Method and system for constructing a nutritional status transition prognosis model based on body weight dynamics

Abstract

The present application relates to a kind of nutrition state prognosis prediction model construction method and system based on weight dynamics, belong to medical information field.This method first establishes the population dataset containing individual weight information and nutrition state data, and carries out data preprocessing, including calculating weight loss percentage and defining outcome label.Subsequently, the dataset is divided into training set and test set, and the key features related to nutrition state prognosis are extracted from the training set by feature screening.Based on the screened features and training set, multiple machine learning classifiers are constructed, and the best model is selected according to the performance indicators of the classifier.Finally, the best model is used to predict the test set, and the performance of the model is evaluated.The present application uses weight dynamic information, without the aid of other information, can realize the high-performance prediction of common adverse nutrition state prognosis, has high practical value.

Description

Technical Field

[0001] The present invention belongs to the field of medical informationization and relates to a method and system for constructing a nutritional status outcome prediction model based on body weight dynamics. Background Art

[0002] Nutrition-related problems encompass a large category of disease states, including malnutrition, cachexia, and sarcopenia, and have become a major global public health challenge. Nutritional problems also commonly plague the diagnosis and treatment of clinical diseases. Poor nutritional status can affect the effectiveness and safety of disease treatment, increase the use of medical resources, and lead to a variety of adverse consequences. Taking malignant tumors as an example, approximately 10%-20% of cancer deaths can be attributed to malnutrition. However, due to the lack of appropriate diagnostic technology and sufficient attention, poor nutritional states such as malnutrition and cachexia are often underestimated, misdiagnosed, or untreated. Therefore, active diagnosis, monitoring, and intervention of poor nutritional status are crucial for patients, which can minimize or reverse its negative impact on prognosis, improve patients' quality of life and outcomes, and reduce medical costs.

[0003] Diagnostic criteria for diagnosing nutritional conditions such as malnutrition and cachexia have long varied across regions and institutions, making the development of a globally harmonized framework for patient management difficult. To address this challenge, a range of academic organizations have proposed different frameworks for assessing nutritional status. For example, in 2019, the Global Leadership Initiative on Malnutrition (GLIM), an international consensus-based conceptual framework for diagnosing malnutrition in adults, was proposed for clinical use. Since its publication, the GLIM framework has proven valuable in diagnosing malnutrition and predicting clinical outcomes across a wide range of conditions. The GLIM criteria use a phenotypic and an etiological criterion for diagnosing malnutrition. The phenotypic criteria further include weight loss calculated from historical and current weight, body mass index, and muscle mass. Similarly, in 2011, Fearon et al. led an international Delphi consensus and developed a conceptual framework for the diagnosis of cancer cachexia, which has since gained widespread acceptance. This framework also uses three pieces of information for diagnosing cachexia: recent weight loss, body mass index, and muscle mass.

[0004] However, existing technical frameworks for assessing nutritional deficiencies, whether the GLIM criteria for malnutrition or the Fearon criteria for cachexia, can only assess an individual's nutritional status at a single point in time and cannot effectively predict future nutritional outcomes. Given the variability in individual responses to malnutrition and its treatment, identifying patients who may benefit from multidisciplinary treatment and / or potentially recover normal nutritional status would facilitate personalized medical decision-making. Weight and body mass index are key factors in identifying, characterizing, and stratifying nutritional status. Skeletal muscle mass, which accounts for almost half of body weight, can also be predicted by weight, height, sex, and age. Previous studies have shown that these factors also help predict the risk of future weight and muscle loss. Therefore, using information about weight, which is routinely available at the time of hospital admission, could potentially independently predict future changes in nutritional status. Predicting changes in nutritional status could enable precise patient stratification and identification of those with potential for nutritional recovery, enabling more precise management strategies and optimizing patient outcomes. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method and system for constructing a nutritional status outcome prediction model based on weight dynamics, so as to serve as an auxiliary decision-making tool to realize nutritional status outcome assessment, patient risk stratification and efficacy estimation in various scenarios such as low cost, remote and self-assessment.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] Methods for constructing a nutritional status outcome prediction model based on body weight dynamics include:

[0008] Establishing a population data set, the population data set including a plurality of individuals, each individual including weight information at a plurality of time points and nutritional status at the corresponding time points;

[0009] Preprocessing the sample data includes calculating a percentage of weight loss and defining an outcome label, wherein the outcome label is determined based on changes in the nutritional status of the individual at the multiple time points;

[0010] Dividing the population dataset into a training set and a test set;

[0011] Modeling feature screening, screening features related to the outcome label from the training set;

[0012] Constructing multiple machine learning classifiers based on the screened features and the training set;

[0013] Selecting the best model, selecting the best model based on the performance indicators of the machine learning classifier;

[0014] Performance evaluation and prediction: using the optimal model to predict the test set and evaluate the performance of the prediction model.

[0015] Furthermore, the weight information at multiple time points includes:

[0016] Current weight;

[0017] Historical weight, including historical weight data at multiple time points.

[0018] Furthermore, the nutritional status is diagnosed according to the GLIM standard, the Fearon standard or the SGA scale.

[0019] Furthermore, the ending tag includes:

[0020] Examples of nutritional status transitions, i.e., changes in the nutritional status of the individual at the multiple time points;

[0021] An example of a continuous nutritional state is one in which the nutritional state of the individual remains constant at the multiple time points.

[0022] Furthermore, the machine learning classifier includes:

[0023] Random Forest;

[0024] Multilayer Perceptron;

[0025] Long Short-Term Memory Network;

[0026] Transformer.

[0027] Furthermore, the performance indicators include:

[0028] Accuracy;

[0029] Area under the curve;

[0030] Recall rate;

[0031] Accuracy;

[0032] F1 score;

[0033] Kappa index.

[0034] A nutritional status prognosis prediction system based on body weight dynamics, comprising:

[0035] A data collection module is used to collect weight information and nutritional status information of multiple individuals;

[0036] A data analysis module, configured to construct a nutritional status outcome prediction model based on the weight information and nutritional status information;

[0037] The result output module is used to output the prediction result of the prediction model.

[0038] Furthermore, the data acquisition module includes:

[0039] A model training data input submodule is used to collect training data for building the prediction model;

[0040] The individual prediction data input submodule is used to collect individual data for nutritional status outcome prediction.

[0041] Furthermore, the data analysis module includes:

[0042] A prediction model building submodule, configured to build the prediction model based on the training data;

[0043] The prediction submodule is executed to predict the individual data using the prediction model.

[0044] Furthermore, the result output module includes:

[0045] Batch modeling result output submodule, used to output the performance indicators of the prediction model;

[0046] The individual prediction result output submodule is used to output the prediction result of the prediction model.

[0047] The beneficial effects of the present invention are:

[0048] (1) Compared with commonly used nutritional status evaluation methods, the present invention uses only dynamic weight information as the independent variable in the prediction process, which has the advantages of being simple and easy to understand. It does not require reliance on complex instruments and high-end medical equipment, and does not require the intervention of health professionals. All independent variable information can be obtained through baseline evaluation by others or individuals themselves to achieve prediction. Therefore, it is expected to be widely used in various types of hospitals at all levels, community health service institutions, family scenarios, and research scenarios, and has the characteristics of being simple, fast, and low-cost.

[0049] (2) The present invention does not need to consider what kind of clinical treatment or other information the patient will receive in the future, so it can determine which patients are likely to benefit from nutrition at an early stage;

[0050] (3) The present invention does not specify a specific machine learning classification algorithm. Therefore, a specific algorithm can be reasonably selected according to different populations, disease stages, data types, and sample sizes. This improves the flexibility of the present invention in the application process and can meet the needs of saving computing costs under different hardware conditions;

[0051] (4) The present invention can achieve more refined predictions such as aggravation or alleviation of abnormal nutritional status by replacing the multi-classification machine learning algorithm in the modeling step;

[0052] (5) The present invention is expected to be applicable to other systems that use body weight as the primary diagnostic criterion and has a certain degree of deductive potential.

