Patent Application
20250232858
The present invention belongs to the field of predicting adverse drug reactions, and specifically relates to a method for constructing an ADR prediction model for elderly patients, a prediction system, and a storage medium.
Adverse drug reactions (ADRs) refer to harmful reactions that occur under normal usage and dosage of qualified drugs, which are unrelated to the intended purpose of medication. Minimizing and avoiding ADRs during medication administration is of great significance for patients.
In 2003, the Institute for Healthcare Improvement (IHI) introduced the Global Trigger Tool (GTT) for monitoring medical-related adverse events (AEs), including ADRs. This tool incorporates triggers based on case review, and by monitoring clues related to medical AEs, it purposefully locates content related to AEs, thereby enhancing the efficiency and accuracy of case review.
Since its introduction, GTT has undergone nearly 20 years of research and application by scholars both domestically and internationally, achieving significant progress. Currently, GTT can only be used for active monitoring of ADRs with low efficiency, and it cannot be used for predicting ADRs, thus failing to effectively prevent ADRs and reduce their incidence. Therefore, there is an urgent need in this field for a method that can predict ADRs through tools such as artificial intelligence algorithms. However, the existing GTT was not designed specifically for machine learning technology. For the prediction of ADRs in specific populations (such as elderly patients), how to construct features and select appropriate model algorithms remains an urgent problem to be solved in this field.
Addressing the issues in the prior art, the present invention introduces a method for constructing a prediction model and a prediction system for adverse drug reactions in elderly patients. The aim is to facilitate the prediction of adverse drug reactions in elderly patients and aid clinical medication decisions.
A method for constructing an ADR prediction model for elderly patients, which comprises the following steps:
The risk factors include at least one of the patient's basic information, disease conditions, symptoms and signs, laboratory examination results, or medication situation.
Preferably, the basic information of the patient includes at least one of the following characteristics: gender, age, height, weight, surgical history, infectious disease history, allergy history, smoking history, admission method, and admission condition;
Preferably, in step 3, during the annotation process, the identification of ADRs is carried out based on the judgment criteria established by the National Center for ADR Monitoring, China.
Preferably, in step 4, before using the annotated dataset to train the machine learning model, data preprocessing is also performed. The data preprocessing includes: deleting features with missing values greater than 20%, and using at least one method for handling missing values; the methods for handling missing values include: at least one of no imputation, mean imputation, regression imputation, or missForest method.
Preferably, the algorithm of the machine learning model is selected from XGBoost, AdaBoost, CatBoost, GBDT, LightGBM, TPOT, or random forest.
Preferably, the risk factors include: age, number of admission diagnoses, number of hospitalizations before admission, tumor disease, level of nursing care upon admission, gender, number of drug types, frequency of medication administration, and drug category;
Preferably, the risk factors include: age, number of admission diagnoses, number of hospitalizations before admission, tumor disease, level of nursing care upon admission, and gender;
The present invention also provides an ADR prediction system for elderly patients, which comprises:
The present invention also provides a computer-readable storage medium, which stores a computer program for implementing the above method for constructing an ADR prediction model for elderly patients, or for implementing the above ADR prediction system for elderly patients.
In the present invention, “risk factor annotation” refers to annotating the nature of different characteristics (i.e., risk factors) for each patient among all features, such as whether a certain type of medicament is used, whether there is a certain disease, whether there are certain symptoms or signs, etc., for subsequent machine learning. “ADR discrimination” refers to manually discriminating whether an ADR has occurred, for subsequent machine learning.
The present invention aims to predict ADRs in elderly patients, optimize ADR trigger entries, and select corresponding features based on these entries to establish a machine learning model, achieving artificial intelligence prediction of ADRs in elderly patients. The prediction model and system established in the present invention exhibit excellent predictive performance, enabling the assessment and prediction of ADR risks for existing or alternative medication regimens in elderly patients, thereby assisting clinical decision-making. Therefore, the present invention holds great potential for clinical application.
Obviously, based on the above content of the present invention, according to the common technical knowledge and the conventional means in the field, other various modifications, alternations, or changes can further be made, without department from the above basic technical spirits.
With reference to the following specific examples, the above content of the present invention is further illustrated. But it should not be construed that the scope of the above subject matter of the present invention is limited to the following examples. The techniques realized based on the above content of the present invention are all within the scope of the present invention.
It should be noted that the algorithms for data collection, transmission, storage, and processing steps not specifically described in the examples, as well as the hardware structure and circuit connections not specifically described, can be performed by the published content available in the prior art.
The purpose of this example was to construct an ADR prediction model for elderly patients, thereby realizing artificial intelligence-based ADR prediction for elderly patients.
