CN115240854A - Method and system for processing pancreatitis prognosis data - Google Patents
Method and system for processing pancreatitis prognosis data Download PDFInfo
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Abstract
The invention discloses a method, a system, equipment and a computer readable storage medium for processing pancreatitis prognosis data, wherein the method comprises the following steps: acquiring human body basic sign data of a sample; inputting the human body basic sign data into a pre-trained prognosis prediction model to obtain a first classification result; calculating the score value of a prognosis classification index based on the human body basic sign data, and obtaining a second classification result according to the score value; the prognostic classifier includes: classifying pancreatitis; carrying out weighted fusion processing on the first classification result and the second classification result to obtain a final classification result; the prognosis prediction model comprises: a pancreatitis grading prediction model.
Description
Technical Field
The invention relates to the technical field of medical biology, in particular to a method and a system for processing pancreatitis prognosis data.
Background
Acute Pancreatitis (AP) is a common acute abdomen disease and is the leading digestive system disease for emergency hospitalization worldwide. Pancreatitis can be classified into severe, moderate and severe, and mild according to systemic and local complications. About 20% of patients with acute pancreatitis develop severe pancreatitis, i.e., with persistent organ failure, with mortality rates as high as 20% -40%. Moderate-severe pancreatitis has local complications and has potential intervention and treatment needs. The mild pancreatitis is self-limited by supportive treatment. Early prognosis of the severity of acute pancreatitis and timely targeted treatment are important to reducing the fatality rate.
At present, no single variable or scoring system for acute pancreatitis can accurately predict the severity of pancreatitis prognosis at the initial stage of disease onset. Machine learning has been widely used in clinical studies including predicting the risk of severe pancreatitis, but most studies only perform dichotomous prediction, i.e., acute pancreatitis patients will develop severe pancreatitis or non-severe pancreatitis in the future, and do not retrieve the research results that can accurately classify and predict the severity of pancreatitis prognosis, i.e., differentiate light, medium and severe pancreatitis. In addition, most of the existing researches are single-center researches, the number of samples is small, and the accuracy of results is required to be provided for the merchant.
The invention provides a method and a system for processing pancreatitis prognosis data, which enable doctors to identify high-risk patients as early as possible and make diagnosis and treatment plans to provide help references.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. According to the method, a prediction model is established for indexes such as the three-classification prognosis of the acute pancreatitis, whether the acute pancreatitis dies or not, whether the pancreas is infected and necrotic, whether the acute pancreatitis stays in an ICU (intensive care unit) or not, the hospitalization duration and the like by adopting a large amount of data from multiple centers and various machine learning methods, the classification result obtained by the prediction model is subjected to weighted fusion with the classification result obtained based on 5 traditional scoring systems, the final classification result is obtained, a help reference is provided for doctors to identify high-risk patients as early as possible and make a diagnosis and treatment plan, and related life science problems are solved.
The application discloses a method for processing pancreatitis prognosis data, which comprises the following steps:
acquiring human body basic sign data of a sample;
inputting the human body basic sign data into a pre-trained prognosis prediction model to obtain a first classification result;
calculating the score value of a prognosis classification index based on the human body basic sign data, and obtaining a second classification result according to the score value; the prognostic classification indicators include: classifying pancreatitis;
carrying out weighted fusion processing on the first classification result and the second classification result to obtain a final classification result;
the prognostic prediction model includes: a pancreatitis grading prediction model; the training method of the pancreatitis grading prediction model comprises the following steps:
acquiring human body basic sign data and classification labels of a training set sample, wherein the labels comprise mild pancreatitis, moderate severe pancreatitis and severe pancreatitis;
carrying out feature extraction on the human body basic sign data of the training set sample to obtain human body basic sign data features after feature extraction;
and constructing a model by using the human body basic sign data characteristics to obtain a constructed pancreatitis grading prediction model.
The scoring values include one or more of: APACHE-II score value, marshall score value, sofa score value, qsofa score value, bisap score value.
The training method of the pancreatitis grading prediction model further comprises the following steps:
sequencing the human body basic sign data characteristics to obtain human body basic sign data characteristics subjected to sequencing;
performing model construction by using the sorted human body basic sign data characteristics to obtain a constructed pancreatitis grading prediction model;
optionally, the sorting process includes:
for one of the human body basic sign data features after feature extraction, calculating all combinations and marginal contribution of each human body basic sign data feature in the combinations according to a combination sequence formed by the human body basic sign data features;
obtaining the contribution degree of a single human body basic sign data characteristic in the human body basic sign data characteristic according to the marginal contribution;
sorting the human body basic sign data features based on the contribution degrees, and outputting N human body basic sign data features with high contribution degrees;
optionally, N is at least 5.