[0053] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0055] Figure 1 It is a technical flow chart of the present invention;

[0056] Figure 2 The precision-recall curve and receiver operating characteristic curve of the test set in Example 1 of the present invention are shown;

[0057] Figure 3 The model prediction confusion matrix of the test set in Example 1 of the present invention and the relative importance diagram of the top three variables;

[0058] Figure 4 The precision-recall curve and receiver operating characteristic curve of the test set in Example 2 of the present invention are shown;

[0059] Figure 5 This is the model prediction confusion matrix of the test set in Example 2 of the present invention and the relative importance diagram of the top three variables. DETAILED DESCRIPTION

[0060] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0061] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0062] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0063] In a first aspect, the present invention provides a method for constructing a nutritional status outcome prediction model based on body weight dynamics, comprising the following steps:

[0064] 1) Establish a crowd dataset:

[0065] A population of hospitalized patients was selected, and variable information of the modeling population was collected through sources such as public databases or medical electronic medical record systems. The modeling information included two time nodes, one before and one after. The variables included at each time point were similar, including but not limited to age, gender, height, body mass index, current weight, historical weight at multiple time points, and limb skeletal muscle index as data set samples;

[0066] 2) Sample data preprocessing:

[0067] The collected sample datasets were preprocessed, including but not limited to removing outliers, excluding samples with missing key modeling information, imputing missing data using multiple imputation, performing one-hot encoding on multi-category data, standardizing continuous variables, and removing variables with identical values ​​across all samples. Percent weight loss was calculated using the formula: (weight at the next time point - weight at the previous time point) ÷ weight at the previous time point × 100%. Outcome labels were defined by first diagnosing nutritional status at the two time points using widely recognized international standards, including but not limited to the GLIM (Global Leaders Initiative on Malnutrition) criteria, the Fearon cachexia diagnostic criteria, and the Subjective Global Assessment Scale (SGA). Cases in which nutritional status changed from normal to abnormal or from abnormal to normal at the next time point were defined as nutritional status transitions and coded as 1. Cases in which nutritional status remained unchanged were defined as persistent nutritional status and coded as 0. If there is a severity rating for nutritional status, the discrete state transition possibilities such as persistence of abnormal status, worsening of status, improved status but not fully restored to normal, and fully restored to normal status are coded separately;

[0068] 3) Divide the training set and test set:

[0069] The sample data set pre-processed in step 2) is randomly divided into a training set and a test set according to a certain ratio. The training set occupies a larger proportion and is used for model training, while the training set data occupies a smaller proportion and is used to evaluate the effectiveness of the model classification;

[0070] 4) Modeling feature screening

[0071] Selecting the training set divided in step 3), using nutritional status outcome as the outcome variable, and using the baseline information preprocessed in step 1) that can be obtained only from the patient's first face-to-face consultation as the independent variable, including but not limited to the percentage of weight loss in the past month, the percentage of weight loss in the past six months, the percentage of weight loss in the past year, the current body mass index, the body mass index one month ago, and the body mass index six months ago; using the importance ranking of the independent variables or manually incorporating variables, retaining key features that have a significant impact on the model's predictive performance, and eliminating irrelevant features that have a small impact on the prediction results, ultimately obtaining a more concise and efficient feature subset;

[0072] 5) Build multiple machine learning classifiers

[0073] Based on the training set of modeling independent variables compiled in step 4), multiple classifiers are constructed using supervised machine learning algorithms, where the machine learning algorithms include but are not limited to random forest, multilayer perceptron, long short-term memory network, and Transformer; based on the number and value range of hyperparameters of different classifiers, the hyperparameters of the classifiers are optimized using random search or grid search, and multiple evaluation graphs and indicators are generated using cross-validation or validation in a validation set, including but not limited to receiver operating characteristic curves, calibration curves, areas under the curves and 95% confidence intervals, and Brier scores;

