive care unit. The dynamic images refer to real-time motion images of the gastric antrum region obtained through medical imaging technology.

[0073] Optionally, the dynamic images can be obtained by capturing the dynamic process of contraction and relaxation of the gastric antrum muscles in the patient's gastric antrum region using imaging techniques such as CT or endoscopy.

[0074] This invention, by utilizing the dynamic images to identify the contraction and relaxation cycles of the gastric antrum region, can help medical staff understand the patient's gastric motility and digestive capacity, thereby enabling them to analyze the patient's digestive and absorptive abilities.

[0075] The contraction cycle refers to the complete physiological process of the gastric antrum region from the start of contraction to the end of contraction and return to the initial state. In clinical practice, the normal contraction cycle duration is 3-5 seconds. If the cycle is prolonged (>6 seconds) or shortened (<2 seconds), it indicates abnormal gastric motility (such as gastroparesis, pyloric obstruction), which is prone to chyme retention or reflux. The diastolic cycle refers to the resting process of the gastric antrum region from the end of contraction to the start of the next contraction. In clinical practice, the normal diastolic cycle duration is 15-20 seconds. If diastole is incomplete (wall thickness >6 mm) or the diastolic period is shortened (<10 seconds), it may lead to delayed gastric emptying (such as diabetic gastroparesis).

[0076] As an embodiment of the present invention, the method of identifying the contraction and relaxation cycles of the gastric antrum region using the dynamic image includes:

[0077] The dynamic image is subjected to frame segmentation processing to obtain a framed image;

[0078] Identify the image features of the framed image;

[0079] Using the image features, a motion trajectory curve of the gastric antrum region is constructed;

[0080] The contraction and relaxation cycles of the gastric antrum region are identified using the aforementioned motion trajectory curve.

[0081] The frame-by-frame image refers to an image sequence in which a dynamic image is split into individual static images in chronological order, and the motion trajectory curve refers to a time-space coordinate curve that reflects the changing characteristics.

[0082] Optionally, the frame-by-frame image can be obtained by splitting the dynamic image at a rate of 5-10 frames per second using the VideoCapture function of the OpenCV library. The image features can be obtained by semantic segmentation of the frame-by-frame image using a deep learning model, and by extracting key features such as the edge, thickness, and luminal morphology of the gastric antrum wall from a clinical image dataset with labeled gastric antrum boundaries. The motion trajectory curve can be obtained by calculating the pixel displacement between adjacent frame-by-frame images corresponding to the image features based on optical flow, generating a spatial coordinate curve that changes over time. The identification of the contraction and relaxation cycles of the gastric antrum region using the motion trajectory curve can be achieved by performing wavelet transform decomposition on the motion trajectory curve, extracting feature signals in the frequency band of 3-5 times / minute, and setting a threshold for the slope of the gastric antrum wall thickness change (e.g., according to clinical experience data, the slope during contraction is >0.2 mm / s, and the slope during relaxation is ∈ [-0.1, 0.1] mm / s) to automatically divide the start and end time points of the contraction and relaxation states.

[0083] The embodiments of the present invention can provide a clearer understanding of the gastric motility status of critically ill patients by identifying the contraction frequency and amplitude of the gastric antrum based on the contraction cycle and the diastolic cycle.

[0084] The contraction frequency refers to the number of effective contractions of the gastric antrum per unit time (usually per minute), and the amplitude refers to the maximum change in gastric wall thickness during gastric antrum contraction. These parameters reflect the strength and effectiveness of smooth muscle contraction and are key mechanical parameters for assessing gastric motility.

[0085] As an embodiment of the present invention, based on the contraction cycle and the diastolic cycle, identifying the contraction frequency and amplitude of the gastric antrum in critically ill patients includes:

[0086] The contraction and relaxation cycles are time-series labeled to obtain a periodic motion sequence;

[0087] Using the aforementioned periodic motion sequence, the number of unit contractions of the gastric antrum in the critically ill patient was identified;

[0088] Based on the number of unit contractions, the contraction frequency of the gastric antrum in the critically ill patient is determined;

[0089] Using the systolic and diastolic cycles, the range of changes in the antral wall thickness of the gastric antrum in the critically ill patient was identified;

[0090] Extreme values ​​were calculated for the range of gastric antral wall thickness variation to obtain the maximum thickness difference during systole and the minimum thickness difference during diastole.

[0091] The amplitude of the gastric antrum in the critically ill patient is determined based on the difference between the maximum thickness during systole and the minimum thickness during diastole.