The first classification result includes: mild pancreatitis, moderate severe pancreatitis, and severe pancreatitis;
optionally, the second classification result includes: mild pancreatitis, moderate severe pancreatitis, and severe pancreatitis.
The prognostic classifier further includes: whether or not to die;
the prognostic prediction model further includes: a death prediction model;
the training method of the death prediction model comprises the following steps:
acquiring human body basic sign data and classification labels of a training set sample, wherein the labels comprise death and non-death;
carrying out feature extraction on the human body basic sign data of the training set sample to obtain human body basic sign data features after feature extraction;
constructing a model by using the human body basic sign data characteristics to obtain a constructed death prediction model;
optionally, the prognostic classifier further includes: whether or not to infect necrosis;
the prognostic prediction model further includes: an infectious necrosis prediction model;
the method for training the infectious necrosis prediction model comprises the following steps:
acquiring human body basic sign data and classification labels of a training set sample, wherein the labels comprise infection necrosis and non-infection necrosis;
carrying out feature extraction on the human body basic sign data of the training set sample to obtain human body basic sign data features after feature extraction;
constructing a model by using the human body basic sign data characteristics to obtain a constructed infectious necrosis prediction model;
optionally, the prognostic classification index further includes: grading the hospitalization duration;
optionally, the prognostic prediction model further includes: a hospitalization duration prediction model;
the training method of the stay in hospital prediction model comprises the following steps:
acquiring human body basic sign data and classification labels of a training set sample, wherein the labels comprise a first hospitalization duration and a second hospitalization duration; the first length of stay is less than or equal to the classification threshold, and the second length of stay is greater than or equal to the classification threshold;
carrying out feature extraction on the human body basic sign data of the training set sample to obtain human body basic sign data features after feature extraction;
constructing a model by using the human body basic sign data characteristics to obtain a constructed hospitalization duration prediction model;
optionally, the prognostic classifier further includes: whether to live in the ICU;
optionally, the prognostic prediction model further includes: an ICU prediction model;
the ICU prediction model training method comprises the following steps:
acquiring human body basic sign data and classification labels of a training set sample, wherein the labels comprise an entrance ICU and an exit ICU;
carrying out feature extraction on the human body basic sign data of the training set sample to obtain human body basic sign data features after feature extraction;
and constructing a model by using the human body basic sign data characteristics to obtain a constructed ICU prediction model.
The human body basic sign data comprises one or more of the following: age, sex, etiology, body temperature, blood pressure, heart rate, respiratory rate, white blood cells, hematocrit, platelets, blood potassium, blood sodium, blood calcium, creatinine, urea nitrogen, adequacy score, peritoneal irritation, pleural effusion.
Performing model construction on the human body basic sign data by using a machine learning method to obtain a constructed prognosis prediction model;
optionally, the machine learning method includes one or more of the following: linear regression, logistic regression, linear Discriminant Analysis (LDA), classification and regression trees, naive bayes, KNN, learning vector quantization, support Vector Machines (SVMs), random forests, lightGBM;
optionally, the machine learning method includes: weighted fusion of any two algorithms;
optionally, the machine learning method includes: weighted fusion of random forest and LightGBM.
An apparatus for processing pancreatitis prognosis data, the apparatus comprising: a memory and a processor;
the memory is to store program instructions;
the processor is used for calling program instructions and executing the method for processing pancreatitis prognosis data when the program instructions are executed.
A system for processing pancreatitis prognosis data, comprising:
the acquisition unit is used for acquiring human body basic sign data of the sample;
the first classification unit is used for inputting the human body basic sign data into a pre-trained prognosis prediction model to obtain a first classification result;
the second classification unit is used for calculating the score of a prognosis classification index based on the human body basic sign data and obtaining a second classification result according to the score; the prognostic classification indicators include: classifying pancreatitis;
the third classification unit is used for performing weighted fusion processing on the first classification result and the second classification result to obtain a final classification result;
the prognostic prediction model includes: the training method of the pancreatitis grading prediction model comprises the following steps:
acquiring human body basic sign data and classification labels of a training set sample, wherein the labels comprise mild pancreatitis, moderate severe pancreatitis and severe pancreatitis;
carrying out feature extraction on the human body basic sign data of the training set sample to obtain human body basic sign data features after feature extraction;
and constructing a model by using the human body basic sign data characteristics to obtain a constructed pancreatitis grading prediction model.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method of processing pancreatitis prognosis data as described above.