[0074] 6) Select the best model

[0075] Based on the model evaluation index obtained in step 5), a main evaluation index is selected according to the purpose, the candidate classifiers are sorted according to the index evaluation index, and the classifier with the best performance is selected as the best model;

[0076] 7) Performance evaluation and prediction

[0077] Based on the optimal model obtained in step 6), a rapid prediction of the nutritional status of the test set samples or the user-entered samples is performed;

[0078] In step 1), a specific subgroup of the population is selected for stratified modeling to reduce the amount of calculation and obtain a more targeted model;

[0079] In step 2), the standardization method for continuous variables is the z-score method, and the specific formula is: (x-μ) / σ. Where μ represents the mean of the overall data, σ represents the standard deviation of the overall data, and x represents the individual observation value;

[0080] In step 3), a cross-validation method is used to improve sample utilization rate;

[0081] In step 4), features widely used in clinical practice are manually incorporated into the modeling.

[0082] In the second aspect, the present invention provides a method for constructing a nutritional status outcome prediction model based on body weight dynamics, see Figure 1 , the system mainly includes the following modules:

[0083] Data acquisition module:

[0084] 1) Model training data input submodule: used to collect training data required for modeling from electronic medical data systems, local data sources, or other data sources, including but not limited to age, gender, height, body mass index, current weight, historical weight at multiple time points, and limb skeletal muscle index at multiple time points as input data;

[0085] 2) Individual prediction data input submodule: receives single or batch model independent variable information input by the user as input data;

[0086] Data analysis module:

[0087] 1) Prediction model building submodule: Using the data obtained from the model training data input submodule, perform the modeling steps of steps 1) to 7) in the first aspect to obtain a nutritional status outcome prediction model based on body weight dynamics and model performance evaluation indicators;

[0088] 2) executing the prediction submodule: performing risk calculation based on the nutritional status outcome prediction model obtained by the prediction model building submodule using the input data obtained by the data acquisition module to obtain the nutritional status outcome categories and the predicted probability corresponding to each category;

[0089] Result output module:

[0090] 1) Batch modeling result output submodule: outputs model performance evaluation indicators based on the modeling results of the prediction model modeling submodule;

[0091] 2) Individual prediction result output submodule: Obtain the nutritional status conversion to each label category and the prediction probability corresponding to each category based on the calculation result output of the execution prediction submodule.

[0092] Example 1:

[0093] In this embodiment, see Figure 1 A method for constructing a nutritional status prognosis prediction model based on body weight dynamics includes the following steps:

[0094] 1) Establish a crowd dataset:

[0095] A total of 3,610 patients diagnosed with cachexia using the Fearon framework at admission were selected as the dataset sample. Individual data including age, gender, percentage change in weight from six months ago to one month ago, percentage change in weight from one month ago to admission, body mass index at admission, and cachexia diagnosis based on the Fearon framework one month after admission were collected.

[0096] 2) Sample data preprocessing:

[0097] The collected sample data set was preprocessed, and continuous variables were z-score standardized using the formula: (x-μ) / σ. μ represents the mean of the overall data, σ represents the standard deviation of the overall data, and x represents the individual observation value. The cachexia prognosis one month after admission was used as the outcome label. Patients whose cachexia changed from positive at baseline to negative one month later were coded as 1, and those who did not were coded as 0.

[0098] 3) Divide the training set and test set:

[0099] The sample data set preprocessed in step 2) is randomly divided into a training set and a test set in a ratio of 8:2;

[0100] 4) Modeling feature screening

[0101] The training set divided in step 3) was selected, and cachexia prognosis was used as the outcome variable. The percentage change in weight from six months ago to one month ago, the percentage change in weight from one month ago to the time of admission, and the body mass index at the time of admission were manually selected as independent variables to obtain a subset containing four features.

