Methods and devices for predicting treatment outcomes of non-surgical treatment of ectopic pregnancy

Abstract

This application discloses a method and apparatus for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy, relating to the field of ectopic pregnancy treatment prediction technology. The method includes: predicting the success / failure probability of expectant management or minimally invasive treatment (MTX) based on current clinical characteristics and treatment decisions using the CatBoost algorithm, and outputting a ranking of feature contributions to support clinical interpretability. This solves the problems of existing prediction models that rely on complete historical sequences, cannot adapt to missing or non-uniform sampling of real-world medical data, ignore dynamic interactions between indicators, and produce static and singular prediction results.

Description

Technical Field

[0001] This application relates to the field of ectopic pregnancy treatment prediction technology, and in particular to a method and device for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy. Background Technology

[0002] Current clinical prediction methods mostly employ traditional statistical approaches (such as logistic regression). This proposed approach collects clinical characteristics of patients at their first visit or baseline (such as age, initial β-hCG, mass size, etc.), uses univariate and multivariate regression analyses to screen independent predictors, and constructs a linear static prediction model to assess the probability of success of non-surgical treatment or the trend of indicator changes.

[0003] However, this solution has many technical flaws: (1) Limitation of strong linearity assumption: Logistic regression assumes that there is a linear relationship between the predictor variables and the outcome, and that each parameter operates in a homogeneous manner, which cannot capture the nonlinear and high-order interaction features that are common in the evolution of TEP (Tubal Ectopic Pregnancy). (2) Static baseline dependence: It relies solely on the baseline data from the first visit and completely ignores the prognostic information implied by the dynamic changes of indicators during the diagnosis and follow-up process, which causes the prediction results to quickly become invalid when the condition fluctuates; (3) Weak generalization ability: It is sensitive to data distribution. When there are missing, noisy or non-uniform sampling in real-world medical data, the model performance drops significantly.

[0004] In recent years, some existing technologies have incorporated machine learning algorithms (such as decision trees, random forests, and support vector machines) or deep learning time series models (such as LSTM (Long Short-Term Memory) networks) for outcome prediction. These approaches typically integrate multiple patient visit records into a time series, inputting it into a recurrent neural network such as LSTM, utilizing its memory units to capture the impact of historical treatment information on the current outcome; or directly using tree ensemble algorithms to perform nonlinear classification and prediction of multimodal clinical data.

[0005] However, this solution still has the following technical shortcomings: (1) The time series model has too high requirements for data integrity: Deep time series networks such as LSTM rely on complete, continuous and uniformly sampled historical sequences. TEP is an obstetric and gynecological emergency with rapid disease progression, short follow-up period and irregular time intervals. In clinical practice, previous medical records are often missing due to patient referrals and cross-hospital visits. LSTM is unable to give full play to the advantages of long sequence modeling and is prone to training instability or prediction bias. (2) Risk of feature redundancy and overfitting: Directly inputting all historical medical records will introduce a lot of noise and redundant information, which is very easy to overfit with a limited sample size; (3) Not aligned with actual clinical workflow: Existing models mostly assume that physicians can obtain complete disease course data, and do not make lightweight designs for the real clinical scenario of "only the patient's latest medical record can be obtained when receiving a patient", resulting in insufficient usability and robustness of the algorithm when it is implemented in clinical practice.

[0006] In summary, existing predictive models rely on complete historical sequences, which cannot adapt to the lack of real-world medical data and non-uniform sampling. Furthermore, they ignore the dynamic interaction relationships between indicators, resulting in static and singular prediction results, which urgently need to be addressed. Summary of the Invention

[0007] This application provides a method and device for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy, in order to solve the problems in the existing technology that the prediction model relies on a complete historical sequence, cannot adapt to the lack of real-world medical data and non-uniform sampling, ignores the dynamic interaction relationship of indicators, and has static and singular prediction results.

[0008] The first aspect of this application provides a method for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy, comprising the following steps: acquiring complete clinical record information of multiple patients with tubal pregnancy to construct and preprocess a corresponding total training set, and extracting training clinical feature vectors of a training subset and validation clinical feature vectors of a validation subset from the preprocessed total training set; collecting the proposed non-surgical treatment plan categories corresponding to the training clinical feature vectors, and training a pre-constructed gradient boosting tree model using the training clinical feature vectors and the proposed non-surgical treatment plan categories to generate a treatment outcome prediction model, and verifying whether the treatment outcome prediction model meets preset performance requirements based on the validation clinical feature vectors; in response to meeting the preset performance requirements, acquiring the current clinical information of the current patient with tubal pregnancy, and inputting the current clinical information into the treatment outcome prediction model to output the non-surgical treatment outcome prediction data of the current patient with tubal pregnancy, and generating a target clinical prompt report of the non-surgical treatment outcome prediction data.

[0009] Optionally, in one embodiment of this application, the step of obtaining complete clinical record information of multiple patients with ectopic pregnancy to construct and preprocess a corresponding total training set, and extracting the training clinical feature vector of the training subset and the validation clinical feature vector of the validation subset in the preprocessed total training set, includes: obtaining complete clinical record information of the multiple patients with ectopic pregnancy, extracting the structured variable set corresponding to the complete clinical record information, and constructing the total training set based on the structured variable set, wherein the structured variable set includes demographic and medical history characteristics, symptoms and signs, laboratory and ultrasound indicators, and diagnostic and treatment information; performing missing value imputation and standardization on the total training set to obtain a standard training dataset, and dividing the standard training dataset into the training subset and the validation subset based on a preset time threshold; deleting relevant data and postoperative follow-up data corresponding to patients with ectopic pregnancy who have undergone surgical treatment in the training subset and the validation subset, and extracting the training clinical feature vector corresponding to the training subset and the validation clinical feature vector corresponding to the validation subset.