The application has the following beneficial effects:
1. the method establishes a three-classification prognosis prediction model for the prognosis data index of the acute pancreatitis through various machine learning methods, and classifies the prognosis severity of the acute pancreatitis more accurately, namely, the mild pancreatitis, the moderate severe pancreatitis or the severe pancreatitis, wherein the three-classification prediction result is compared with the two-classification prediction result, so that the prognosis severity of the pancreatitis is more accurately predicted and predicted in advance, the early prediction of the severity of the acute pancreatitis is facilitated, and a doctor is assisted in accurately predicting the pancreatitis;
2. the method ingeniously performs weighted fusion on a classification result based on a constructed prognosis prediction model and a classification result obtained by classifying based on score values obtained by 5 traditional scoring systems to obtain a new combined model, and the new combined model has a better and unexpected effect when processing pancreatitis prognosis data; after parameters of a patient, namely human body sign data, are input, a final classification result is obtained through a 2-classification method; the 5 grading systems have certain research on SAP classification, the traditional grading system is fused with a prognosis prediction model constructed by clinical data, and the results obtained by the 2 methods are weighted and fused by using a machine learning algorithm, so that the grading system has multi-center and foresight properties, and the precision and the depth of data analysis and processing are greatly improved;
3. the method has the advantages that the main endpoint for prediction is the pancreatitis severity prognosis, the secondary endpoints are death, pancreatic infection necrosis, ICU admission and length of stay, various indexes are integrated, help reference can be provided for doctors to recognize high-risk patients and make diagnosis and treatment plans as soon as possible, and related life science problems are solved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of the analysis of a method for processing pancreatitis prognosis data provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of an apparatus for processing pancreatitis prognosis data provided by an embodiment of the present invention;
FIG. 3 is a schematic flow chart of a system for processing pancreatitis prognosis data according to an embodiment of the present invention;
FIG. 4 is a diagram of classification results of various machine learning algorithms in human body basic sign data according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an algorithm according to an embodiment of the present invention, which uses a random forest method for classification;
fig. 6 is a schematic diagram of an algorithm two according to the present invention, which uses the LightGBM method for classification;
FIG. 7 is a graph illustrating the difference in AUC for prognosis of acute pancreatitis (SAP, MSAP, MAP) predicted under different machine learning algorithms provided by an embodiment of the present invention;
FIG. 8 is a graph showing the accuracy of prognosis prediction for acute pancreatitis (SAP, MSAP, MAP) with different machine learning algorithms provided by an embodiment of the present invention;
FIG. 9 is a schematic diagram of the comparison between the machine learning algorithm provided by the embodiment of the present invention and the severity AUC of acute pancreatitis predicted by the traditional scoring model;
FIG. 10 is a schematic diagram of a machine learning algorithm in comparison with a calibration graph of a traditional scoring model for predicting the severity of acute pancreatitis;
fig. 11 is a diagram illustrating the machine learning algorithm to predict a secondary endpoint according to an embodiment of the present invention: schematic diagrams of whether death occurred, whether pancreas was necrotized, whether admission to ICU, and length of stay;
fig. 12 shows that when the fusion model provided by the embodiment of the present invention predicts the secondary endpoint: schematic representation of AUC, acutacy, sensitivity, specificity, PPV and dNTP;
fig. 13 is a diagram illustrating variable contribution values when predicting AP severity classification according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention.
In some flows described in the present specification and claims and above figures, a number of operations are included that occur in a particular order, but it should be clearly understood that these operations may be performed out of order or in parallel as they occur herein, with the order of the operations being given as 101, 102, etc. merely to distinguish between various operations, and the order of the operations itself does not represent any order of performance. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor limit the types of "first" and "second" to be different.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of a method for processing pancreatitis prognosis data according to an embodiment of the present invention, specifically, the method includes the following steps:
101: acquiring human body basic sign data of a sample;
in one embodiment, the human body basic sign data includes one or more of the following: age (years), gender Male (%), etiology Etiology, body Temperature (deg.C), blood pressure SBP (mmHg), heart rate HR (bpm), respiratory rate RR (bpm), white blood cell WBC (x 109/L), hematocrit HCT, platelet PLT (x 109/L), blood potassium K (mmol/L), blood sodium Na (mmol/L), blood calcium Ca (mmol/L), creatinine Cr (umol/L), urea nitrogen Bun (mmol/L), mental disorder, peritoneal irritation, pleural effusion.
Optionally, the basic human body sign data further includes one or more of the following: demographic data (age, sex, cause of acute pancreatitis), baseline laboratory tests (white blood cells, hematocrit, platelets, potassium, sodium, calcium, creatinine, urea nitrogen, blood glucose levels Glu (mmol/L)), baseline vital signs (body temperature, systolic pressure, heart rate, respiratory rate), baseline physical signs (presence or absence of mental changes, peritoneal irritation, etc.), prognostic data (incidence of persistent organ dysfunction, incidence of local complications, incidence of infectious pancreatic necrosis, total days of hospitalization Hospital available (days), ICU admission rate ICU administration, mortality Die, etc.), etc.