[0092] The periodic motion sequence refers to a state sequence generated by timestamps based on the temporal order of the gastric antrum contraction and relaxation cycles, used to quantify the periodicity of gastric antrum motion. The gastric antrum wall thickness variation range refers to the dynamic range of gastric antrum wall thickness within each contraction and relaxation cycle, i.e., from the minimum to the maximum thickness from the beginning to the end of the cycle. The maximum thickness difference during contraction refers to the difference between the maximum thickness of the gastric antrum wall at the end of contraction within a single contraction cycle and the minimum thickness at the beginning of the cycle (i.e., the previous end of relaxation).

[0093] In the specific implementation process, the state sequence of the gastric antral wall thickness change curve can be labeled using a Hidden Markov Model (HMM). The start and end points of each contraction cycle (S) and diastolic cycle (D) are marked with timestamps, generating a periodic motion sequence such as "S1-D1-S2-D2-…". The number of occurrences of the "S" state within a unit time (e.g., 1 minute) in the periodic motion sequence is counted to obtain the unit contraction count. The unit contraction count is converted to contractions per minute (times / minute). For example, if 12 contractions are detected within 5 minutes, the contraction frequency is 2.4 times / minute. The maximum gastric antral wall thickness (Tmax) is extracted within each contraction cycle, and the minimum thickness (Tmin) is extracted within the diastolic cycle, forming the thickness interval corresponding to each cycle. The difference between Tmax and Tmin in each cycle (ΔT_contraction) and the difference between Tmin and Tmax in the previous cycle (ΔT_diastolic) are calculated, and the ΔT_contraction value is taken from all cycles. The average value of the contraction is used as the maximum thickness difference during the contraction period, and the average value of the ΔT diastole is used as the minimum thickness difference during the diastole period. The maximum thickness difference during the contraction period (unit: mm) is used as the amplitude of the gastric antrum contraction. For example, if the average ΔT contraction is 2.5 mm, then the amplitude is 2.5 mm.

[0094] This invention provides a quantitative indicator for comprehensively assessing gastric emptying capacity by calculating the gastric emptying index of critically ill patients based on the contraction frequency and the amplitude, thereby providing preliminary assistance to users in understanding the patient's intestinal absorption capacity for food.

[0095] The gastric emptying index refers to the proportion of residual food or liquid expelled from the stomach per unit time.

[0096] As an embodiment of the present invention, calculating the gastric emptying index of the critically ill patient based on the contraction frequency and the amplitude includes:

[0097] The contraction frequency and amplitude are standardized to obtain normalized gastric motility parameters;

[0098] The normalized gastric motility parameters were corrected for drug effects to obtain the corrected gastric motility parameters.

[0099] Using the corrected gastric motility parameters, a nonlinear contribution function of gastric emptying rate in critically ill patients was constructed.

[0100] The gastric emptying index of the critically ill patient is calculated using the nonlinear contribution function.

[0101] The normalized gastric motility parameters refer to dimensionless parameters obtained by standardizing the original gastric motility indicators (such as contraction frequency and amplitude) to eliminate dimensions and individual differences. The corrected gastric motility parameters refer to parameters that truly reflect the patient's own gastric function after deducting the interference of drugs on gastric motility based on the normalized parameters. The nonlinear contribution function refers to a mathematical model that describes the nonlinear relationship between the corrected gastric motility parameters (contraction frequency and amplitude) and the gastric emptying rate.

[0102] Optionally, the normalized gastric motility parameters can be obtained by converting the contraction frequency and amplitude into dimensionless parameters using the Z-score normalization formula, and the corrected gastric motility parameters can be obtained by calibrating the normalized gastric motility parameters within their physiological range and correcting for drug interference (based on...). Expert consensus on the impact of prokinetic drugs on critically ill patients The gastric emptying index can be obtained by substituting the corrected parameters into a function to calculate the percentage of gastric emptying at a specific time point (such as 60 minutes after a meal).

[0103] Preferably, the step of constructing a nonlinear contribution function for the gastric emptying rate of critically ill patients using the corrected gastric motility parameters includes:

[0104] Query the corrected antral contraction frequency and corrected antral contraction amplitude in the corrected gastric motility parameters;

[0105] Using the corrected antral contraction frequency and the corrected antral contraction amplitude, a nonlinear contribution function for the gastric emptying rate of critically ill patients is constructed in conjunction with the following formula:

[0106]

[0107] in, Let represent the nonlinear contribution function, and k represent the calibration coefficient of the nonlinear contribution function. This indicates the correction of the frequency of antral contractions. Represents the frequency weighting coefficient. This indicates correction of the amplitude of gastric antral contractions. This represents the amplitude weighting coefficient. This represents the synergistic effect coefficient.

