WO2022083140A1 - Procédé et appareil de prédiction de longueur de séjour de patient, dispositif électronique et support de stockage - Google Patents

Procédé et appareil de prédiction de longueur de séjour de patient, dispositif électronique et support de stockage Download PDF

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WO2022083140A1
WO2022083140A1 PCT/CN2021/099644 CN2021099644W WO2022083140A1 WO 2022083140 A1 WO2022083140 A1 WO 2022083140A1 CN 2021099644 W CN2021099644 W CN 2021099644W WO 2022083140 A1 WO2022083140 A1 WO 2022083140A1
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prediction
training
data
prediction model
classification
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PCT/CN2021/099644
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English (en)
Chinese (zh)
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吴静依
李鹏飞
李青
张路霞
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杭州未名信科科技有限公司
浙江省北大信息技术高等研究院
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Publication of WO2022083140A1 publication Critical patent/WO2022083140A1/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/245—Classification techniques relating to the decision surface
    • G06F18/2451—Classification techniques relating to the decision surface linear, e.g. hyperplane
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H70/00—ICT specially adapted for the handling or processing of medical references
    • G16H70/20—ICT specially adapted for the handling or processing of medical references relating to practices or guidelines

Definitions

  • the present application relates to the technical field of data processing, and in particular to a method, device, electronic device and storage medium for predicting the length of hospitalization of a patient.
  • the length of hospital stay is a key indicator for evaluating the efficiency of medical resource utilization.
  • the intelligent length of stay prediction system can assist clinicians to identify patients with high disease risk and provide timely medical intervention, thereby improving the patient’s hospitalization prognosis; it can also assist doctors in making reasonable arrangements Limited medical resources maximize the utilization efficiency of medical resources; it can also provide patients and their families with information about the length of stay in the early stage of admission, so that patients and their families can learn more about their illness and possible hospitalization. information, thereby improving patient satisfaction with medical services and reducing doctor-patient conflicts caused by information asymmetry.
  • kidney disease is a group of common chronic diseases caused by kidney damage caused by various primary kidney diseases, diabetes and hypertension.
  • my country's kidney disease medical and health system urgently needs to combine an intelligent clinical decision support system to improve medical efficiency and improve patient prognosis.
  • the existing hospitalization length prediction of patients is generally based on the clinician's work experience. Due to the complexity of the patient's condition, the subjectivity of the doctor's work experience is too high. The prediction of the patient's hospitalization length is difficult, the analysis efficiency is low, the accuracy rate is low, and it cannot be effective. Assist doctors in clinical decision-making and improve medical efficiency.
  • the prediction model of the length of hospitalization that is accurate to the number of days often has a large error. Converting the prediction of hospitalization length from a numerical prediction problem to an ordered multi-classification prediction problem, the differences in patient characteristics between each classification group are more typical, which can improve the prediction accuracy of the model, and the classification results can provide enough information for clinical decision-making Support consultation with patients.
  • ordered multi-classification problems are generally solved based on numerical prediction models or disordered multi-classification prediction models:
  • Numerical prediction models assume that multiple categories of outcome variables follow an proportional correlation, while in real-world ordinal multi-classification data Multiple categories often do not follow a strict proportional relationship; the disordered multi-category prediction model directly ignores the progressive relationship between the categories of the ordered multi-category outcome variable, and the performance of the prediction model is often limited to a certain extent.
  • the unordered multi-category prediction model will produce large prediction errors.
  • the purpose of the present application is to provide a method, device, electronic device and storage medium for predicting the length of a patient's hospital stay.
  • a brief summary is given below. This summary is not intended to be an extensive review, nor is it intended to identify key/critical elements or delineate the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the detailed description that follows.
  • a method for predicting the length of hospitalization of a patient including:
  • the samples to be predicted are selected and input into the trained prediction model to obtain the prediction result.
  • the prediction method further includes:
  • training data is extracted to form a training data set.
  • the prediction method further includes:
  • the selected predictive features are supplemented and adjusted to obtain preset predictive features.
  • performing data cleaning includes:
  • the binary classification base learner is a gradient boosting decision tree algorithm.
  • the use of the training data set to train each of the two-class base learners until each of the two-class base learners meets performance index requirements including:
  • step S2 determine whether m ⁇ M; if so, go to step S3; if not, skip to step S7;
  • the random hyperparameter search combined with the five-fold cross-validation method is used to realize the hyperparameter optimization of each basic learner, and the F1 score is used as the reference index of the model prediction performance of the hyperparameter optimization.
  • the prediction method also includes:
  • the prediction model is updated periodically and synchronously.
  • a device for predicting hospitalization length of a patient including:
  • the building module is used to construct an ordered multi-class prediction model by cascading and concatenating multiple binary classification base learners;
  • a training module used for training each of the basic learners by using the training data set until each of the basic learners meets the performance index requirements, and obtains a trained prediction model
  • the prediction module is used for selecting samples to be predicted and inputting the trained prediction model according to the preset prediction features to obtain prediction results.
  • an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the program, In order to achieve the above-mentioned method of predicting the length of hospitalization of patients.