[0010] Optionally, in one embodiment of this application, the step of collecting multiple key control variables corresponding to the training clinical feature vector, training a pre-constructed gradient boosting tree model using the training clinical feature vector and the multiple key control variables to generate a treatment outcome prediction model, and verifying whether the treatment outcome prediction model meets preset performance requirements based on the verification clinical feature vector, includes: obtaining the proposed non-surgical treatment plan category corresponding to the training clinical feature vector, encoding the proposed non-surgical treatment plan category to obtain binary feature encoding data, and determining the prediction label corresponding to the binary feature encoding data, wherein the binary feature encoding data includes expected treatment encoding data and conservative treatment encoding data, and the prediction label includes treatment success label and treatment failure label; constructing the gradient boosting tree model based on a preset cross-entropy loss function, ordered enhancement mechanism and category boosting algorithm, and verifying whether the treatment outcome prediction model meets preset performance requirements based on the proposed non-surgical treatment plan category and the training clinical feature vector; and verifying whether the treatment outcome prediction model meets preset performance requirements based on the verification clinical feature vector. The gradient boosting tree model is iteratively trained using the vector and the predicted label until the iteration process meets the preset iteration termination requirements. A preset model solidification operation is then performed on the trained gradient boosting tree model to generate the treatment outcome prediction model. The validation clinical feature vector is input into the treatment outcome prediction model, and the matching degree between the prediction result corresponding to the validation clinical feature vector and the true label corresponding to the validation clinical feature vector is calculated. Multiple validation data for the treatment outcome prediction model are determined based on the matching degree, including accuracy, weighted F1 score, area under the receiver operating characteristic curve, recall, and precision. Based on the multiple validation data, a corresponding confusion matrix is ​​generated. The true positive and false negative distributions for each risk stratification interval are statistically analyzed using the confusion matrix, and the true positive and false negative distributions are used to verify whether the treatment outcome prediction model meets the preset discrimination balance requirements in different clinical risk populations.

[0011] Optionally, in one embodiment of this application, the step of obtaining the current clinical information of the current ectopic pregnancy patient and inputting the current clinical information into the treatment outcome prediction model to output the non-surgical treatment outcome prediction data of the current ectopic pregnancy patient, and generating a target clinical prompt report of the non-surgical treatment outcome prediction data, includes: collecting the current clinical information of the current ectopic pregnancy patient, inputting the current clinical information into the treatment outcome prediction model, outputting the success probability estimate or failure probability estimate corresponding to the expected treatment coding data and the conservative treatment coding data, as well as feature contribution ranking data; determining the non-surgical treatment outcome prediction data based on the success probability estimate or the failure probability estimate, and generating a corresponding target clinical prompt report based on the non-surgical treatment outcome prediction data; and pushing the target clinical prompt report to the target clinical information system through a preset API interface or visualization terminal, so that the target physician can perform corresponding diagnosis and treatment operations on the current ectopic pregnancy patient based on the target clinical prompt report.

[0012] A second aspect of this application provides a treatment outcome prediction device for non-surgical treatment of ectopic pregnancy, comprising: a preprocessing module, configured to acquire complete clinical record information of multiple patients with tubal pregnancy to construct and preprocess a corresponding total training set, and extract training clinical feature vectors of a training subset and validation clinical feature vectors of a validation subset from the preprocessed total training set; a training module, configured to collect the proposed non-surgical treatment plan categories corresponding to the training clinical feature vectors, and train a pre-constructed gradient boosting tree model using the training clinical feature vectors and the proposed non-surgical treatment plan categories to generate a treatment outcome prediction model, and verify whether the treatment outcome prediction model meets preset performance requirements based on the validation clinical feature vectors; and an inference module, configured to, in response to meeting the preset performance requirements, acquire the current clinical information of the current patient with tubal pregnancy, input the current clinical information into the treatment outcome prediction model, output non-surgical treatment outcome prediction data of the current patient with tubal pregnancy, and generate a target clinical prompt report of the non-surgical treatment outcome prediction data.

[0013] Optionally, in one embodiment of this application, the preprocessing module includes: an extraction unit, configured to acquire complete clinical record information of the plurality of patients with ectopic pregnancy, extract the structured variable set corresponding to the complete clinical record information, and construct the total training set based on the structured variable set, wherein the structured variable set includes demographic and medical history characteristics, symptoms and signs, laboratory and ultrasound indicators, and diagnostic and treatment information; a partitioning unit, configured to perform missing value imputation and standardization processing on the total training set to obtain a standard training dataset, and partition the standard training dataset into the training subset and the validation subset based on a preset time threshold; and a filtering unit, configured to delete relevant data and postoperative follow-up data corresponding to patients with ectopic pregnancy who have undergone surgical treatment in the training subset and the validation subset, and extract the training clinical feature vector corresponding to the training subset and the validation clinical feature vector corresponding to the validation subset.

[0014] Optionally, in one embodiment of this application, the training module includes: an encoding unit, configured to acquire the proposed non-surgical treatment plan category corresponding to the training clinical feature vector, encode the proposed non-surgical treatment plan category to obtain binary feature encoding data, and determine the prediction label corresponding to the binary feature encoding data, wherein the binary feature encoding data includes expected treatment encoding data and conservative treatment encoding data, and the prediction label includes treatment success label and treatment failure label; and an iteration unit, configured to construct the gradient boosting tree model based on a preset cross-entropy loss function, an ordered enhancement mechanism, and a category boosting algorithm, and iteratively train the gradient boosting tree model using the proposed non-surgical treatment plan category, the training clinical feature vector, and the prediction label until the iteration process meets a preset iteration termination requirement, and perform gradient boosting after training. A tree model performs a preset model solidification operation to generate the treatment outcome prediction model; a calculation unit is used to input the validation clinical feature vector into the treatment outcome prediction model, calculate the matching degree between the prediction result corresponding to the validation clinical feature vector and the true label corresponding to the validation clinical feature vector, and determine multiple validation data of the treatment outcome prediction model based on the matching degree, wherein the multiple validation data include accuracy, weighted F1 score, area under the receiver operating characteristic curve, recall, and precision; a validation unit is used to generate a corresponding confusion matrix based on the multiple validation data, to statistically analyze the true positive distribution and false negative distribution of each risk stratification interval based on the confusion matrix, and to verify whether the treatment outcome prediction model meets the preset discrimination balance requirements in different clinical risk populations through the true positive distribution and the false negative distribution.

[0015] Optionally, in one embodiment of this application, the inference module includes: a collection unit, used to collect the current clinical information of the current ectopic pregnancy patient, input the current clinical information into the treatment outcome prediction model, and output the success probability estimate or failure probability estimate corresponding to the expected treatment coding data and the conservative treatment coding data, as well as feature contribution ranking data; a generation unit, used to determine the non-surgical treatment outcome prediction data based on the success probability estimate or the failure probability estimate, and generate a corresponding target clinical prompt report based on the non-surgical treatment outcome prediction data; and a push unit, used to push the target clinical prompt report to the target clinical information system through a preset API interface or a visualization terminal, so that the target physician can perform corresponding diagnosis and treatment operations on the current ectopic pregnancy patient based on the target clinical prompt report.

[0016] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy as described in the above embodiments.

[0017] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy.

[0018] A fifth aspect of this application provides a computer program product, including a computer program that is executed to implement the above-described method for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy.