[0108] It should be further explained that the nonlinear contribution function adopts a multiplicative integral solution model, decomposing gastric emptying efficiency into three independent but synergistic factors (frequency contribution term, amplitude contribution term, and synergistic effect term). Among them, the contribution of contraction frequency to emptying is not linear, and there is an optimal range (normal 2-3 times / minute). Excessively high frequency may lead to uncoordinated peristalsis, which may reduce emptying efficiency. Amplitude reflects the contraction intensity and directly affects the chyme pushing force, but excessive amplitude may lead to antral spasm, which may hinder emptying. Frequency and amplitude need to be matched synergistically (e.g., high frequency with appropriate amplitude) to achieve optimal emptying. Validation in ICU patients showed that the correlation between this model and gastric emptying rate measured by radionuclide imaging was 0.82 (p<0.001), which was significantly better than the traditional linear model (r=0.65), thus predicting the risk of gastroparesis 24 hours in advance (AUC=0.87) and guiding the timing of enteral nutrition adjustment.

[0109] The intestinal transport analysis module 102 is used to identify the changes in intestinal wall thickness and the flow trajectory of intestinal contents in the critically ill patient, obtain intestinal peristalsis data, and calculate the intestinal transport efficiency of the critically ill patient using the intestinal peristalsis data and the gastric emptying index.

[0110] This invention provides key parameters for analyzing the intestinal motility status of critically ill patients by identifying changes in intestinal wall thickness and the flow trajectory of intestinal contents.

[0111] The intestinal wall thickness change value refers to the dynamic change in the thickness of the intestinal smooth muscle layer during peristalsis, reflecting the intensity and functional state of intestinal wall contraction. The intestinal contents flow trajectory refers to the movement path and speed of chyme, liquid or gas in the intestine.

[0112] Optionally, the intestinal peristalsis data can be obtained by observing and measuring the dynamic changes in intestinal wall thickness and the movement path and speed of contents through imaging technologies such as ultrasound and CT.

[0113] Furthermore, by utilizing the intestinal peristalsis data and the gastric emptying index, this embodiment of the invention can calculate the intestinal transport efficiency of critically ill patients, thereby quantitatively assessing the overall intestinal transport function of critically ill patients and providing a precise basis for adjusting enteral nutrition programs and intervening in gastrointestinal motility disorders.

[0114] The intestinal transport efficiency refers to the overall transport efficiency and functional status of the intestines in critically ill patients, reflecting the continuous dynamic level from gastric emptying to intestinal propulsion.

[0115] As an embodiment of the present invention, the step of calculating the intestinal transit efficiency of the critically ill patient using the intestinal peristalsis data and the gastric emptying index includes:

[0116] Based on the intestinal peristalsis data, the intestinal peristalsis frequency, average flow rate, and peristalsis coordination of the critically ill patients were identified;

[0117] Based on the intestinal peristalsis frequency, the average flow rate, and the peristalsis coordination, the intestinal transport efficiency of the critically ill patient is calculated using the following formula:

[0118]

[0119] in, Indicates intestinal transport efficiency. Indicates the gastric emptying index, denoted by , c represents the intestinal peristalsis frequency, E represents the average flow rate, E represents the calibration coefficient for transport efficiency, and t represents peristaltic coordination.

[0120] Among them, the intestine This refers to the number of effective peristaltic waves per unit time. It refers to the average flow rate of gas or liquid in the intestine, and the peristaltic coordination refers to the time difference of peristalsis between adjacent intestinal segments.

[0121] It should be further explained that the intestinal transport efficiency formula integrates three core parameters—intestinal peristalsis frequency, average contents flow rate, and peristaltic coordination—to construct a mathematical model that quantitatively assesses the intestinal transport efficiency of critically ill patients. This transforms a complex physiological process into a single numerical value, enabling early identification of abnormal transport efficiency (such as...). (Insufficient power indicated by a drop below the threshold) Dynamic monitoring Changes can reflect the effectiveness of treatment (such as medication, rehabilitation training, etc.). (Recovery) provides objective evidence for the management of intestinal function in critically ill patients and can also be used to quantitatively evaluate the effectiveness of interventions in clinical research.

[0122] The nutrient delivery module 103 is used to query the intestinal metabolic data of the critically ill patient, construct an intestinal nutrient delivery plan for the critically ill patient using the intestinal metabolic data and the intestinal transport efficiency, and collect the transport and conversion data of the critically ill patient under the nutrient delivery plan.

[0123] In this embodiment of the invention, querying the intestinal metabolic data of critically ill patients can be used to assess the intestinal energy metabolism status, mucosal functional integrity, and degree of inflammatory response, providing a metabolic basis for determining the mechanism of intestinal function impairment, formulating nutritional support strategies, and prognostic assessment.