In one embodiment, the sample comprises: 930 patients with acute pancreatitis from the multicenter, prospective, random-control acute pancreatitis study database from month 5 in 2018 to month 4 in 2022. The study was approved by the ethical committee (ethical number ZS-1413). The inclusion criteria were: diagnosing acute pancreatitis, within 48 hours of onset, at the age of 18-75 years, and participating in the test with informed consent. Exclusion criteria were: the acute pancreatitis can be caused by tumor or ERCP operation after onset of over 48 hours, renal insufficiency, gestational or lactation period, hypertension with poor control, cardiovascular disease, abnormal mind, and acute pancreatitis.
The research result shows that: the average age of 930 patients with acute pancreatitis was 48.6 ± 6.0; the male patient ratio was 62.7% (586/930); causes of acute pancreatitis include: biliary =383, lipogenic =291, alcoholic =65, other =191; body temperature of 37.1 +/-1.05; blood pressure was 130.3 ± 68.0.
102: inputting the human body basic sign data into a pre-trained prognosis prediction model to obtain a first classification result;
in one embodiment, the prognostic prediction model includes: the training method of the pancreatitis grading prediction model comprises the following steps:
acquiring human body basic sign data and classification labels of a training set sample, wherein the labels comprise mild pancreatitis, moderate severe pancreatitis and severe pancreatitis; symptoms of the mild pancreatitis MAP include: no systemic or local complications, no organ failure; symptoms of the moderate-severe pancreatitis MSAP include: with systemic or local complications, the organ failure is transient, i.e. less than or equal to 48h; symptoms of severe pancreatitis SAP include: persistent organ failure; the data set was randomly divided into a training set and a test set at a ratio of 4 to 1.
Carrying out feature extraction on the human body basic sign data of the training set sample to obtain human body basic sign data features after feature extraction;
and constructing a model by using the human body basic sign data characteristics to obtain a constructed pancreatitis grading prediction model.
In one embodiment, the training method of the pancreatitis grading prediction model further comprises the following steps:
sequencing the human body basic sign data characteristics to obtain human body basic sign data characteristics after sequencing;
performing model construction by using the sorted human body basic sign data characteristics to obtain a constructed pancreatitis grading prediction model;
optionally, the sorting process includes:
for one of the human body basic sign data features after feature extraction, calculating all combinations and marginal contribution of a single human body basic sign data feature in the combinations according to a combination sequence formed by the human body basic sign data features;
obtaining the contribution degree of a single human body basic sign data characteristic in the human body basic sign data characteristics according to the marginal contribution;
sorting the human body basic sign data features based on the contribution degrees, and outputting N human body basic sign data features with high contribution degrees; said N is at least 5.
In the process of constructing the pancreatitis grading prediction model, basic sign data of a human body are sorted according to the contribution degree from high to low, and the result is as follows: multiplex effect, HR, cr, RR, ca, glu, wbc, na, plt, BUN, age, temporal, GCS =15, period authentication, sbp, k, etiology, hct, gene, as shown in FIG. 13. During pancreatitis grading prediction, the AUC modeled by using the most important 5 characteristics of HR, ca, glu, RR and temperature is 0.792, and the AUC modeled by using the most important 10 characteristics of HR, ca, glu, RR, temperature, cr, wbc, BUN, k and plt is 0.836;
in one embodiment, the first classification result includes: mild pancreatitis, moderate severe pancreatitis, and severe pancreatitis;
in one embodiment, the basic physical sign data of the human body is input into a pre-trained pancreatitis grading prediction model to obtain a first classification result of the mild pancreatitis, the moderate severe pancreatitis and the severe pancreatitis.
In one embodiment, the prognostic classifier further includes: whether or not to die;
the prognostic prediction model further includes: a death prediction model;
the training method of the death prediction model comprises the following steps:
acquiring human body basic sign data and classification labels of a training set sample, wherein the labels comprise death and non-death;
carrying out feature extraction on the human body basic sign data of the training set sample to obtain human body basic sign data features after feature extraction;
constructing a model by using the human body basic sign data characteristics to obtain a constructed death prediction model;
optionally, the training method of the death prediction model further includes: sequencing the human body basic sign data characteristics to obtain human body basic sign data characteristics subjected to sequencing; carrying out model construction by using the sorted human body basic sign data characteristics to obtain a constructed death prediction model; when whether the death is caused is predicted, the AUC modeled by using the most important 5 characteristics of Ca, HR, wbc, BUN and etiology is 0.911, and the AUC modeled by using the most important 10 characteristics of Ca, HR, wbc, BUN, etiology, sbp, cr, age, glu and Na is 0.949;
in one embodiment, the human body basic sign data is input into a pre-trained death prediction model to obtain a first classification result of death and non-death.