  • a computer-readable storage medium on which a computer program is stored, and the program is executed by a processor to implement the above-mentioned method for predicting the length of hospitalization of a patient.
  • an ordered and multi-classified prediction model is constructed by cascading and concatenated multiple binary base learners, and the ordered and multi-classified prediction task is divided into several layer-by-layer steps.
  • each layer has a base learner, and the information of the samples to be predicted is input into each trained base learner layer by layer to obtain the predicted category, and the sequence between the categories in the ordered multi-category outcome variable is preserved.
  • Progressive relationship and does not assume a proportional relationship between ordered categories, which is more in line with the characteristics of real data.
  • FIG. 1 shows a flowchart of a method for predicting hospitalization length of a patient according to an embodiment of the present application
  • Fig. 2 shows the training process flow chart of the basic learner in an embodiment of the present application
  • FIG. 3 shows a flowchart of selecting a sample to be predicted and inputting a trained prediction model to obtain a prediction result in an embodiment of the present application
  • FIG. 4 shows a structural block diagram of an apparatus for predicting hospitalization length of a patient provided by another embodiment of the present application
  • FIG. 5 shows a structural block diagram of an electronic device provided by another embodiment of the present application.
  • FIG. 6 shows a structural block diagram of an intelligent prediction system for the length of stay of a patient with kidney disease provided by another embodiment of the present application.
  • an embodiment of the present application provides a method for predicting the length of hospitalization of a patient, including the following steps:
  • a patient with renal disease is used as an example.
  • the method in this embodiment is not limited to being used for patients with renal disease, but can also be used for predicting the length of hospitalization for patients with other diseases.
  • Based on the electronic medical record data of kidney disease patients in the hospital information management system after data cleaning, effective modeling data is extracted. Modeling data is the training data used to train the base learner.
  • a certain number of predictive features with high predictive value and easy to collect in clinical practice are selected from the modeling database to form a feature subset for modeling.
  • a predictive feature set is extracted from the electronic medical record data in the hospital information management system, wherein the predictive feature set includes: demographic features, kidney disease features, medical treatment features, general disease features, laboratory test index features, etc.
  • Demographic characteristics include: age, gender, marital status, occupation, education level, medical insurance type and other parameter data;
  • kidney disease includes: chronic kidney disease stage, primary disease of kidney disease, years of diagnosis of kidney disease and other parameter data;
  • the characteristics of medical treatment include: type of medical institution, number of hospitalizations, admission status, admission route, admission department and other parameter data;
  • General disease characteristics include: the cause of admission, whether there is comorbidity (diabetes, hypertension, tumor, chronic obstructive pulmonary disease, pulmonary infection, cardiovascular disease, cerebrovascular disease, chronic liver disease) and other parameter data;
  • Laboratory test index characteristics include: blood routine, urine routine, urine protein/creatinine, serum creatinine, blood glucose, blood lipids, electrolytes, serum calcium, serum phosphorus, parathyroid hormone and other parameter data.
  • a recursive feature elimination algorithm was used to screen out a certain number of predictive feature subsets with high predictive value for the length of stay in patients with renal disease; secondly, combined with expert knowledge, the selected predictive feature subsets were supplemented and adjusted.
  • the feature selection combining expert knowledge and feature screening algorithm is beneficial to ensure the accuracy of screening features and the feasibility of clinical practice. Feature screening can reduce the complexity of predictive models and facilitate clinical practice.
  • a multi-class prediction model is constructed by cascading and concatenating multiple binary base learners.
  • the length of hospitalization of patients with renal disease is divided into M categories in order from low to high, and the predicted feature subset screened in step S2 is used as the input of the prediction model, and the cascaded layer-by-layer modeling algorithm is used, Using the gradient boosting decision tree algorithm as the base learner, a prediction model for the length of stay in patients with kidney disease was constructed; among them, the hyperparameter optimization of each base learner used random hyperparameter search combined with five-fold cross-validation method, and F1 score was used as hyperparameter search. A reference indicator of optimal model prediction performance.
  • the basic structure of the cascaded layer-by-layer modeling algorithm in this embodiment adopts a multi-level integrated architecture, which is composed of multiple binary classification base learners connected in series. Each layer trains a base learner respectively.
  • the prediction model contains M-1 base learners. M is the number of classification categories of the prediction model.
  • the M categories of outcome variables are arranged in increasing order.
  • the training data subset is the data of y ⁇ mth category.
  • y is an outcome variable containing an ordered M classification, and the M categories of the outcome variable are arranged in increasing order to obtain the first category ⁇ the second category ⁇ ... ⁇ mth category ⁇ ... ⁇ Mth category;
  • x represents the set of predicted features for the training samples.
  • the training process of the base learner includes the following steps:
  • the random hyperparameter search combined with the five-fold cross-validation method is used to realize the hyperparameter optimization of each basic learner, and the F1 score is used as the reference index of the model prediction performance of the hyperparameter optimization.
  • the information of newly admitted patients is input into the hospitalization length prediction model, and the prediction results are obtained, and the prediction results and diagnosis and treatment suggestions are displayed visually.
  • step S5 specifically includes:
  • step S54 determine whether m is equal to M: if yes, then the final prediction category of the sample is the Mth category, and skip to step S55; if not, return to step S52;
  • the hospitalization duration prediction model is updated synchronously on a regular basis.