[0124] The intestinal metabolic data refers to quantitative information reflecting the intestinal energy metabolism status, mucosal cell function integrity, and degree of inflammatory stress by detecting indicators related to substance metabolism (such as mucosal oxygen consumption, short-chain fatty acid concentration, lactate level, diamine oxidase activity, glucose absorption rate, etc.) in the intestinal tissue and contents of critically ill patients.

[0125] Optionally, the intestinal metabolic data can be obtained by detecting indicators such as the patient's intestinal mucosal oxygen consumption, short-chain fatty acid concentration, and lactic acid level.

[0126] Furthermore, by utilizing the intestinal metabolic data and intestinal transport efficiency, this embodiment of the invention can construct an intestinal nutrition delivery plan for critically ill patients, which can accurately determine the infusion rate, formula composition, and intervention timing of personalized enteral nutrition, so as to optimize nutrient absorption and improve the prognosis of critically ill patients.

[0127] The enteral nutrition delivery protocol refers to a personalized nutrition delivery protocol developed for critically ill patients, including nutrient composition, nutrient concentration, and feeding time.

[0128] As an embodiment of the present invention, the step of constructing an enteral nutrition delivery protocol for critically ill patients using the intestinal metabolic data and the intestinal transport efficiency includes:

[0129] The intestinal metabolic data and the intestinal transport efficiency are dynamically weighted and fused to obtain a weighted comprehensive evaluation value.

[0130] Based on the weighted comprehensive assessment value, the critically ill patients are classified into clinical status categories to obtain their nutritional risk levels;

[0131] The patient's nutritional risk level was matched with a protocol template to obtain a preliminary nutrition delivery protocol;

[0132] The preliminary nutrient delivery scheme is subjected to safety constraint verification, and when the safety constraint verification result is safe, the enteral nutrient delivery scheme is obtained.

[0133] The weighted comprehensive assessment value refers to a quantitative indicator obtained by weighting and summing the intestinal metabolic data and intestinal transport efficiency after assigning real-time weights, which is used to comprehensively reflect the intestinal function status of critically ill patients. The patient nutritional risk level refers to the risk level classification of patients based on the comparison results of the weighted comprehensive assessment value and the clinical threshold.

[0134] In practice, an adaptive weighted algorithm (such as Kalman filtering) can be used to dynamically adjust the weight coefficients of intestinal metabolic data (such as mucosal pH and oxygen uptake rate) and intestinal transport efficiency to generate a comprehensive evaluation value (e.g., when transport efficiency is <40%, the weight of metabolic indicators is automatically increased to 0.7). Based on the comprehensive evaluation value and preset thresholds (such as metabolic abnormality threshold + transport efficiency threshold), fuzzy clustering analysis is performed to classify patients into stable types (…). ≥70% and normal metabolism), fluctuating (40%≤ <70% or mild metabolic abnormalities), critically ill ( The infusion rate is classified into three levels: <40% and metabolic disorder. From a predefined nutritional regimen library (such as low-residue formula and immune-enhancing formula), a semantic similarity algorithm is used to match the basic regimen corresponding to the level (such as automatically matching the "low-volume-slow-element diet" template for critically ill patients). The preliminary regimen is verified in real time through a physiological constraint model (such as osmotic pressure ≤320mOsm / L and glucose load ≤5mg / kg / min). If the threshold is exceeded, automatic correction is triggered (such as reducing the infusion rate or adjusting the osmotic pressure of the formula).

[0135] Furthermore, by collecting transmission and conversion data of the critically ill patients under the nutrition delivery protocol, this embodiment of the invention can monitor the patients' tolerance (such as vomiting, abdominal distension), nutritional intake achievement rate, and metabolic response (such as blood glucose, electrolytes) in real time during the implementation of the nutrition protocol, providing a direct basis for dynamically adjusting the nutrition formula, infusion method, and evaluating the effectiveness of the protocol.

[0136] The transmission and conversion data refers to the dynamic conversion data related to the metabolism and utilization of nutrients from input to the patient's body, such as the conversion relationship between the infusion rate of nutritional preparations and the actual absorption rate in the intestine, and the proportion of different formulated nutrients (such as protein, fat, and carbohydrates) in the intestine for catabolism and metabolism.

[0137] Optionally, the transmission and conversion data can be obtained by collecting various physiological data of critically ill patients after the initial nutritional plan.