[0019] Therefore, the embodiments of this application have the following beneficial effects: The embodiments of this application can acquire complete clinical record information from multiple patients with ectopic pregnancy to construct and preprocess a corresponding total training set, and extract training clinical feature vectors from the training subset and validation clinical feature vectors from the validation subset in the preprocessed total training set; collect the proposed non-surgical treatment plan categories corresponding to the training clinical feature vectors, and train a pre-constructed gradient boosting tree model using the training clinical feature vectors and proposed non-surgical treatment plan categories to generate a treatment outcome prediction model; and verify whether the treatment outcome prediction model meets preset performance requirements based on the validation clinical feature vectors; in response to meeting the preset performance requirements, acquire the current clinical information of the current ectopic pregnancy patient, and input the current clinical information into the treatment outcome prediction model to output the non-surgical treatment outcome prediction data of the current ectopic pregnancy patient, and generate a target clinical prompt report of the non-surgical treatment outcome prediction data. Based on electronic medical record data streams, this application, through data acquisition and preprocessing, task label construction, model training and inference, and clinical decision output, can achieve high-precision outcome prediction with only the latest medical data, providing clinicians with a quantitative decision support tool that fits their actual workflow. This solves the problems in existing technologies, such as prediction models relying on complete historical sequences, being unable to adapt to missing or non-uniform sampling of real-world medical data, ignoring dynamic interactions between indicators, and having static and singular prediction results.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a method for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy according to an embodiment of this application; Figure 2 A schematic diagram of a dataset usage path is provided for one embodiment of this application; Figure 3 A schematic diagram illustrating the ranking of variable importance in an OUTCOME model, provided as an embodiment of this application; Figure 4 This is an example diagram of a treatment outcome prediction device for non-surgical treatment of ectopic pregnancy according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0022] Among them, 10-prediction device for non-surgical treatment of ectopic pregnancy, 100-preprocessing module, 200-training module, 300-inference module, 501-memory, 502-processor, and 503-communication interface. Detailed Implementation

[0023] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0024] The following describes a method and apparatus for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy according to embodiments of this application, with reference to the accompanying drawings. Addressing the problems mentioned in the background art, this application provides a method for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy. In this method, complete clinical record information of multiple patients with tubal pregnancy is acquired to construct and preprocess a corresponding total training set. Training clinical feature vectors of the training subset and validation clinical feature vectors of the validation subset are extracted from the preprocessed total training set. The proposed non-surgical treatment plan categories corresponding to the training clinical feature vectors are collected. A pre-constructed gradient boosting tree model is trained using the training clinical feature vectors and the proposed non-surgical treatment plan categories to generate a treatment outcome prediction model. Based on the validation clinical feature vectors, the treatment outcome prediction model is verified to meet preset performance requirements. In response to meeting the preset performance requirements, the current clinical information of the current patient with tubal pregnancy is acquired and input into the treatment outcome prediction model to output non-surgical treatment outcome prediction data for the current patient with tubal pregnancy, and a target clinical prompt report of the non-surgical treatment outcome prediction data is generated. This application, based on electronic medical record data streams, achieves high-precision outcome prediction through data acquisition and preprocessing, task label construction, model training and inference, and clinical decision output, requiring only the latest patient data. It provides clinicians with a quantitative decision support tool that aligns with their actual workflow. This solves the problems of existing technologies where prediction models rely on complete historical sequences, cannot adapt to missing or non-uniform sampling of real-world medical data, ignore dynamic interactions between indicators, and produce static and singular prediction results.

[0025] Specifically, Figure 1 This is a flowchart illustrating a method for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy, as provided in an embodiment of this application.

[0026] like Figure 1 As shown, the method for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy includes the following steps: In step S101, complete clinical record information of multiple patients with ectopic pregnancy is obtained to construct and preprocess the corresponding total training set, and the training clinical feature vector of the training subset and the validation clinical feature vector of the validation subset are extracted from the preprocessed total training set. This application embodiment first receives the complete clinical record of a patient's visit for ectopic pregnancy (TEP), constructs a corresponding total training set based on the complete clinical record, and performs data preprocessing and partitioning operations on the total training set to obtain the corresponding validation subset's validation clinical feature vector training subset and validation subset. Then, it extracts the training clinical feature vector of the training subset and the validation clinical feature vector of the validation subset, thereby providing reliable data support for predicting the outcome of subsequent non-surgical treatment of ectopic pregnancy (EP).

[0027] Optionally, in one embodiment of this application, complete clinical record information of multiple patients with ectopic pregnancy is obtained to construct and preprocess the corresponding total training set, and the training clinical feature vector of the training subset and the validation clinical feature vector of the validation subset in the preprocessed total training set are extracted. This includes: obtaining complete clinical record information of multiple patients with ectopic pregnancy, extracting the structured variable set corresponding to the complete clinical record information, and constructing the total training set based on the structured variable set, wherein the structured variable set includes demographic and medical history characteristics, symptoms and signs, laboratory and ultrasound indicators, and diagnostic and treatment information; performing missing value imputation and standardization on the total training set to obtain a standard training dataset, and dividing the standard training dataset into a training subset and a validation subset based on a preset time threshold; deleting relevant data and postoperative follow-up data corresponding to patients with ectopic pregnancy who have undergone surgical treatment in the training subset and validation subset, and extracting the training clinical feature vector corresponding to the training subset and the validation clinical feature vector corresponding to the validation subset.

[0028] In actual implementation, this embodiment first obtains the complete clinical records of multiple patients who visited the hospital for ectopic pregnancy (TEP) through the electronic medical record system, extracts the corresponding structured variable set, and constructs the corresponding total training set based on the structured variable set. The structured variable set includes the following: 1. Demographic and medical history characteristics: age, height, weight, BMI, number of days of amenorrhea, parity, parity, number of previous ectopic pregnancies, menstrual regularity, method of conception, plans for continued fertility, history of previous ipsilateral ectopic pregnancies, location of current ectopic pregnancies (left / right), history of use of pregnancy-supporting drugs, smoking / drinking history.

[0029] 2. Symptoms and signs: abdominal pain, a feeling of pressure in the anus, vaginal bleeding, and shock.

[0030] 3. Laboratory and ultrasound indicators: current serum β-hCG level, serum β-hCG (β-human chorionic gonadotropin) level and rate of increase 48 hours ago, endometrial thickness, HGB level, maximum diameter of adnexal mass, depth of pelvic effusion, yolk sac / fetal bud / fetal heartbeat within the mass, and preliminary ultrasound conclusions.

[0031] 4. Treatment information: Current proposed treatment plan (expectant management or MTX (Methotrexate) treatment) and the timestamp of this visit.