[0102] 5) Build multiple classifiers:

[0103] Based on the training set after feature selection in step 4), three classifiers were constructed using supervised machine learning algorithms in the Python 3.9.11 environment, including a Gaussian process classifier, a multilayer perceptron, and a gradient boosting machine algorithm. A random search was iterated 1000 times to find the hyperparameters of each model, and a 10-fold cross-validation method was used to calculate the model performance parameters in the training set, including accuracy, area under the curve, recall, precision, F1 score, and Kappa index. Table 1 shows the classification performance of the model in the training set after 10-fold cross-validation of Example 1.

[0104] Table 1

[0105] Model Accuracy Area under the curve Recall Accuracy F1 score Kappa Gaussian process classifier 0.864 0.916 0.913 0.838 0.874 0.726 Multilayer Perceptron 0.860 0.916 0.907 0.836 0.870 0.718 Gradient Boosting Machine 0.856 0.915 0.901 0.835 0.866 0.712

[0106] 6) Select the best model

[0107] On the basis of the model evaluation index obtained in step 5), the area under the cross-validation curve was used as the main evaluation index, and the candidate classifiers were ranked according to the index evaluation index, and the Gaussian process classifier model with the best performance was selected as the best model, see Table 1;

[0108] 7) Performance evaluation and prediction

[0109] According to the best model obtained in step 6), the cachexia prognosis of the test set samples was quickly predicted, and the precision-recall rate curve, receiver operating characteristic curve, confusion matrix and the relative importance diagram of the top three variables were drawn respectively. Figure 2 and Figure 3 .

[0110] Example 2:

[0111] This embodiment is substantially the same as the first embodiment. Figure 1 The data analysis module and result output module in

[15] are similar, but use different input features and different machine learning algorithms for modeling. A method for constructing a nutritional status outcome prediction model based on weight dynamics includes the following steps:

[0112] 1) Establish a crowd dataset:

[0113] A total of 4,197 patients diagnosed with malnutrition using the GLIM framework upon admission were selected as data sets. Individual data including age, gender, body mass index six months prior, body mass index one month prior, body mass index upon admission, predicted limb skeletal muscle index six months prior, predicted limb skeletal muscle index one month prior, predicted limb skeletal muscle index upon admission, and malnutrition diagnosis based on the GLIM framework one month after admission were collected as data set samples.

[0114] 2) Sample data preprocessing:

[0115] The collected sample data set was preprocessed and continuous variables were z-score standardized using the formula: (x-μ) / σ. μ represents the mean of the overall data, σ represents the standard deviation of the overall data, and x represents the individual observation. The malnutrition outcome one month after admission was used as the outcome label. Patients whose malnutrition changed from positive at baseline to negative one month later were coded as 1, and those who did not were coded as 0.

[0116] 3) Divide the training set and test set:

[0117] The sample data set preprocessed in step 2) is randomly divided into a training set and a test set in a ratio of 8:2;

[0118] 4) Modeling feature screening

[0119] The training set divided in step 3) was selected, malnutrition outcome was used as the outcome variable, and body mass index six months ago, body mass index one month ago, body mass index at admission, predicted limb skeletal muscle index six months ago, predicted limb skeletal muscle index one month ago, and predicted limb skeletal muscle index at admission were manually selected as independent variables to obtain a subset containing 7 features.

[0120] 5) Build multiple classifiers:

[0121] Based on the training set after feature selection in step 4), three classifiers were constructed using supervised machine learning algorithms in the Python 3.9.11 environment, including gradient boosting, lightweight gradient boosting, and extreme gradient boosting. Random search was used to iterate 1000 times to find the hyperparameters of each model. Ten-fold cross-validation was used to calculate the model performance parameters in the training set, including accuracy, area under the curve, recall, precision, F1 score, and Kappa index. Table 2 shows the classification performance of the model in Example 2 after ten-fold cross-validation in the training set.