  • the modeling data is updated based on the system data of the past three years at the end of each year, and a new hospitalization length prediction model is constructed according to the method described in step S3, and the updated hospitalization length prediction model is used to replace the historical prediction. model, thereby realizing regular synchronous updates to the hospital length prediction model.
  • the method for predicting the length of stay of a patient in the embodiment of the present application is based on a cascaded layer-by-layer modeling algorithm based on ordered multi-classification prediction, adopts a multi-level integrated architecture, and is formed by cascading a plurality of basic learners, and is suitable for ordering There are multiple categories and the categories do not follow the proportional relationship or there is a data imbalance between the categories.
  • the method provided by the embodiment of the present application divides the ordered multi-category prediction task into several progressive binary classification tasks, each layer trains a basic learner, and the information of the new sample to be predicted is input layer by layer Each trained base learner until its predicted class is obtained and output.
  • the cascaded layer-by-layer modeling algorithm retains the sequential progressive relationship between the categories in the ordered multi-category outcome variable, and does not assume the proportional relationship between the ordered categories, which is more in line with the real data characteristics.
  • the data of the two categories in the data set used for training each layer of the base learner is relatively balanced, which can effectively solve the problem of data imbalance between multiple categories.
  • FIG. 4 another embodiment of the present application provides a device for predicting the length of hospitalization of a patient, including:
  • the building module 30 is used for constructing an ordered multi-classification prediction model by cascading and concatenating a plurality of binary classification base learners;
  • a training module 40 configured to train each of the basic learners by using the training data set until each of the basic learners meets the performance index requirements, and obtain a trained prediction model
  • the prediction module 50 is configured to select the samples to be predicted and input the trained prediction model according to the preset prediction feature to obtain the prediction result.
  • the prediction device further includes a data extraction module 10 for performing data cleaning based on the patient's electronic medical record data in the hospital information management system before using the training data set to train each basic learner, and extracting the training data to form a training dataset.
  • the prediction device further includes a prediction feature acquisition module 20, which is used to select samples to be predicted according to preset prediction features and input them into the trained prediction model,
  • the selected predictive features are supplemented and adjusted to obtain preset predictive features.
  • the data extraction module 10 includes a cleaning unit for performing data cleaning, and the cleaning unit is specifically used for:
  • the binary classification base learner is a gradient boosting decision tree algorithm.
  • the training module 40 is specifically used to:
  • step S12 determine whether m ⁇ M; if yes, go to step S13; if not, go to step S17;
  • the training module 40 is further configured to use random hyperparameter search combined with a five-fold cross-validation method to realize the hyperparameter optimization of each basic learner, and use the F1 score as a reference index of the model prediction performance for hyperparameter optimization. .
  • the prediction apparatus further includes an update module 60, and the update module 60 is configured to periodically update the prediction model synchronously based on the update of the electronic medical record data in the hospital information management system.
  • the electronic device 70 may include: a processor 700, a memory 701, a bus 702 and a communication interface 703, the processor 700, the communication interface 703 and the memory 701 are connected through the bus 702; the memory 701
  • a computer program that can be run on the processor 700 is stored in the computer, and when the processor 700 runs the computer program, the method for predicting the length of hospitalization of a patient provided by any of the foregoing embodiments of the present application is executed.
  • Another embodiment of the present application provides a computer-readable storage medium on which a computer program is stored, and the program is executed by a processor to implement the above-mentioned method for predicting the length of hospitalization of a patient.
  • FIG. 6 another embodiment of the present application provides an intelligent prediction system for the length of hospitalization of patients with renal disease, including:
  • An input module at least for entering information on newly admitted kidney disease patients
  • a prediction module which is at least used for the prediction model of the length of stay of kidney disease patients constructed and trained by the aforementioned method, and predicts the length of stay in the hospital for the data of the newly admitted patient;
  • a display module at least used to display the visual prediction results.
  • the method for predicting the length of stay of a patient in the embodiment of the present application can achieve the following beneficial effects: the cascaded layer-by-layer modeling algorithm based on ordered multi-classification
  • the sequential progressive relationship between categories, and does not assume the proportional relationship between the ordered categories, is more in line with the real data characteristics; by splitting the data set layer by layer, the data set used for each layer of base learner training is made.
  • the data is relatively balanced, which can effectively solve the problem of data imbalance between multiple categories.
  • the present disclosure mines the patient data collected by the hospital electronic case data management system based on the cascaded layer-by-layer modeling algorithm, and uses the gradient boosting decision tree algorithm as the base learner to construct a patient-oriented hospitalization duration prediction model and system.
  • the method, apparatus, electronic device, and computer-readable storage medium provided by the embodiments of the present application are not only limited to predicting the length of hospitalization for patients with kidney disease, but can also be widely used for predicting the length of hospitalization for patients with other diseases.
  • module is not intended to be limited to a particular physical form. Depending on the specific application, a module may be implemented in hardware, firmware, software, and/or a combination thereof. Furthermore, different modules can share common components or even be implemented by the same components. There may or may not be clear boundaries between different modules.