[0032] Secondly, embodiments of this application can perform data preprocessing operations on the aforementioned total training set, the specific process of which is as follows: 1. Missing value imputation: For missing items of serum β-hCG, rise rate and endometrial thickness 48 hours ago, if the gradient boosting tree algorithm is used, the outlier value -9999 is uniformly filled in; if the sequence neural network algorithm is used, -1 is filled in and the corresponding binary missing indicator variable is generated simultaneously.

[0033] 2. Standardized measurement: All continuous variables are standardized and mapped to a distribution with a mean of 0 and a variance of 1 (the validation set strictly reuses the mean and variance parameters of the training set).

[0034] Subsequently, in embodiments of this application, historical data in the preprocessed total training set can be divided into a training subset and recent data into a validation subset according to the time of medical visit.

[0035] Based on this, an embodiment of this application can select surgical decision-making and postoperative follow-up data from the training subset and validation subset to construct an OUTCOM model dataset, and perform corresponding feature processing operations on the filtered data to extract the training clinical feature vector corresponding to the training subset and the validation clinical feature vector corresponding to the validation subset.

[0036] Figure 2 Use a path diagram for the dataset. For example... Figure 2 As shown, This is a dataset of outpatient visits for TEP patients with generally stable conditions. This is a training subset of TEP patients with generally stable conditions; A validation subset of TEP patients with stable vital signs; The training set for the OUTCOME model is... Based on this, the data on surgical decision-making and postoperative follow-up visits were excluded; The validation set for the OUTCOME model is... Based on this, data from the decision-making process for surgical procedures and postoperative follow-up visits were excluded.

[0037] Therefore, the embodiments of this application construct a corresponding total training set based on the complete clinical record information of patients with ectopic pregnancy, perform preprocessing and segmentation operations on it, and extract the corresponding clinical feature vectors, thereby effectively improving the quality of the data and enhancing the reliability and robustness of model training.

[0038] In step S102, the proposed non-surgical treatment plan categories corresponding to the training clinical feature vectors are collected, and a pre-constructed gradient boosting tree model is trained by training the clinical feature vectors and the proposed non-surgical treatment plan categories to generate a treatment outcome prediction model. Based on the validation clinical feature vectors, the treatment outcome prediction model is verified to see if it meets the preset performance requirements.

[0039] Furthermore, embodiments of this application obtain the proposed non-surgical treatment plan category corresponding to the training clinical feature vector, and perform label mapping on the data of the training subset according to the corresponding prediction target (i.e., the prediction of the treatment outcome of non-surgical treatment of ectopic pregnancy), and assemble the model input vector (i.e., the input feature vector).

[0040] Secondly, in this embodiment, CatBoost (Categorical Boosting), i.e. gradient boosting tree model, can be trained by inputting feature vectors to generate a corresponding treatment outcome prediction model (i.e., OUTCOME model). The treatment outcome prediction model can be validated by using validation clinical feature vectors to determine whether the trained model meets the preset performance requirements, thereby improving the reliability and generalization of model training.

[0041] Optionally, in one embodiment of this application, multiple key control variables corresponding to training clinical feature vectors are collected, and a pre-constructed gradient boosting tree model is trained using the training clinical feature vectors and multiple key control variables to generate a treatment outcome prediction model. Furthermore, based on validating clinical feature vectors, the treatment outcome prediction model is verified to meet preset performance requirements. This includes: obtaining the proposed non-surgical treatment plan categories corresponding to the training clinical feature vectors, encoding the proposed non-surgical treatment plan categories to obtain binary feature encoded data, and determining the prediction labels corresponding to the binary feature encoded data. The binary feature encoded data includes expected treatment encoded data and conservative treatment encoded data, and the prediction labels include treatment success labels and treatment failure labels. Based on a preset cross-entropy loss function, ordered enhancement mechanism, and category boosting algorithm, a gradient boosting tree model is constructed, and the model is generated by training the proposed non-surgical treatment plan categories, training the clinical feature vectors, and multiple key control variables to generate a treatment outcome prediction model. The model uses feature vectors and predicted labels to iteratively train a gradient boosting tree model until the iteration process meets a preset termination requirement. The trained gradient boosting tree model is then subjected to a preset model solidification operation to generate a treatment outcome prediction model. Validation clinical feature vectors are input into the treatment outcome prediction model, and the matching degree between the predicted results corresponding to the validation clinical feature vectors and the corresponding true labels is calculated. Multiple validation data points for the treatment outcome prediction model are determined based on the matching degree, including accuracy, weighted F1 score, area under the receiver operating characteristic curve, recall, and precision. Based on these validation data points, a corresponding confusion matrix is ​​generated. The true positive and false negative distributions for each risk stratification interval are statistically analyzed using the confusion matrix, and the true positive and false negative distributions are used to verify whether the treatment outcome prediction model meets the preset discrimination balance requirement across different clinical risk populations.

[0042] It should be noted that the embodiments of this application aim to predict whether the final outcome of a patient receiving the currently selected non-surgical treatment plan (expectant treatment or MTX) will be "treatment success" or "treatment failure". The embodiments of this application are based solely on the clinical feature vector at the time of the current visit (i.e., the current visit feature vector), with the proposed non-surgical treatment plan as an additional variable, and the proposed non-surgical treatment plan is encoded as binary feature code data, namely "expectant treatment" or "MTX treatment".

[0043] Subsequently, the embodiments of this application may define a binary outcome (i.e., perform label definition) based on the actual clinical outcome, as described below: 1. Expectant treatment: Success means no further intervention is received and β-hCG levels drop to negative; Failure means subsequent treatment is switched to surgery or MTX.

[0044] 2. MTX treatment: Success means β-hCG levels drop to negative; failure means subsequent treatment involves surgery.

[0045] Furthermore, in this embodiment, a pre-built gradient boosting tree model can be used as the core prediction engine, and the gradient boosting tree model can be trained based on the input features consisting of the current medical visit feature vector and the proposed non-surgical treatment plan category.

[0046] In the specific implementation process, the process of training the gradient boosting tree model in this embodiment of the application is as follows: 1. Feature Encoding: The embodiments of this application can utilize the built-in Ordered Boosting mechanism of CatBoost to automatically process category features and reduce target leakage bias.

[0047] 2. Iterative optimization: The embodiments of this application can use the cross-entropy loss function for gradient descent optimization, and adopt the algorithm's default recommended hyperparameter initialization.

[0048] 3. Overfitting prevention control: The embodiments of this application can monitor the F1 score of the validation set in real time during the training process and configure an early stopping mechanism; when the optimal F1 score no longer decreases within 5 consecutive rounds, the training is automatically terminated and the snapshot of the model with the optimal weights is saved.

[0049] 4. Model solidification: The trained CatBoost tree ensemble structure is serialized and deployed on the server to perform online prediction operations.