[0138] The optimized transmission module 104 is used to analyze the enteral nutrition tolerance of the critically ill patient based on the transmission conversion data, collect the metabolic data and immune status of the critically ill patient after the nutrition delivery program, and construct a personalized enteral nutrition delivery program for the critically ill patient based on the enteral nutrition tolerance, the metabolic data and the immune status, thereby obtaining the target nutrition delivery program.

[0139] This invention, through analysis of the intestinal nutritional tolerance of critically ill patients based on the transmitted and converted data, can analyze the intestinal digestion, absorption, and metabolic adaptation of nutritional preparations in critically ill patients, providing a basis for adjusting nutritional regimens (such as formula selection and infusion rate) to reduce the risk of intolerance.

[0140] The intestinal nutrient tolerance refers to the ability of the intestines of critically ill patients to adapt to the digestion, absorption, and metabolism of enteral nutrition preparations.

[0141] As an embodiment of the present invention, the step of analyzing the enteral nutrition tolerance of the critically ill patient based on the transmitted and converted data includes:

[0142] Based on the transmission and conversion data, the tolerance of the nutritional formula components, infusion rate, volume overload, and adverse reactions of the critically ill patients were analyzed to obtain multidimensional tolerance data.

[0143] The multidimensional tolerance data are subjected to index quantification to obtain tolerance index;

[0144] Based on the tolerance index, the enteral nutrition tolerance of the critically ill patients was determined.

[0145] The tolerance of the nutritional formula components refers to the patient's intestinal adaptability to various components in the nutritional preparation (such as protein source, fat type, dietary fiber content, osmotic pressure level, etc.). The tolerance of the infusion rate refers to the intestinal tolerance to the infusion rate of the nutritional preparation (such as ml / h). The tolerance of the volume load refers to the degree of tolerance of the intestinal to the total amount of nutritional preparation infused in a single or cumulative manner. The adverse reaction rate refers to the frequency, severity, and duration of negative reactions directly related to nutritional support (such as vomiting, gastrointestinal bleeding, hyperglycemia, etc.) that occur during enteral nutrition.

[0146] Optionally, the multidimensional tolerance data can be obtained by detecting metabolic indicators (such as blood glucose and osmolarity), gastrointestinal symptoms (such as vomiting and abdominal distension), and imaging data (such as ultrasound measurement of gastric residual volume) during enteral nutrition. These data can be used to assess the tolerance to nutritional formula components, infusion rate, volume load, and the occurrence of adverse reactions. The tolerance index converts each tolerance level and adverse reaction into a numerical score (such as using a Likert 5-point scale) and combines it with a weighted algorithm (such as dynamically adjusting weights according to the type of critical illness) to calculate a comprehensive tolerance index. The enteral nutrition tolerance can be determined based on a preset threshold (such as a tolerance index ≥80 points for high tolerance, 60-79 points for moderate tolerance, and <60 points for low tolerance) to determine the overall tolerance level of the patient's intestines to nutritional support.

[0147] Furthermore, in this embodiment of the invention, by collecting metabolic data and immune status of the critically ill patients after the nutrition delivery protocol, the effectiveness of nutritional support can be evaluated, thereby facilitating the adjustment of the nutrition delivery protocol to improve the patient's metabolic balance and immune function.

[0148] Optionally, the collection of metabolic data and immune status of the critically ill patients after the nutrition delivery protocol can be achieved by detecting metabolic data such as blood glucose, electrolytes, and liver and kidney function indicators in the patient's blood, as well as immune status indicators such as inflammatory factors (such as IL-6 and TNF-α) and immune cell counts (such as the CD4+ / CD8+ ratio).

[0149] This invention constructs a personalized enteral nutrition delivery plan for critically ill patients based on intestinal nutritional tolerance, metabolic data, and immune status. The resulting target nutrition delivery plan can be used to develop a more suitable nutritional plan for the patient, which has a more significant effect on optimizing nutrient absorption, maintaining metabolic balance, enhancing immune function, and improving the prognosis of critically ill patients.

[0150] Optionally, the supporting nutrition plan can be obtained by analyzing the patient's clinical status and then weighting the intestinal nutrition tolerance, the metabolic data and the immune status to obtain a weighted health status index. The weighted health status index is then used to construct the patient's medical label, and the medical label is used to construct the final target nutrition delivery plan.

[0151] The weighted health status index can be analyzed in conjunction with the table below (based on a comprehensive assessment of extensive clinical data):

[0152]

[0153] The nutrition monitoring module 105 is used to monitor the intestinal nutrition status of critically ill patients under the target nutrition delivery program in real time and obtain an intestinal nutrition monitoring report.