Abstract

Sont divulgués dans la présente demande un procédé et un appareil de prédiction de longueur de séjour de patient, un dispositif électronique et un support de stockage. Le procédé consiste : à construire un modèle de prédiction multiclassification ordonné au moyen d'une concaténation en cascade d'une pluralité de systèmes d'apprentissage de base de classification binaire ; à entraîner chaque système d'apprentissage de base à l'aide d'un ensemble de données d'entraînement jusqu'à ce que chaque système d'apprentissage de base réponde aux exigences d'indice de performance pour obtenir un modèle de prédiction entraîné ; et en fonction d'une caractéristique de prédiction prédéfinie, à sélectionner un échantillon à prédire et à entrer ce dernier dans le modèle de prédiction entraîné pour obtenir un résultat de prédiction. Selon le procédé de la présente demande, le modèle de prédiction multiclassification ordonné est construit au moyen d'une concaténation en cascade de la pluralité de systèmes d'apprentissage de base de classification binaire ; une relation progressive de séquence entre des catégories dans des variables de résultat multiclassification ordonnées est réservée, et les catégories ordonnées ne sont pas supposées être une relation géométriquement proportionnelle, ce qui permet de répondre plus à des caractéristiques de données réelles ; l'ensemble de données est divisé couche par couche, de sorte que les données de deux catégories dans l'ensemble de données permettant d'entraîner chaque système d'apprentissage de base sont relativement équilibrées, ce qui permet de résoudre efficacement le problème de déséquilibre entre des données multicatégorie et d'améliorer la précision du résultat de prédiction.
PCT/CN2021/099644 2020-10-22 2021-06-11 Procédé et appareil de prédiction de longueur de séjour de patient, dispositif électronique et support de stockage WO2022083140A1 (fr)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115831300A (zh) * 2022-09-29 2023-03-21 广州金域医学检验中心有限公司 一种基于患者信息的检测方法、装置、设备及介质
CN116434893A (zh) * 2023-06-12 2023-07-14 中才邦业(杭州)智能技术有限公司 混凝土抗压强度预测模型、构建方法、介质、电子设备
CN117472789A (zh) * 2023-12-28 2024-01-30 成都工业学院 基于集成学习的软件缺陷预测模型构建方法和装置