[0050] As one possible approach, embodiments of this application can input the filtered validation subset into a fixed gradient boosting tree model (i.e., a treatment outcome prediction model), perform corresponding forward inference operations to calculate the matching degree between the prediction results and the true labels, and output accuracy, weighted F1 score, area under the receiver operating characteristic curve (AUC), recall (i.e., sensitivity), and precision (i.e., positive predictive value). In addition, embodiments of this application can also generate a multi-class / binary-class confusion matrix and statistically analyze the true positive and false negative distributions of each risk stratification interval to verify the model's discriminative balance in different risk populations in clinical practice.

[0051] It should be noted that in actual implementation, those skilled in the art can also use other gradient boosting tree algorithms (such as XGBoost, LightGBM) or ensemble learning models (such as random forest, Bagging) to replace CatBoost, depending on the actual situation. These methods can also achieve nonlinear feature mining and high-dimensional data processing, which will not be elaborated here.

[0052] In step S103, in response to meeting the preset performance requirements, the current clinical information of the current ectopic pregnancy patient is obtained and input into the treatment outcome prediction model to output the non-surgical treatment outcome prediction data of the current ectopic pregnancy patient and generate a target clinical prompt report of the non-surgical treatment outcome prediction data.

[0053] Subsequently, if the discrimination balance of the judgment model in different clinical risk populations meets the corresponding requirements, the embodiments of this application can obtain the current clinical information of the current ectopic pregnancy patient and input it into the treatment outcome prediction model to output the non-surgical treatment outcome prediction data of the current ectopic pregnancy patient and generate an interpretable clinical suggestion report (i.e., target clinical suggestion report) of the non-surgical treatment outcome prediction data.

[0054] Optionally, in one embodiment of this application, the current clinical information of the current ectopic pregnancy patient is obtained and input into the treatment outcome prediction model to output the non-surgical treatment outcome prediction data of the current ectopic pregnancy patient, and a target clinical prompt report of the non-surgical treatment outcome prediction data is generated. This includes: collecting the current clinical information of the current ectopic pregnancy patient and inputting the current clinical information into the treatment outcome prediction model, outputting the success probability estimate or failure probability estimate corresponding to the expected treatment code data and the conservative treatment code data, as well as the feature contribution ranking data; determining the non-surgical treatment outcome prediction data based on the success probability estimate or failure probability estimate, and generating a corresponding target clinical prompt report based on the non-surgical treatment outcome prediction data; and pushing the target clinical prompt report to the target clinical information system through a preset API interface or visualization terminal, so that the target physician can perform corresponding diagnosis and treatment operations on the current ectopic pregnancy patient based on the target clinical prompt report.

[0055] In actual implementation, this application embodiment collects the current clinical information of the current ectopic pregnancy patient and inputs it into the treatment outcome prediction model to output the corresponding inference results, namely the success probability estimate or failure probability estimate corresponding to the expected treatment code data and the conservative treatment code data; furthermore, this application embodiment can call the feature importance parsing algorithm to perform importance analysis on multiple input features of the treatment outcome prediction model and output the Top-N key driving variables and their feature contribution ranking results.

[0056] Subsequently, in this embodiment of the application, an interpretable clinical prompt report can be generated based on the reasoning results and feature contribution ranking data, and pushed to the clinical information system through an API interface or a visualization terminal, so that physicians can perform corresponding diagnosis and treatment operations on the current ectopic pregnancy patient based on the interpretable clinical prompt report.

[0057] Therefore, embodiments of this application can predict the success / failure probability of expectant treatment or MTX treatment based on current clinical characteristics and treatment decisions using the CatBoost algorithm, and output a ranking of feature contributions to support clinical interpretability.

[0058] The following section uses outpatient, emergency, and inpatient medical records of patients with stable ectopic pregnancy (TEP) admitted to a tertiary hospital from January 2015 to April 2024 to provide a detailed explanation of the specific construction and application process of the OUTCOME model (non-surgical treatment outcome prediction model) in this application.

[0059] 1. Dataset Construction and Preprocessing: This application first extracts complete clinical information of the patient's current visit from the electronic medical record system. Each visit record constitutes an independent data entry, and the included clinical variables include the following: (1) Demographic and medical history characteristics: age, height, weight, BMI, number of days of amenorrhea, parity, parity, number of previous ectopic pregnancies (EP), regularity of menstruation, method of conception, whether there is a plan to continue having children, history of previous ipsilateral TEP, location of this TEP (left / right), whether tocolytic drugs were used in this pregnancy, smoking history, and drinking history.

[0060] (2) Symptoms and signs: presence of abdominal pain, anal tenesmus, vaginal bleeding, and shock.

[0061] (3) Laboratory tests: serum β-hCG level at the time of this visit, serum β-hCG level and increase rate 48 hours ago, HGB level, and endometrial thickness.

[0062] (4) Ultrasound examination: maximum diameter of the mass in the adnexal region, depth of pelvic effusion, whether yolk sac / fetal bud / fetal heartbeat is seen in the mass, and preliminary ultrasound conclusion.

[0063] (5) Medical information: the treatment plan (expectant management, MTX treatment or surgery) at the time of this visit and the outcome of the treatment.

[0064] 2. Data partitioning: In this embodiment of the application, data from January 2015 to December 2022 can be divided into a training subset and data from January 2023 to April 2024 can be divided into a validation subset according to the time dimension.

[0065] 3. Missing values ​​and standardization: (1) For the missing blood β-hCG level, increase rate and endometrial thickness 48 hours ago, if the CatBoost algorithm is used, the outlier value -9999 is used for imputation; if the LSTM algorithm is used, -1 is used for imputation and missing indicator binary variables are generated simultaneously.

[0066] (2) For the LSTM algorithm, all continuous variables are standardized to a distribution with a mean of 0 and a variance of 1 before input (the validation subset uses the mean and variance of the training set).

[0067] 4. Dataset filtering: OUTCOME model dataset: Select surgical decision-making and postoperative follow-up data from the training and validation subsets to form the final training subset (n=1,401 records) and validation subset (n=344 records).

[0068] 5. Label definition and feature vector assembly: Input features: current medical visit feature vector, proposed non-surgical treatment plan category.

[0069] Output label: Binary classification. Expected treatment success is defined as no further intervention and β-hCG levels dropping to negative; failure is defined as conversion to surgery or MTX. Successful MTX treatment is defined as β-hCG levels dropping to negative; failure is defined as conversion to surgery.