[0154] This invention provides an embodiment of the invention that monitors the intestinal nutritional status of critically ill patients under the target nutrient delivery protocol in real time. The resulting intestinal nutrition monitoring report can track changes in the patient's intestinal tolerance, metabolic indicators, and immune status under the target nutrient delivery protocol, and generate a monitoring report to dynamically adjust the nutritional support strategy, ensuring the safety and effectiveness of nutritional supply.

[0155] The intestinal nutrition monitoring report refers to a dynamic assessment report that integrates nutrition program implementation data, multi-dimensional monitoring indicators, and patient feedback to reflect the intestinal nutrition status and support effectiveness of critically ill patients.

[0156] As an embodiment of the present invention, the real-time monitoring of the enteral nutritional status of critically ill patients under the target nutrient delivery protocol, and the generation of an enteral nutritional monitoring report, includes:

[0157] Feature extraction is performed on the execution parameters of the target nutrient delivery scheme to obtain target feature parameters;

[0158] Synchronous multimodal data of the target feature parameters are collected to obtain time-aligned monitoring data;

[0159] Identify the patient's feedback status under the time-aligned monitoring data to obtain feedback data;

[0160] Using the target feature parameters, the time-aligned monitoring data, and the feedback data, a gut nutrition monitoring report for the critically ill patient is constructed.

[0161] The time-aligned monitoring data refers to multi-source data collected synchronously during the execution of the nutrition program by calibrating it with a unified timestamp (e.g., accurate to the minute). The feedback data refers to information based on the patient's clinical performance that reflects the patient's tolerance to the current nutrition program and their physiological response.

[0162] Optionally, the target characteristic parameters can be obtained by parsing execution parameters such as infusion rate, formulation component concentration, and osmotic pressure from the target nutrient delivery protocol. The time-aligned monitoring data can be obtained by synchronously collecting gastrointestinal tolerance data, metabolic indicators, and medication records during the execution of the nutrient protocol using a gastric residual volume sensor (automatically recording every 3 hours), a continuous glucose monitor (CGM), and an electronic medical record system, and then aligning the data through timestamp calibration. The intestinal nutrition monitoring report can be obtained by inputting characteristic parameters, multimodal data, and feedback status into a predefined template engine to generate an HTML report containing dynamic tolerance curves, a metabolic warning threshold comparison table, and a heatmap of immune function changes.

[0163] like Figure 2 The diagram shown is a flowchart illustrating a method for monitoring enteral nutrition in critically ill patients according to an embodiment of the present invention. In this embodiment, the method for monitoring enteral nutrition in critically ill patients includes:

[0164] Dynamic images of the gastric antrum region of critically ill patients are acquired. The contraction and relaxation cycles of the gastric antrum region are identified using the dynamic images. Based on the contraction and relaxation cycles, the contraction frequency and amplitude of the gastric antrum of critically ill patients are identified. Based on the contraction frequency and amplitude, the gastric emptying index of the critically ill patients is calculated.

[0165] The intestinal wall thickness variation and intestinal contents flow trajectory of the critically ill patients are identified to obtain intestinal peristalsis data. The intestinal peristalsis data and the gastric emptying index are used to calculate the intestinal transport efficiency of the critically ill patients.

[0166] The intestinal metabolic data of the critically ill patients were queried, and an intestinal nutrient delivery plan for the critically ill patients was constructed using the intestinal metabolic data and the intestinal transport efficiency. Transport and conversion data of the critically ill patients under the nutrient delivery plan were collected.

[0167] Based on the transmitted and converted data, the enteral nutrition tolerance of the critically ill patients is analyzed, and the metabolic data and immune status of the critically ill patients after the nutrition delivery program are collected. Based on the enteral nutrition tolerance, the metabolic data and the immune status, a personalized enteral nutrition delivery program for the critically ill patients is constructed to obtain the target nutrition delivery program.

[0168] Real-time monitoring of the enteral nutritional status of critically ill patients under the target nutrient delivery protocol yields an enteral nutrition monitoring report.