Optionally, the prognostic classification index further includes: whether or not to infect necrosis;
the prognostic prediction model further includes: an infectious necrosis prediction model;
the training method of the infectious necrosis prediction model comprises the following steps:
acquiring human body basic sign data and classification labels of a training set sample, wherein the labels comprise infection necrosis and non-infection necrosis;
carrying out feature extraction on the human body basic sign data of the training set sample to obtain human body basic sign data features after feature extraction;
constructing a model by using the human body basic sign data characteristics to obtain a constructed infection necrosis prediction model;
optionally, the method for training the infectious necrosis prediction model further includes: sequencing the human body basic sign data characteristics to obtain human body basic sign data characteristics subjected to sequencing; performing model construction by using the sorted human body basic sign data characteristics to obtain a constructed infectious necrosis prediction model; when whether the infection necrosis is predicted, the AUC modeled by the most important 5 characteristics of glu, HR, ca, plt and age is 0.776, and the AUC modeled by the most important 10 characteristics of glu, HR, ca, plt, age, wbc, hct, BUN, temperature and RR is 0.825;
in one embodiment, the human body basic sign data is input into a pre-trained infectious necrosis prediction model to obtain a first classification result of infectious necrosis and non-infectious necrosis.
Optionally, the prognostic classification index further includes: grading the length of hospitalization;
optionally, the prognostic prediction model further includes: a hospitalization duration prediction model;
the training method of the stay duration prediction model comprises the following steps:
acquiring human body basic sign data and classification labels of a training set sample, wherein the labels comprise a first hospitalization duration and a second hospitalization duration; the first length of stay is less than or equal to the classification threshold, and the second length of stay is greater than or equal to the classification threshold; the classification threshold is the first 24h of admission, and the later prognosis of the patient is predicted by using the data of the first 24 h;
carrying out feature extraction on the human body basic sign data of the training set sample to obtain human body basic sign data features after feature extraction;
constructing a model by using the human body basic sign data characteristics to obtain a constructed hospitalization duration prediction model;
optionally, the training method of the stay duration prediction model further includes: sequencing the human body basic sign data characteristics to obtain human body basic sign data characteristics subjected to sequencing; performing model construction by using the sorted human body basic sign data characteristics to obtain a constructed hospitalization duration prediction model; when the hospital stay number grading is predicted, the AUC modeled by using the most important 5 characteristics of Ca, glu, wbc, plt and HR is 0.790, and the AUC modeled by using the most important 10 characteristics of Ca, glu, wbc, plt, HR, hct, temperature, na, BUN and age is 0.807;
in one embodiment, the human body basic sign data is input into a pre-trained hospital stay prediction model, and a first classification result of a first hospital stay and a second hospital stay is obtained.
Optionally, the prognostic classifier further includes: whether to live in the ICU;
optionally, the prognostic prediction model further includes: an ICU prediction model;
the ICU prediction model training method comprises the following steps:
acquiring human body basic sign data and classification labels of a training set sample, wherein the labels comprise an entrance ICU and an exit ICU;
carrying out feature extraction on the human body basic sign data of the training set sample to obtain human body basic sign data features after feature extraction;
constructing a model by using the human body basic sign data characteristics to obtain a constructed ICU prediction model;
optionally, the method for training the ICU prediction model further includes: sequencing the human body basic sign data characteristics to obtain human body basic sign data characteristics subjected to sequencing; performing model construction by using the sorted human body basic sign data characteristics to obtain a constructed ICU prediction model; when predicting whether to live in the ICU, the AUC modeled by the most important 5 characteristics glu, RR, HR, ca and plt is 0.863, and the AUC modeled by the most important 10 characteristics glu, RR, HR, ca, plt, cr, wbc, temperature, na and BUN is 0.887.
In one embodiment, the human body basic sign data is input into a pre-trained ICU prediction model to obtain a first classification result of an entering ICU and a non-entering ICU.
In one embodiment, a machine learning method is used for model construction of the human body basic sign data to obtain a constructed prognosis prediction model;
optionally, the machine learning method includes one or more of the following: linear regression, logistic regression, linear Discriminant Analysis (LDA), classification and regression trees, naive bayes, KNN, learning vector quantization, support Vector Machines (SVMs), random forests, lightGBM;
optionally, the machine learning method includes: weighted fusion of any two algorithms;
optionally, the machine learning method includes: weighted fusion of random forest and LightGBM.