The specific steps were as follows:
A certain number of cases were randomly selected, and the aforementioned ADR trigger entries for elderly patients (preliminary version) were used to conduct research on real cases. Based on the ADR monitoring results of these real cases, a total of 28 ADR trigger entries (final version) were obtained. Details are shown in Table 2.
Specifically, based on the ADR types involved in the ADR trigger entries (final version), we retrieved literature related to a specific ADR, information from domestic and international ADR monitoring databases, as well as ADR information from existing hospital drug instructions. We established a dataset of ADR risk factors for elderly patients. The features in the dataset include non-drug factors (including patient's basic information, disease conditions, symptoms and signs, laboratory tests) and drug factors (medication use). Specifically, the features used in this example include:
tuberculosis, bacteriostatic drugs for
Mycobacterium tuberculosis
Specifically, it comprised the following steps:
It should be noted that in the prior art, those skilled in this field (such as doctors, pharmacists, researchers, etc.) were aware of the relationship between certain characteristics and ADRs in patients with aid of research and professional knowledge. However, due to factors such as the wide variety of drugs, individual differences among patients, and advancements in related medical research, it was impossible for them to provide a complete and optimal combination of features for each type of drug. Therefore, the way of inputting features in this example was to input all the features listed in step S2 into the model (for example, for the drug categories listed in step S2, based on whether the patient used the drug category, “Yes” or “No” could be used as the input value for input). During the model training process, the model algorithm determined the impact of various features on the prediction results.
The model training step employed a 10-fold cross-validation method to perform internal model validation on the training set and adjust model parameters, aiming to achieve the maximum AUC value for the training set. Using the 10-fold cross-validation method, each model obtained 10 different sets of machine learning evaluation metrics based on the training set. The best-performing models, totaling N×M, were selected for the test set. The superiority of each model was determined based on metrics such as AUC, accuracy, precision, recall, and F1 scores for the test set.
The system of this example includes:
Feature data acquisition module, used for inputting the ADR-related feature data of the elderly patients and sending the ADR-related feature data of the elderly patients to the data acquisition and storage module;
The technical solution of the present invention was further illustrated with reference to the following experimental examples. The models in the following experimental examples were trained according to the method of Example 1, with the difference being the selection of some features (risk factors) or the choice of specific machine learning algorithms. Steps or parameters not specifically described in the following experimental examples were executed according to the records of Example 1.
This experimental example was pre-tested among thousands of patients treated with medication. After incorporating 23 non-drug influencing factors (see the left column of Table 5 for details), eXtreme Gradient Boosting (XGBoost), AdaBoost, CatBoost, Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LightGBM), Tree-based Pipeline Optimization Tool (TPOT), and Random Forest (RF) were used for analysis.
As shown in
Based on experimental example 1, this experimental example incorporated 23 non-drug influencing factors (see Table 5) and factors related to medication use (including the number of drug types, frequency of medication use, and drug categories, as detailed in the description of medication use in Example 1) as input features. Analysis was conducted using eXtreme Gradient Boosting (XGBoost), AdaBoost, CatBoost, Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LightGBM), Tree-based Pipeline Optimization Tool (TPOT), and Random Forest (RF).
The experimental results, as shown in
Taking drug-induced liver injury as an example, we extracted tens of thousands of cases of drug treatment from patients, among which 480 cases developed drug-induced liver injury. We then conducted a statistical analysis on these cases.
As shown in Table 7, a total of 73 types of drugs were involved in drug-induced liver injury; since one ADR may involve multiple drugs simultaneously, the cumulative frequency of triggering ADRs by drugs was 684 times.
As shown in the table, the results indicated that the following drugs were most commonly associated with drug-induced liver injury: penicillin antibiotics, heparin anticoagulants, cephalosporin antibiotics, antipyretic-analgesic and anti-inflammatory drugs, other β-lactam antibiotics, carbapenem antibiotics, proton pump inhibitors for suppressing gastric acid secretion, quinolone antimicrobial agents, alkylating agents, azole antifungals, drugs affecting cholesterol synthesis, dopamine or serotonin receptor-based anti-emetic drugs, other anti-epileptic drugs, anti-metabolite antitumor drugs, tetracycline antibiotics, glycopeptide antibiotics, glucocorticoids, and bactericidal agents for Mycobacterium tuberculosis. Therefore, when using the method of Example 1 to predict ADRs in elderly patients, and specifically limiting the type of ADR to drug-induced liver injury, the medication use of the above drug types was a priority risk factor.
As evident from the above examples and experimental examples, the prediction model and system provided in the present invention were capable of evaluating and predicting the ADR risks associated with existing or alternative medication regimens for elderly patients. By analyzing the safety prediction outcomes of different medication regimens, this system aids doctors in making medication decisions. Consequently, the present invention holded promising clinical applications.
| Number | Date | Country | Kind |
|---|---|---|---|
| 202410049577.5 | Jan 2024 | CN | national |