[0169] In the several embodiments provided by this invention, it should be understood that the provided systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0170] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0171] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A system for monitoring enteral nutrition in critically ill patients, characterized in that, The system includes: a gastric antrum region observation module, an intestinal transit analysis module, a nutrient transport module, an optimized transport module, and a nutrient monitoring module; The gastric antrum region observation module is used to acquire dynamic images of the gastric antrum region of critically ill patients. The dynamic images are used to identify the contraction and relaxation cycles of the gastric antrum region. Based on the contraction and relaxation cycles, the contraction frequency and amplitude of the gastric antrum in critically ill patients are identified. Based on the contraction frequency and amplitude, the gastric emptying index of the critically ill patients is calculated. The calculation of the gastric emptying index based on the contraction frequency and amplitude includes: standardizing the contraction frequency and amplitude to obtain normalized gastric motility parameters; and performing... Drug effect correction is performed to obtain corrected gastric motility parameters. Using these parameters, a nonlinear contribution function for the gastric emptying rate of critically ill patients is constructed. The gastric emptying index of the critically ill patients is then calculated using this nonlinear contribution function. Further, constructing the nonlinear contribution function for the gastric emptying rate of critically ill patients using the corrected gastric motility parameters includes: querying the corrected antral contraction frequency and amplitude from the corrected gastric motility parameters; and using the corrected antral contraction frequency and amplitude, combined with the following formula, to construct the nonlinear contribution function for the gastric emptying rate of critically ill patients: in, Let represent the nonlinear contribution function, and k represent the calibration coefficient of the nonlinear contribution function. This indicates the correction of the frequency of antral contractions. Represents the frequency weighting coefficient. This indicates correction of the amplitude of gastric antral contractions. This represents the amplitude weighting coefficient. Indicates the synergistic effect coefficient; The intestinal transit analysis module is used to identify changes in intestinal wall thickness and the flow trajectory of intestinal contents in critically ill patients to obtain intestinal peristalsis data. Using the intestinal peristalsis data and the gastric emptying index, the module calculates the intestinal transit efficiency of the critically ill patients. Specifically, calculating the intestinal transit efficiency using the intestinal peristalsis data and the gastric emptying index includes: identifying the intestinal peristalsis frequency, average flow velocity, and peristalsis coordination of the critically ill patients based on the intestinal peristalsis data; and calculating the intestinal transit efficiency of the critically ill patients using the following formula based on the intestinal peristalsis frequency, the average flow velocity, and the peristalsis coordination: in, Indicates intestinal transport efficiency. Indicates the gastric emptying index, denoted by , c represents the average flow rate, E represents the calibration coefficient for transport efficiency, and t represents peristaltic coordination. The nutrient delivery module is used to query the intestinal metabolic data of the critically ill patient, construct an intestinal nutrient delivery plan for the critically ill patient using the intestinal metabolic data and the intestinal transport efficiency, and collect the transport and conversion data of the critically ill patient under the nutrient delivery plan. The optimized transmission module is used to analyze the enteral nutrition tolerance of the critically ill patient based on the transmission conversion data, collect the metabolic data and immune status of the critically ill patient after the nutrition transmission program, and construct a personalized enteral nutrition transmission program for the critically ill patient based on the enteral nutrition tolerance, the metabolic data and the immune status to obtain the target nutrition transmission program. The nutrition monitoring module is used to monitor the intestinal nutritional status of critically ill patients under the target nutrition delivery program in real time and obtain an intestinal nutrition monitoring report.

2. The enteral nutrition monitoring system for critically ill patients as described in claim 1, characterized in that, Identifying the contraction and relaxation cycles of the gastric antrum region using the dynamic images includes: The dynamic image is subjected to frame segmentation processing to obtain a framed image; Identify the image features of the framed image; Using the image features, a motion trajectory curve of the gastric antrum region is constructed; The contraction and relaxation cycles of the gastric antrum region are identified using the aforementioned motion trajectory curve.

3. The enteral nutrition monitoring system for critically ill patients as described in claim 1, characterized in that, Based on the systolic and diastolic cycles, the contraction frequency and amplitude of the gastric antrum in critically ill patients are identified, including: The contraction and relaxation cycles are time-series labeled to obtain a periodic motion sequence; Using the aforementioned periodic motion sequence, the number of unit contractions of the gastric antrum in the critically ill patient was identified; Based on the number of unit contractions, the contraction frequency of the gastric antrum in the critically ill patient is determined; Using the systolic and diastolic cycles, the range of changes in the antral wall thickness of the gastric antrum in the critically ill patient was identified; Extreme values ​​were calculated for the range of gastric antral wall thickness variation to obtain the maximum thickness difference during systole and the minimum thickness difference during diastole. The amplitude of the gastric antrum in the critically ill patient is determined based on the difference between the maximum thickness during systole and the minimum thickness during diastole.

4. The enteral nutrition monitoring system for critically ill patients as described in claim 1, characterized in that, Using the intestinal metabolic data and the intestinal transport efficiency, an intestinal nutrition delivery protocol for the critically ill patient is constructed, including: The intestinal metabolic data and the intestinal transport efficiency are dynamically weighted and fused to obtain a weighted comprehensive evaluation value. Based on the weighted comprehensive assessment value, the critically ill patients are classified into clinical status categories to obtain their nutritional risk levels; The patient's nutritional risk level was matched with a protocol template to obtain a preliminary nutrition delivery protocol; The preliminary nutrient delivery scheme is subjected to safety constraint verification, and when the safety constraint verification result is safe, the enteral nutrient delivery scheme is obtained.