In one embodiment, the study uniformly employs 5-rule cross validation, targeting average AUC, and using Optuna based bayesian probabilities for hyper-parametric adjustment of all models. First, the support vector machine is a classical and efficient method that aims to find an optimal hyperplane to classify a data set. Modeling was attempted by adjusting gamma and dependency term using kernel functions including linear, sigmoid, polymodal and radial basis functions. Secondly, logistic regression is a simple, effective and well-interpretable method, data are assumed to obey Bernoulli distribution, sigmoid is introduced on the basis of a linear model, and a gradient descent method is adopted to update parameters, so that data classification is realized. We try to model by selecting different regularization functions, setting different regularization coefficients, residual convergence conditions, and maximum number of iterations. Thirdly, the decision tree is a basic method of various tree models, simulates human decision judgment ideas, and simply and intuitively carries out decision classification according to characteristic attribution. And performing characteristic division by comparing the information entropy and the gini coefficient, and constructing a model by adopting different tree depths, the minimum sample number of leaf nodes and the minimum impurity degree reduction value. And fourthly, the random forest is a bagging ensemble learning method taking a decision tree as a sub-model, training samples are randomly extracted by adopting a bootstrap sample method to train each sub-model, and each sub-model votes to obtain a final classification result. In addition to basic sub-model superparameters including maximum tree depth and the like, we also try to adjust ensemble superparameters such as the number of sub-models. Fifthly, the LightGBM is a lightweight and efficient improved model based on (GBDT), which adopts histogram algorithm to carry out feature discretization, adopts Leaf-wise to inhibit Leaf growth, and enhances the robustness and generalization capability of the algorithm while improving the algorithm speed. The hyper-parameters of the LightGBM to be adjusted mainly comprise maximum tree depth, maximum leaf node number, learning rate, regularization parameters, iteration times, minimum splitting gain, maximum histogram bin and the like. And sixthly, fusing the random forest and the LightGBM with excellent comprehensive performance by adopting a soft voting fusion method with weight to generate an optimal classification model.
The accuracy of the machine learning algorithm is evaluated by combining the area AUC under the Receiver Operating Characteristic (ROC) curve with the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value. The belibration belt verifies the consistency of the model output probability and the true probability. The interpretability of the model and the contribution of each parameter to the classification are very important in clinical research, and a general machine learning interpretation method ShapleAddidative evolution (SHAP) is adopted to perform characteristic contribution analysis on the optimal model of each endpoint. For the tree model, we used a variant TreeSHAP of fast and efficient SHAP for analysis.
In one embodiment, a weighted fusion model of random forest and LightGBM is specifically used; for the patient parameter x input to the model, the output classification result y of the model is shown as
w 1 And w 2 Fusion weights of the random forest and the LightGBM are respectively; for random forests, it is composed of N 1 And each base learner DT is trained by different data subsets to perform decision prediction on input, and all predicted values are averaged to obtain a prediction result. For the reaction of N 2 In the LIghtGBM formed by the base learners CART, the input of each base learner is firstly determined by the histogram function H to which the input belongs, then the corresponding predicted value is generated by the base learners, and the predicted value and the initial value y0 are summed to generate the predicted result. Fusing the random forest and the LightGBM through respective weights to obtain a final fusion classification result;
optionally, the initial value y0 may be:
wherein
Is the average value of the training set labels;
optionally, the N is 1 The individual basis learner DT is: ID3, C4.5, CART; the N2 base learners CART is: a CART decision tree;
optionally, referring to fig. 5, the method is a method for performing calculation by using a random forest algorithm; referring to fig. 6, a method of performing a calculation using the LightGBM algorithm;
103: calculating the score value of a prognosis classification index based on the human body basic sign data, and obtaining a second classification result according to the score value;
in one embodiment, the score values include one or more of: APACHE-II score value, marshall score value, sofa score value, qsofa score value and bisap score value; the score value is calculated by adopting a public scoring rule, and when the existing index is not included in the public scoring rule, the index is directly abandoned; or according to the sorting result of sorting the human body basic sign data features based on the contribution degree, scoring the indexes which are not included in the public scoring rules according to the scoring rules of the indexes which are closest to the contribution degree; for example, when the a-index is not included in the disclosed scoring rules, the result from the ranking process: and if the index closest to the contribution degree of the index A is the index B, the scoring rule of the index A is consistent with the scoring rule of the index B.