[0070] 6. Model Training and Inference: This embodiment of the application can use the CatBoost algorithm to construct a gradient boosting tree model, and uses the CatBoost default recommended hyperparameters, with cross-entropy loss as the loss function. During training, this embodiment of the application can iteratively construct a decision tree ensemble on a subset of training data, and utilize an ordered augmentation mechanism to process class features, reducing target leakage bias, so as to obtain the trained gradient boosting tree model, i.e., the treatment outcome prediction model.

[0071] 7. Performance Verification and Result Analysis: Furthermore, the embodiments of this application can be validated on an independent validation set (i.e., a validation subset). The efficacy data of the treatment outcome prediction model are: accuracy 0.831; F1 score 0.748; AUC 0.867, which is better than the LSTM model based on complete time series data (accuracy 0.770, AUC 0.829), as shown in Table 1: Table 1

[0072] 8. Variable Importance Analysis: The importance ranking of model features is shown below: (1) The embodiments of this application explicitly define "current blood β-hCG level, current mass size, time interval Δt, HGB level, pelvic effusion depth, BMI, and treatment plan selection" as the core prediction feature set, and their stable weight relationship in the ranking of the importance of model features. Among them, the treatment plan itself contributes the most to the prediction results, reflecting the difference in efficacy of different treatment methods.

[0073] (2) such as Figure 3 As shown, the key variables (i.e., the Top-4 key driver variables of the pre-ranking results) are, in order: serum β-hCG level at the time of consultation, BMI, pelvic effusion depth, and adnexal mass size. In other words, in the OUTCOME model, the proposed treatment plan itself has the highest weight, followed by the current serum β-hCG level, BMI, pelvic effusion depth, and mass size.

[0074] 9. Clinical guidance: When physicians consider deviating from guideline recommendations (such as attempting non-surgical treatment for masses >3.5cm), this treatment outcome prediction model can be used to quantify the success rate and assist physicians and patients in making joint decisions.

[0075] Understandably, given the clinical challenges of short follow-up periods in obstetric and gynecological emergencies and the lack of prior medical records due to inter-hospital referrals, this application's implementation explicitly extracts only the patient's most recent medical record as model input, eliminating strong reliance on complete historical time-series data. Comparative tests on the same independent validation set show that, under this lightweight strategy, the CatBoost model comprehensively outperforms the LSTM network, which relies on complete time-series data, in terms of accuracy, F1 score, and AUC (blood β-hCG prediction: 0.885 vs 0.792; block size prediction: 0.907 vs 0.810; treatment outcome prediction: 0.831 vs 0.770). This design completely avoids the gradient instability and prediction bias problems that deep time-series networks are prone to when data is not uniformly sampled or records are incomplete, significantly improving the model's usability and algorithm stability in scenarios with incomplete medical records.

[0076] Furthermore, real-world clinical data shows that the overall discrepancy between the initial treatment decision and guideline recommendations for generally stable TEP patients is as high as 42.0%. Even in groups where deviations from guideline recommendations (e.g., patients choosing MTX treatment despite guideline-recommended surgery) still achieved a success rate of 68.8%. Traditional fixed decision-making models cannot cover such complex interactive scenarios. The OUTCOME model constructed in this application can accurately output the probability of success / failure of non-surgical treatment (expected or MTX) based on current clinical characteristics and proposed treatment plan. On the independent validation set (n=344), the model's prediction accuracy reached 0.831, and the AUC was 0.867. This performance provides an objective risk quantification reference for collaborative decision-making between doctors and patients, assisting physicians in scientifically assessing potential failure risks while respecting patients' fertility preferences and willingness to undergo conservative treatment, effectively reducing clinical overtreatment and undertreatment.

[0077] The method for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy proposed in this application involves acquiring complete clinical records of multiple patients with tubal pregnancy to construct and preprocess a corresponding total training set. Training clinical feature vectors from the training subset and validation clinical feature vectors from the validation subset are extracted from the preprocessed total training set. The proposed non-surgical treatment plan categories corresponding to the training clinical feature vectors are collected, and a pre-constructed gradient boosting tree model is trained using the training clinical feature vectors and proposed non-surgical treatment plan categories to generate a treatment outcome prediction model. Based on the validation clinical feature vectors, the model is verified to meet preset performance requirements. If the preset performance requirements are met, the current clinical information of the current patient with tubal pregnancy is acquired and input into the treatment outcome prediction model to output the non-surgical treatment outcome prediction data for the current patient with tubal pregnancy, and a target clinical suggestion report for the non-surgical treatment outcome prediction data is generated. Based on electronic medical record data streams, this application achieves high-precision outcome prediction with only the latest medical records through data acquisition and preprocessing, task label construction, model training and inference, and clinical decision output, providing clinicians with a quantitative decision support tool that fits their actual workflow.

[0078] Secondly, with reference to the accompanying drawings, a treatment outcome prediction device for non-surgical treatment of ectopic pregnancy according to an embodiment of this application is described.

[0079] Figure 4 This is a block diagram of a treatment outcome prediction device for non-surgical treatment of ectopic pregnancy according to an embodiment of this application.

[0080] like Figure 4 As shown, the treatment outcome prediction device 10 for non-surgical treatment of ectopic pregnancy includes: a preprocessing module 100, a training module 200, and an inference module 300.

[0081] The preprocessing module 100 is used to acquire complete clinical record information of multiple patients with ectopic pregnancy, so as to construct and preprocess the corresponding total training set, and extract the training clinical feature vector of the training subset and the validation clinical feature vector of the validation subset in the preprocessed total training set.

[0082] The training module 200 is used to collect the proposed non-surgical treatment plan categories corresponding to the training clinical feature vectors, and to train a pre-constructed gradient boosting tree model by training the training clinical feature vectors and proposed non-surgical treatment plan categories to generate a treatment outcome prediction model. Based on the validation clinical feature vectors, the module verifies whether the treatment outcome prediction model meets the preset performance requirements.

[0083] The inference module 300 is used to obtain the current clinical information of the current ectopic pregnancy patient in response to the preset performance requirements, and input the current clinical information into the treatment outcome prediction model to output the non-surgical treatment outcome prediction data of the current ectopic pregnancy patient, and generate a target clinical prompt report of the non-surgical treatment outcome prediction data.

[0084] Optionally, in one embodiment of this application, the preprocessing module 100 includes: an extraction unit, a segmentation unit, and a filtering unit.

[0085] The extraction unit is used to obtain complete clinical record information of multiple patients with ectopic pregnancy, extract the structured variable set corresponding to the complete clinical record information, and construct a total training set based on the structured variable set. The structured variable set includes demographic and medical history characteristics, symptoms and signs, laboratory and ultrasound indicators, and diagnostic and treatment information.

[0086] The partitioning unit is used to impute missing values ​​and standardize the total training set to obtain a standard training dataset. Based on a preset time threshold, the standard training dataset is divided into a training subset and a validation subset.