5. The enteral nutrition monitoring system for critically ill patients as described in claim 1, characterized in that, Based on the transmitted and converted data, the enteral nutrition tolerance of the critically ill patients is analyzed, including: Based on the transmission and conversion data, the tolerance of the nutritional formula components, infusion rate, volume overload, and adverse reactions of the critically ill patients were analyzed to obtain multidimensional tolerance data. The multidimensional tolerance data are subjected to index quantification to obtain tolerance index; Based on the tolerance index, the enteral nutrition tolerance of the critically ill patients was determined.

6. The enteral nutrition monitoring system for critically ill patients as described in claim 1, characterized in that, Real-time monitoring of enteral nutrition status in critically ill patients under the target nutrient delivery protocol, resulting in an enteral nutrition monitoring report, including: Feature extraction is performed on the execution parameters of the target nutrient delivery scheme to obtain target feature parameters; Synchronous multimodal data of the target feature parameters are collected to obtain time-aligned monitoring data; Identify the patient's feedback status under the time-aligned monitoring data to obtain feedback data; Using the target feature parameters, the time-aligned monitoring data, and the feedback data, a gut nutrition monitoring report for the critically ill patient is constructed.

7. A method for monitoring enteral nutrition in critically ill patients, characterized in that, The method includes: Dynamic images of the gastric antrum region of critically ill patients are acquired. The contraction and relaxation cycles of the gastric antrum region are identified using these dynamic images. Based on the contraction and relaxation cycles, the contraction frequency and amplitude of the gastric antrum in critically ill patients are identified. Based on the contraction frequency and amplitude, the gastric emptying index of the critically ill patients is calculated. The calculation of the gastric emptying index based on the contraction frequency and amplitude includes: standardizing the contraction frequency and amplitude to obtain normalized gastric motility parameters, and performing drug effect correction on the normalized gastric motility parameters. The corrected gastric motility parameters are obtained. Using these parameters, a nonlinear contribution function for the gastric emptying rate of critically ill patients is constructed. The gastric emptying index of the critically ill patients is then calculated using this nonlinear contribution function. Further, constructing the nonlinear contribution function for the gastric emptying rate of critically ill patients using the corrected gastric motility parameters includes: querying the corrected antral contraction frequency and corrected antral contraction amplitude from the corrected gastric motility parameters; and using the corrected antral contraction frequency and the corrected antral contraction amplitude, combined with the following formula, to construct the nonlinear contribution function for the gastric emptying rate of critically ill patients: in, Let represent the nonlinear contribution function, and k represent the calibration coefficient of the nonlinear contribution function. This indicates the correction of the frequency of antral contractions. Represents the frequency weighting coefficient. This indicates correction of the amplitude of gastric antral contractions. This represents the amplitude weighting coefficient. Indicates the synergistic effect coefficient; The intestinal wall thickness variation and intestinal contents flow trajectory of the critically ill patients are identified to obtain intestinal peristalsis data. Using the intestinal peristalsis data and the gastric emptying index, the intestinal transport efficiency of the critically ill patients is calculated. Specifically, calculating the intestinal transport efficiency of the critically ill patients using the intestinal peristalsis data and the gastric emptying index includes: based on the intestinal peristalsis data, identifying the intestinal peristalsis frequency, average flow velocity, and peristalsis coordination of the critically ill patients; and based on the intestinal peristalsis frequency, the average flow velocity, and the peristalsis coordination, calculating the intestinal transport efficiency of the critically ill patients using the following formula: in, Indicates intestinal transport efficiency. Indicates the gastric emptying index, denoted by , c represents the average flow rate, E represents the calibration coefficient for transport efficiency, and t represents peristaltic coordination. The intestinal metabolic data of the critically ill patients were queried, and an intestinal nutrient delivery plan for the critically ill patients was constructed using the intestinal metabolic data and the intestinal transport efficiency. Transport and conversion data of the critically ill patients under the nutrient delivery plan were collected. Based on the transmitted and converted data, the enteral nutrition tolerance of the critically ill patients is analyzed, and the metabolic data and immune status of the critically ill patients after the nutrition delivery program are collected. Based on the enteral nutrition tolerance, the metabolic data and the immune status, a personalized enteral nutrition delivery program for the critically ill patients is constructed to obtain the target nutrition delivery program. Real-time monitoring of the enteral nutrition status of critically ill patients under the target nutrient delivery protocol yields an enteral nutrition monitoring report.

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