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112365943A (zh) * 2020-10-22 2021-02-12 杭州未名信科科技有限公司 患者住院时长的预测方法、装置、电子设备及存储介质
CN113393939A (zh) * 2021-04-26 2021-09-14 上海米健信息技术有限公司 重症监护室患者住院天数预测方法及系统
CN113197578B (zh) * 2021-05-07 2023-06-09 天津医科大学 一种基于多中心模型的精神分裂症分类方法及系统
CN117894481A (zh) * 2024-03-15 2024-04-16 长春大学 基于贝叶斯超参数优化梯度提升树心脏病预测方法及装置

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106202883A (zh) * 2016-06-28 2016-12-07 成都中医药大学 一种基于大数据分析建立疾病云图的方法
CN107403198A (zh) * 2017-07-31 2017-11-28 广州探迹科技有限公司 一种基于级联分类器的官网识别方法
CN108231146A (zh) * 2017-12-01 2018-06-29 华南师范大学 一种基于深度学习的医疗记录模型构建方法、系统及装置
US20190357797A1 (en) * 2018-05-28 2019-11-28 The Governing Council Of The University Of Toronto System and method for generating visual identity and category reconstruction from electroencephalography (eeg) signals
CN112365943A (zh) * 2020-10-22 2021-02-12 杭州未名信科科技有限公司 患者住院时长的预测方法、装置、电子设备及存储介质

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107103198A (zh) * 2017-04-26 2017-08-29 上海联影医疗科技有限公司 医学数据处理方法、装置及设备

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106202883A (zh) * 2016-06-28 2016-12-07 成都中医药大学 一种基于大数据分析建立疾病云图的方法
CN107403198A (zh) * 2017-07-31 2017-11-28 广州探迹科技有限公司 一种基于级联分类器的官网识别方法
CN108231146A (zh) * 2017-12-01 2018-06-29 华南师范大学 一种基于深度学习的医疗记录模型构建方法、系统及装置
US20190357797A1 (en) * 2018-05-28 2019-11-28 The Governing Council Of The University Of Toronto System and method for generating visual identity and category reconstruction from electroencephalography (eeg) signals
CN112365943A (zh) * 2020-10-22 2021-02-12 杭州未名信科科技有限公司 患者住院时长的预测方法、装置、电子设备及存储介质

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115831300A (zh) * 2022-09-29 2023-03-21 广州金域医学检验中心有限公司 一种基于患者信息的检测方法、装置、设备及介质
CN115831300B (zh) * 2022-09-29 2023-12-29 广州金域医学检验中心有限公司 一种基于患者信息的检测方法、装置、设备及介质
CN116434893A (zh) * 2023-06-12 2023-07-14 中才邦业(杭州)智能技术有限公司 混凝土抗压强度预测模型、构建方法、介质、电子设备
CN116434893B (zh) * 2023-06-12 2023-08-29 中才邦业(杭州)智能技术有限公司 混凝土抗压强度预测模型、构建方法、介质、电子设备
CN117472789A (zh) * 2023-12-28 2024-01-30 成都工业学院 基于集成学习的软件缺陷预测模型构建方法和装置
CN117472789B (zh) * 2023-12-28 2024-03-12 成都工业学院 基于集成学习的软件缺陷预测模型构建方法和装置

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