Construction method, prediction system, and storage medium of ADR prediction model for elderly patients

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:

Step 1: Constructing an ADR trigger entries for elderly patients, which includes laboratory results, rescue medication use, symptoms, and blood drug concentration; Step 2: Based on the ADR trigger entries for elderly patients, selecting risk factors and constructing a dataset of ADR risk factors related to the triggers for elderly patients; Step 3: Completing the risk factor annotation and ADR discrimination for the dataset of ADR risk factors in elderly patients related to triggers; Step 4: Training a machine learning model using the dataset information annotated in Step 3 to obtain an ADR risk prediction model for elderly patients;

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;

The disease conditions include at least one of the following features: main diagnostic classification, number of admission diagnoses, department of admission, level of nursing care upon admission, number of hospitalizations before admission, whether surgery was performed, and type of surgery; the main diagnostic classification includes whether the patient has a disease, and the diseases include at least one of the following types: infectious diseases or parasitic diseases, tumor diseases, blood or hematopoietic organ diseases, immune system diseases, endocrine or metabolic diseases, neurological or psychiatric diseases, eye or ear diseases, circulatory system diseases, respiratory system diseases, digestive system diseases, skin diseases, musculoskeletal or connective tissue diseases, urogenital system diseases, injuries or poisoning; The symptoms and signs include at least one of the following features: admission temperature, breathing, pulse, heart rate, blood pressure, and mental status; The laboratory examination includes at least one of the following features: alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, total bilirubin, creatinine, creatinine clearance rate, white blood cell count and percentage, red blood cell count, hemoglobin, platelet count, lymphocyte count, eosinophil count, neutrophil count, international normalized ratio, D-dimer, blood glucose, and glycosylated hemoglobin; The medication situation includes the number of medication types, medication frequency, and drug categories. The drug categories include at least one of the following types: penicillin antibiotics, cephalosporin antibiotics, β-lactamase inhibitors and their compound formulations with β-lactam antibiotics, carbapenems and other β-lactam antibiotics, aminoglycoside antibiotics, tetracycline antibiotics, macrolide antibiotics, lincomycin antibiotics, glycopeptide antibiotics, other antibacterial antibiotics, sulfonamide antibiotics, trimethoprim antibacterial drugs, nitrofuran antibacterial drugs, quinolone antibacterial drugs, nitroimidazole antibacterial drugs, oxazolidinone antibacterial drugs, bactericidal drugs for Mycobacterium tuberculosis , bacteriostatic drugs for Mycobacterium tuberculosis , polyene antifungal drugs, azole antifungal drugs, allylamine antifungal drugs, echinocandin antifungal drugs, pyrimidine antifungal drugs, broad-spectrum antiviral drugs, antiretroviral drugs, anti-herpes virus drugs, anti-influenza virus drugs, anti-hepatitis virus drugs, drugs for controlling malaria symptoms, drugs for preventing the spread and prevention of malaria, anti-amoebic drugs, anti- trichomonas drugs, anti-leishmaniasis drugs, other antiprotozoal drugs, anti-schistosomiasis drugs, drugs against other trematodes, anti-filarial drugs, anthelmintics, broad-spectrum anthelmintics and insecticides, anlitapeworm, other anthelmintics, resuscitative central nervous system stimulants, psychostimulant central nervous system stimulants, central nervous system stimulants promoting brain metabolism, opioid analgesics, non-opioid analgesics, formic acid antipyretic-analgesic and anti-inflammatory drugs, acetic acid antipyretic-analgesic and anti-inflammatory drugs, propionic acid antipyretic-analgesic and anti-inflammatory drugs, fenamic acid antipyretic-analgesic and anti-inflammatory drugs, pyrazolone antipyretic-analgesic and anti-inflammatory drugs, aniline-based antipyretic-analgesic, and anti-inflammatory drugs, sulfonylanilide antipyretic-analgesic and anti-inflammatory drugs, oxicam antipyretic-analgesic and anti-inflammatory drugs, coxib antipyretic-analgesic and anti-inflammatory drugs, other antipyretic-analgesic and anti-inflammatory drugs, anti-gout drugs inhibiting granulocyte infiltration, anti-gout drugs promoting uric acid excretion, anti-gout drugs