[0122] Table 2

[0123] Model Accuracy Area under the curve Recall Accuracy F1 score Kappa Gradient Boosting Machine 0.885 0.828 0.471 0.752 0.578 0.515 Lightweight Gradient Boosting Machine 0.884 0.820 0.503 0.726 0.593 0.528 Extreme Gradient Boosting Machine 0.881 0.818 0.501 0.702 0.583 0.516

[0124] 6) Select the best model

[0125] Based on the model evaluation index obtained in step 5), the area under the cross-validation curve is used as the main evaluation index. The candidate classifiers are ranked according to the index evaluation index, and the gradient boosting machine model with the best performance is selected as the best model, see Table 2;

[0126] 7) Performance evaluation and prediction

[0127] According to the best model obtained in step 6), the cachexia prognosis of the test set samples was quickly predicted, and the precision-recall rate curve, receiver operating characteristic curve, confusion matrix and the relative importance diagram of the top three variables were drawn respectively. Figure 4 and Figure 5 .

[0128] The results demonstrate that the present invention can leverage weight dynamics to achieve high-performance predictions of common adverse nutritional outcomes without the need for additional information. The proposed method and system for constructing a weight dynamics-based nutritional outcome prediction model is expected to serve as a decision-making aid for nutritional outcome prediction in a variety of settings, including medical institutions, public health and healthcare facilities, and households.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for constructing a nutritional status outcome prediction model based on body weight dynamics, characterized by: include: Establishing a population data set, the population data set including a plurality of individuals, each individual including weight information at a plurality of time points and nutritional status at the corresponding time points; Preprocessing the sample data includes calculating a percentage of weight loss and defining an outcome label, wherein the outcome label is determined based on changes in the nutritional status of the individual at the multiple time points; The weight information at multiple time points includes: Current weight; Historical weight, including historical weight data at multiple time points; Dividing the population dataset into a training set and a test set; Modeling feature screening, screening features related to the outcome label from the training set; the outcome label includes: Examples of nutritional status transitions, i.e., changes in the nutritional status of the individual at the multiple time points; Examples of continuous nutritional states, i.e., the nutritional state of the individual remains unchanged at the multiple time points; Constructing multiple machine learning classifiers based on the screened features and the training set; Selecting the best model, selecting the best model based on the performance indicators of the machine learning classifier; Performance evaluation and prediction: using the optimal model to predict the test set and evaluate the performance of the prediction model.

2. The method for constructing a nutritional status outcome prediction model based on body weight dynamics according to claim 1, characterized in that: The nutritional status was diagnosed according to the GLIM standard, Fearon standard or SGA scale.

3. The method for constructing a nutritional status outcome prediction model based on body weight dynamics according to claim 1, characterized in that: The machine learning classifier includes: Random Forest; Multilayer Perceptron; Long Short-Term Memory Network; Transformer.

4. The method for constructing a nutritional status outcome prediction model based on body weight dynamics according to claim 1, characterized in that: The performance indicators include: Accuracy; Area under the curve; Recall rate; Accuracy; F1 score; Kappa index.

5. A nutritional status prognosis prediction system based on body weight dynamics using the construction method of claim 1, characterized in that: include: A data collection module is used to collect weight information and nutritional status information of multiple individuals; A data analysis module, configured to construct a nutritional status outcome prediction model based on the weight information and nutritional status information; The result output module is used to output the prediction result of the prediction model.

6. The nutritional status prognosis prediction system based on body weight dynamics according to claim 5, characterized in that: The data acquisition module includes: A model training data input submodule is used to collect training data for building the prediction model; The individual prediction data input submodule is used to collect individual data for nutritional status outcome prediction.

7. The nutritional status prognosis prediction system based on body weight dynamics according to claim 5, characterized in that: The data analysis module includes: A prediction model building submodule, configured to build the prediction model based on the training data; The prediction submodule is executed to predict the individual data using the prediction model.

8. The nutritional status prognosis prediction system based on body weight dynamics according to claim 5, characterized in that: The result output module includes: Batch modeling result output submodule, used to output the performance indicators of the prediction model; The individual prediction result output submodule is used to output the prediction result of the prediction model.

Images