[0087] The filtering unit is used to delete relevant data and postoperative follow-up data of patients with ectopic pregnancy who were selected for surgical treatment in the training subset and validation subset, and to extract the training clinical feature vector corresponding to the training subset and the validation clinical feature vector corresponding to the validation subset.

[0088] Optionally, in one embodiment of this application, the training module 200 includes: an encoding unit, an iteration unit, a calculation unit, and a verification unit.

[0089] The encoding unit is used to obtain the proposed non-surgical treatment plan category corresponding to the training clinical feature vector, and to encode the proposed non-surgical treatment plan category to obtain binary feature encoding data, and to determine the prediction label corresponding to the binary feature encoding data. The binary feature encoding data includes expected treatment encoding data and conservative treatment encoding data, and the prediction label includes treatment success label and treatment failure label.

[0090] The iterative unit is used to construct a gradient boosting tree model based on a preset cross-entropy loss function, ordered enhancement mechanism, and category boosting algorithm. It iteratively trains the gradient boosting tree model by proposing non-surgical treatment plan categories, training clinical feature vectors, and prediction labels until the iteration process meets the preset iteration termination requirements. The trained gradient boosting tree model is then subjected to a preset model solidification operation to generate a treatment outcome prediction model.

[0091] The computational unit is used to input the validation clinical feature vector into the treatment outcome prediction model, calculate the matching degree between the prediction result corresponding to the validation clinical feature vector and the true label corresponding to the validation clinical feature vector, and determine multiple validation data of the treatment outcome prediction model based on the matching degree. The multiple validation data include accuracy, weighted F1 score, area under the receiver operating characteristic curve, recall, and precision.

[0092] The validation unit is used to generate a corresponding confusion matrix based on multiple validation data, and to statistically analyze the true positive and false negative distributions of each risk stratification interval according to the confusion matrix. The true positive and false negative distributions are then used to verify whether the treatment outcome prediction model meets the preset discrimination balance requirements in different clinical risk populations.

[0093] Optionally, in one embodiment of this application, the inference module 300 includes: a collection unit, a generation unit, and a push unit.

[0094] The acquisition unit is used to collect the current clinical information of patients with ectopic pregnancy and input the current clinical information into the treatment outcome prediction model. It outputs the success probability estimate or failure probability estimate corresponding to the expected treatment code data and the conservative treatment code data, as well as the feature contribution ranking data.

[0095] The generation unit is used to determine the prediction data of non-surgical treatment outcomes based on the success probability estimate or the failure probability estimate, and to generate the corresponding target clinical prompt report based on the prediction data of non-surgical treatment outcomes.

[0096] The push unit is used to push the target clinical prompt report to the target clinical information system through a preset API interface or visual terminal, so that the target physician can perform corresponding diagnosis and treatment operations on the current ectopic pregnancy patient based on the target clinical prompt report.

[0097] It should be noted that the explanation of the aforementioned method for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy also applies to the device for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy in this embodiment, and will not be repeated here.

[0098] The treatment outcome prediction device for non-surgical treatment of ectopic pregnancy proposed in this application includes a preprocessing module 100, which acquires complete clinical record information of multiple patients with tubal pregnancy to construct and preprocess the corresponding total training set, and extracts the training clinical feature vector of the training subset and the validation clinical feature vector of the validation subset in the preprocessed total training set; a training module 200, which collects the proposed non-surgical treatment plan categories corresponding to the training clinical feature vectors, trains a pre-constructed gradient boosting tree model through the training clinical feature vectors and the proposed non-surgical treatment plan categories, generates a treatment outcome prediction model, and verifies whether the treatment outcome prediction model meets the preset performance requirements based on the validation clinical feature vectors; and an inference module 300, which, in response to meeting the preset performance requirements, acquires the current clinical information of the current patient with tubal pregnancy, inputs the current clinical information into the treatment outcome prediction model, outputs the non-surgical treatment outcome prediction data of the current patient with tubal pregnancy, and generates a target clinical prompt report of the non-surgical treatment outcome prediction data. Based on electronic medical record data streams, this application enables high-precision outcome prediction through data acquisition and preprocessing, task label construction, model training and inference, and clinical decision output, requiring only the latest medical data. It provides clinicians with a quantitative decision support tool that fits their actual workflow.

[0099] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0100] When the processor 502 executes the program, it implements the treatment outcome prediction method for non-surgical treatment of ectopic pregnancy provided in the above embodiments.

[0101] Furthermore, electronic devices also include: Communication interface 503 is used for communication between memory 501 and processor 502.

[0102] The memory 501 is used to store computer programs that can run on the processor 502.

[0103] Memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0104] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0105] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0106] Processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0107] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy.

[0108] This application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-described method for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy.

[0109] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0110] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0111] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0112] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0113] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0114] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0115] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0116] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy, characterized in that, Includes the following steps: Complete clinical records of multiple patients with ectopic pregnancy were obtained to construct and preprocess the corresponding total training set, and the training clinical feature vectors of the training subset and the validation clinical feature vectors of the validation subset were extracted from the preprocessed total training set. Collect the proposed non-surgical treatment plan categories corresponding to the training clinical feature vectors, and train a pre-constructed gradient boosting tree model using the training clinical feature vectors and the proposed non-surgical treatment plan categories to generate a treatment outcome prediction model. Based on the verification clinical feature vectors, verify whether the treatment outcome prediction model meets the preset performance requirements. In response to meeting the preset performance requirements, the current clinical information of the current ectopic pregnancy patient is obtained and input into the treatment outcome prediction model to output the non-surgical treatment outcome prediction data of the current ectopic pregnancy patient and generate a target clinical prompt report of the non-surgical treatment outcome prediction data.

2. The method for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy according to claim 1, characterized in that, The process involves acquiring complete clinical records from multiple patients with ectopic pregnancies to construct and preprocess a corresponding total training set. The preprocessed total training set includes extracting training clinical feature vectors from the training subset and validation clinical feature vectors from the validation subset, including: Complete clinical record information of the multiple patients with ectopic pregnancy is obtained, and the structured variable set corresponding to the complete clinical record information is extracted. The total training set is constructed based on the structured variable set, wherein the structured variable set includes demographic and medical history characteristics, symptoms and signs, laboratory and ultrasound indicators, and diagnostic and treatment information. The total training set is imputed and standardized to obtain a standard training dataset. Based on a preset time threshold, the standard training dataset is divided into the training subset and the validation subset. Delete the relevant data and postoperative follow-up data of the patients with ectopic pregnancy who were selected for surgical treatment in the training subset and the validation subset, and extract the training clinical feature vector corresponding to the training subset and the validation clinical feature vector corresponding to the validation subset.