In one embodiment, the prognostic classification indicators include: classifying pancreatitis; the second classification result comprises: mild pancreatitis, moderate severe pancreatitis, and severe pancreatitis. Calculating the score value of a prognosis classification index based on the human body basic sign data, and obtaining a second classification result of the mild pancreatitis, the moderate and severe pancreatitis and the severe pancreatitis according to the score value;
in one embodiment, the prognostic classifier further includes: whether or not to die; calculating the score value of a prognosis classification index based on the human body basic sign data, and obtaining a second classification result of death and non-death according to the score value;
in one embodiment, the prognostic classifier further includes: whether or not to infect necrosis; calculating the score value of a prognosis classification index based on the human body basic sign data, and obtaining a second classification result of infection necrosis and non-infection necrosis according to the score value;
in one embodiment, the prognostic classifier further includes: grading the hospitalization duration; calculating the score of a prognosis classification index based on the human body basic sign data, and obtaining a second classification result of the first hospitalization duration and the second hospitalization duration according to the score;
in one embodiment, the prognostic classifier further includes: whether to live in the ICU; and calculating the score value of the prognosis classification index based on the human body basic sign data, and obtaining a second classification result of the ICU of the patient who lives in and the ICU of the patient who does not live in according to the score value.
104: carrying out weighted fusion processing on the first classification result and the second classification result to obtain a final classification result;
in one embodiment, the process of weighted fusion processing is accomplished using algorithms of conventional modeling.
FIG. 2 isThe embodiment of the invention provides a pancreasApparatus for processing inflammation prognosis data, the apparatus comprising: a memory and a processor;
the memory is to store program instructions;
the processor is used for calling program instructions and executing the method for processing pancreatitis prognosis data when the program instructions are executed.
FIG. 3 is a schematic view ofThe system for processing pancreatitis prognosis data provided by the embodiment of the invention comprises:
an obtaining unit 301, configured to obtain human body basic sign data of a sample;
a first classification unit 302, configured to input the human basic sign data into a pre-trained prognosis prediction model to obtain a first classification result;
the second classification unit 303 is configured to calculate a score of a prognostic classification index based on the human body basic sign data, and obtain a second classification result according to the score; the prognostic classification indicators include: classifying pancreatitis;
a third classification unit 304, configured to perform weighted fusion processing on the first classification result and the second classification result to obtain a final classification result;
the prognostic prediction model includes: the training method of the pancreatitis grading prediction model comprises the following steps:
acquiring human body basic sign data and classification labels of a training set sample, wherein the labels comprise mild pancreatitis, moderate severe pancreatitis and severe pancreatitis;
carrying out feature extraction on the human body basic sign data of the training set sample to obtain human body basic sign data features after feature extraction;
and constructing a model by using the human body basic sign data characteristics to obtain a constructed pancreatitis grading prediction model.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method of processing pancreatitis prognosis data as described above.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by hardware related to instructions of a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include: read Only Memory (ROM), random Access Memory (RAM), magnetic or optical disks, and the like.
It will be understood by those skilled in the art that all or part of the steps in the method according to the above embodiments may be implemented by hardware that is related to instructions of a program, and the program may be stored in a computer-readable storage medium, where the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk.
While the invention has been described in detail with reference to specific embodiments thereof, it will be apparent to one skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.
Claims (10)
1. A method of processing pancreatitis prognostic data, comprising:
acquiring human body basic sign data of a sample;
inputting the human body basic sign data into a pre-trained prognosis prediction model to obtain a first classification result;
calculating the score value of a prognosis classification index based on the human body basic sign data, and obtaining a second classification result according to the score value; the prognostic classification indicators include: classifying pancreatitis;
carrying out weighted fusion processing on the first classification result and the second classification result to obtain a final classification result;
the prognostic prediction model includes: a pancreatitis grading prediction model; the training method of the pancreatitis grading prediction model comprises the following steps:
acquiring human body basic sign data and classification labels of a training set sample, wherein the labels comprise mild pancreatitis, moderate severe pancreatitis and severe pancreatitis;
carrying out feature extraction on the human body basic sign data of the training set sample to obtain human body basic sign data features after feature extraction; and constructing a model by using the human body basic sign data characteristics to obtain a constructed pancreatitis grading prediction model.
2. The method for processing pancreatitis prognostic data according to claim 1, wherein said score values include one or more of: APACHE-II score value, marshall score value, sofa score value, qsofa score value, bisap score value.
3. The method for processing pancreatitis prognosis data according to claim 1, wherein said method for training said pancreatitis grading prediction model further comprises:
sequencing the human body basic sign data characteristics to obtain human body basic sign data characteristics subjected to sequencing;
performing model construction by using the sorted human body basic sign data characteristics to obtain a constructed pancreatitis grading prediction model;
optionally, the sorting process includes:
for one of the human body basic sign data features after feature extraction, calculating all combinations and marginal contribution of a single human body basic sign data feature in the combinations according to a combination sequence formed by the human body basic sign data features;
obtaining the contribution degree of a single human body basic sign data characteristic in the human body basic sign data characteristic according to the marginal contribution;
sorting the human body basic sign data features based on the contribution degrees, and outputting N human body basic sign data features with high contribution degrees;
optionally, N is at least 5.