3. The method for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy according to claim 2, characterized in that, The process involves collecting multiple key control variables corresponding to the training clinical feature vectors, training a pre-constructed gradient boosting tree model using the training clinical feature vectors and the multiple key control variables, generating a treatment outcome prediction model, and verifying whether the treatment outcome prediction model meets preset performance requirements based on the validation clinical feature vectors, including: Obtain the proposed non-surgical treatment plan category corresponding to the training clinical feature vector, and encode the proposed non-surgical treatment plan category to obtain binary feature coding data, and determine the prediction label corresponding to the binary feature coding data, wherein the binary feature coding data includes expected treatment coding data and conservative treatment coding data, and the prediction label includes treatment success label and treatment failure label; Based on the preset cross-entropy loss function, ordered enhancement mechanism and category boosting algorithm, the gradient boosting tree model is constructed. The gradient boosting tree model is iteratively trained using the proposed non-surgical treatment plan category, the training clinical feature vector and the prediction label until the iteration process meets the preset iteration termination requirements. The trained gradient boosting tree model is then subjected to a preset model solidification operation to generate the treatment outcome prediction model. The validation clinical feature vector is input into the treatment outcome prediction model. The matching degree between the prediction result corresponding to the validation clinical feature vector and the true label corresponding to the validation clinical feature vector is calculated. Based on the matching degree, multiple validation data of the treatment outcome prediction model are determined. The multiple validation data include accuracy, weighted F1 score, area under the receiver operating characteristic curve, recall, and precision. Based on the multiple validation data, a corresponding confusion matrix is ​​generated. The true positive and false negative distributions of each risk stratification interval are statistically analyzed according to the confusion matrix. The true positive and false negative distributions are then used to verify whether the treatment outcome prediction model meets the preset discrimination balance requirements in different clinical risk populations.

4. The method for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy according to claim 3, characterized in that, The process of acquiring the current clinical information of the current ectopic pregnancy patient and inputting the current clinical information into the treatment outcome prediction model to output the non-surgical treatment outcome prediction data of the current ectopic pregnancy patient, and generating a target clinical suggestion report of the non-surgical treatment outcome prediction data, includes: Collect the current clinical information of the current ectopic pregnancy patient, input the current clinical information into the treatment outcome prediction model, and output the success probability estimate or failure probability estimate corresponding to the expected treatment code data and the conservative treatment code data, as well as the feature contribution ranking data; Based on the success probability estimate or the failure probability estimate, the non-surgical treatment outcome prediction data is determined, and a corresponding target clinical prompt report is generated based on the non-surgical treatment outcome prediction data. The target clinical alert report is pushed to the target clinical information system through a preset API interface or visual terminal, so that the target physician can perform corresponding diagnosis and treatment operations on the current ectopic pregnancy patient based on the target clinical alert report.

5. A device for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy, characterized in that, include: The preprocessing module is used to obtain complete clinical record information of multiple patients with ectopic pregnancy in order to construct and preprocess the corresponding total training set, and extract the training clinical feature vector of the training subset and the validation clinical feature vector of the validation subset in the preprocessed total training set. The training module is used to collect the proposed non-surgical treatment plan categories corresponding to the training clinical feature vectors, and to train a pre-constructed gradient boosting tree model through the training clinical feature vectors and the proposed non-surgical treatment plan categories to generate a treatment outcome prediction model. Based on the verification clinical feature vectors, the module verifies whether the treatment outcome prediction model meets the preset performance requirements. The inference module is used to obtain the current clinical information of the current ectopic pregnancy patient in response to the preset performance requirements, and input the current clinical information into the treatment outcome prediction model to output the non-surgical treatment outcome prediction data of the current ectopic pregnancy patient, and generate a target clinical prompt report of the non-surgical treatment outcome prediction data.

6. The treatment outcome prediction device for non-surgical treatment of ectopic pregnancy according to claim 5, characterized in that, The preprocessing module includes: The extraction unit is used to obtain complete clinical record information of the multiple patients with ectopic pregnancy, extract the structured variable set corresponding to the complete clinical record information, and construct the total training set based on the structured variable set. The structured variable set includes demographic and medical history characteristics, symptoms and signs, laboratory and ultrasound indicators, and diagnostic and treatment information. A partitioning unit is used to perform missing value imputation and standardization on the total training set to obtain a standard training dataset, and to divide the standard training dataset into the training subset and the validation subset based on a preset time threshold. The filtering unit is used to delete relevant data and postoperative follow-up data of patients with ectopic pregnancy who were selected for surgical treatment in the training subset and the validation subset, and to extract the training clinical feature vector corresponding to the training subset and the validation clinical feature vector corresponding to the validation subset.

7. The treatment outcome prediction device for non-surgical treatment of ectopic pregnancy according to claim 6, characterized in that, The training module includes: The encoding unit is used to obtain the proposed non-surgical treatment plan category corresponding to the training clinical feature vector, and to encode the proposed non-surgical treatment plan category to obtain binary feature encoding data, and to determine the prediction label corresponding to the binary feature encoding data, wherein the binary feature encoding data includes expected treatment encoding data and conservative treatment encoding data, and the prediction label includes treatment success label and treatment failure label. An iterative unit is used to construct the gradient boosting tree model based on a preset cross-entropy loss function, ordered enhancement mechanism and category boosting algorithm, and iteratively train the gradient boosting tree model using the proposed non-surgical treatment plan category, the training clinical feature vector and the prediction label until the iteration process meets the preset iteration termination requirements, and perform a preset model solidification operation on the trained gradient boosting tree model to generate the treatment outcome prediction model. The calculation unit is used to input the validation clinical feature vector into the treatment outcome prediction model, calculate the matching degree between the prediction result corresponding to the validation clinical feature vector and the true label corresponding to the validation clinical feature vector, and determine multiple validation data of the treatment outcome prediction model based on the matching degree, wherein the multiple validation data include accuracy, weighted F1 score, area under the receiver operating characteristic curve, recall and precision. The validation unit is used to generate a corresponding confusion matrix based on the multiple validation data, to statistically analyze the true positive and false negative distributions of each risk stratification interval according to the confusion matrix, and to verify whether the treatment outcome prediction model meets the preset discrimination balance requirements in different clinical risk populations through the true positive and false negative distributions.

8. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the method for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy as described in any one of claims 1-4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by a processor to implement the treatment outcome prediction method for non-surgical treatment of ectopic pregnancy as described in any one of claims 1-4.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the method for predicting the treatment outcome of non-surgical treatment of ectopic pregnancy as described in any one of claims 1-4.

Images