4. The method of processing pancreatitis prognostic data according to claim 1, wherein said first classification result includes: mild pancreatitis, moderate severe pancreatitis, and severe pancreatitis;
optionally, the second classification result includes: mild pancreatitis, moderate severe pancreatitis, and severe pancreatitis.
5. The method of processing pancreatitis prognostic data according to claim 1, wherein said prognostic classifier further comprises: whether or not to die;
the prognostic prediction model further includes: a death prediction model;
the training method of the death prediction model comprises the following steps:
acquiring human body basic sign data and classification labels of a training set sample, wherein the labels comprise death and non-death;
carrying out feature extraction on the human body basic sign data of the training set sample to obtain human body basic sign data features after feature extraction;
constructing a model by using the human body basic sign data characteristics to obtain a constructed death prediction model;
optionally, the prognostic classifier further includes: whether or not to infect necrosis;
the prognostic prediction model further includes: an infectious necrosis prediction model;
the method for training the infectious necrosis prediction model comprises the following steps:
acquiring human body basic sign data and classification labels of a training set sample, wherein the labels comprise infection necrosis and non-infection necrosis;
carrying out feature extraction on the human body basic sign data of the training set sample to obtain human body basic sign data features after feature extraction;
constructing a model by using the human body basic sign data characteristics to obtain a constructed infection necrosis prediction model;
optionally, the prognostic classification index further includes: grading the length of hospitalization;
optionally, the prognostic prediction model further includes: a hospitalization duration prediction model;
the training method of the stay in hospital prediction model comprises the following steps:
acquiring human body basic sign data and classification labels of a training set sample, wherein the labels comprise a first hospitalization duration and a second hospitalization duration; the first length of stay is less than or equal to the classification threshold, and the second length of stay is greater than or equal to the classification threshold;
carrying out feature extraction on the human body basic sign data of the training set sample to obtain human body basic sign data features after feature extraction;
constructing a model by using the human body basic sign data characteristics to obtain a constructed hospitalization duration prediction model;
optionally, the prognostic classifier further includes: whether to live in the ICU;
optionally, the prognostic prediction model further includes: an ICU prediction model;
the ICU prediction model training method comprises the following steps:
acquiring human body basic sign data and classification labels of a training set sample, wherein the labels comprise an entrance ICU and an exit ICU;
carrying out feature extraction on the human body basic sign data of the training set sample to obtain human body basic sign data features after feature extraction;
and constructing a model by using the human body basic sign data characteristics to obtain a constructed ICU prediction model.
6. The method for processing pancreatitis prognostic data according to claim 1, wherein said human basic signs data include one or more of: age, sex, etiology, body temperature, blood pressure, heart rate, respiratory rate, white blood cells, hematocrit, platelets, blood potassium, blood sodium, blood calcium, creatinine, urea nitrogen, adequacy score, peritoneal irritation, pleural effusion.
7. The method for processing pancreatitis prognostic data according to claim 1, wherein a machine learning method is used to model the basic human body sign data to obtain a well-constructed prognosis prediction model;
optionally, the machine learning method includes one or more of the following: linear regression, logistic regression, linear Discriminant Analysis (LDA), classification and regression trees, naive bayes, KNN, learning vector quantization, support Vector Machines (SVMs), random forests, lightGBM;
optionally, the machine learning method includes: weighted fusion of any two algorithms;
optionally, the machine learning method includes: weighted fusion of random forest and LightGBM.
8. An apparatus for processing pancreatitis prognosis data, the apparatus comprising: a memory and a processor;
the memory is to store program instructions;
the processor is configured to invoke program instructions for performing, when executed, the method of processing pancreatitis prognosis data as set forth in any one of claims 1-7.
9. A system for processing pancreatitis prognosis data, comprising:
the acquisition unit is used for acquiring human body basic sign data of the sample;
the first classification unit is used for inputting the human body basic sign data into a pre-trained prognosis prediction model to obtain a first classification result;
the second classification unit is used for calculating the score value of a prognosis classification index based on the human body basic sign data and obtaining a second classification result according to the score value; the prognostic classification indicators include: classifying pancreatitis;
the third classification unit is used for performing weighted fusion processing on the first classification result and the second classification result to obtain a final classification result;
the prognostic prediction model includes: the training method of the pancreatitis grading prediction model comprises the following steps:
acquiring human body basic sign data and classification labels of a training set sample, wherein the labels comprise mild pancreatitis, moderate severe pancreatitis and severe pancreatitis;
carrying out feature extraction on the human body basic sign data of the training set sample to obtain human body basic sign data features after feature extraction;
and constructing a model by using the human body basic sign data characteristics to obtain a constructed pancreatitis grading prediction model.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method of processing pancreatitis prognosis data as set forth in any one of the preceding claims 1 to 7.
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