inhibiting uric acid production, anti-epileptic drugs regulating sodium channels, anti-epileptic drugs regulating γ-aminobutyric acid, anti-epileptic drugs for absence seizures, other anti-epileptic drugs, benzodiazepine sedative hypnotics and anticonvulsants, barbiturate sedative hypnotics and anticonvulsants, aldehyde sedative hypnotics and anticonvulsants, cyclopyrrolidone sedative hypnotics and anticonvulsants, other sedative hypnotics and anticonvulsants, DA precursor antiparkinsonian drugs, peripheral dopamine decarboxylase inhibitor antiparkinsonian drugs, catechol-O-methyltransferase inhibitor antiparkinsonian drugs, central DA receptor agonist antiparkinsonian drugs, anticholinergic antiparkinsonian drugs, monoamine oxidase-B inhibitor antiparkinsonian drugs, other antiparkinsonian drugs, phenothiazine antipsychotics, butyrophenone antipsychotics, thioxanthene antipsychotics, benzamide antipsychotics, novel structural antipsychotics, long-acting antipsychotics, benzodiazepine anxiolytics, non-benzodiazepine anxiolytics, lithium salt antimanic drugs, other antimanic drugs, tricyclic antidepressants, monoamine oxidase inhibitor antidepressants, selective 5-hydroxytryptamine reuptake inhibitor antidepressants, selective norepinephrine reuptake inhibitor antidepressants, selective 5-hydroxytryptamine and norepinephrine reuptake inhibitor antidepressants, norepinephrine and specific serotoninergic antidepressants, selective serotonin reuptake activator antidepressants, central nervous system stimulant antidepressants, other types of antidepressants, thrombolytic cerebrovascular drugs, anti-platelet aggregation cerebrovascular drugs, free radical scavenging cerebrovascular drugs, calcium antagonist cerebrovascular drugs, vasodilator cerebrovascular drugs acting directly on vascular smooth muscle, cerebrovascular drugs improving microcirculation and reducing blood viscosity, cerebrovascular drugs improving brain metabolism, other types of cerebrovascular drugs, cholinesterase inhibitor anti-senile dementia and brain metabolism improvement agents, NMDA receptor antagonist anti-senile dementia and brain metabolism improvement agents, pyrrolidone brain metabolism activators, agents enhancing brain oxygen or glucose or energy metabolism for anti-senile dementia and brain metabolism improvement, other types of anti-senile dementia and brain metabolism improvement agents, inhalational general anesthetics, intravenous general anesthetics, ester local anesthetics, amide local anesthetics, other types of local anesthetics, skeletal muscle relaxants, direct-acting cholinomimetic agents, anti-cholinesterase cholinomimetic agents, muscarinic receptor antagonist anticholinergic agents, nicotinic receptor antagonist anticholinergic agents, adrenergic drugs, αβ-receptor antagonists, α-receptor antagonists, β-receptor antagonists, selective calcium channel blockers, non-selective calcium channel blockers, cardiac glycosides, non-glycoside positive inotropic agents, enkephalin inhibitors, sodium channel blocker antiarrhythmic agents, β-receptor blockers, action potential prolongation antiarrhythmic agents, calcium channel blockers, nitrate anti-anginal agents, nitrite anti-anginal agents, other anti-anginal agents, calcium channel blocker peripheral vasodilator, peripheral vasodilators by directly dilating small vascular smooth muscles, renin inhibitor antihypertensive agents, ACEI antihypertensive agents, ARB antihypertensive agents, diuretics antihypertensive agents, calcium channel blocker antihypertensive agents, peripheral vasodilator antihypertensive agents, calcium channel opener antihypertensive agents, central antihypertensive agents, adrenergic receptor antagonist antihypertensive agents, antihypertensive agents affecting sympathetic neurotransmitters, ganglionic blocker antihypertensive agents, adrenergic anti-shock vasoactive agents, other anti-shock vasoactive agents, agents affecting cholesterol synthesis, agents affecting cholesterol absorption and transport, agents affecting lipoprotein transport and decomposition, antioxidant lipid-regulating agents, polyunsaturated fatty acid lipid-regulating agents, nauseating expectorants and irritating expectorants, mucolytic agents, mucus diluents, central cough suppressants, peripheral cough suppressants, β-adrenergic receptor agonist bronchodilators, M-cholinergic receptor antagonist bronchodilators, xanthine bronchodilators, histamine release inhibitor bronchodilators, adrenocortical hormone bronchodilators, anti-leukotriene bronchodilators, antacids, H2 receptor antagonist gastric acid secretion inhibitors, proton pump inhibitor gastric acid secretion inhibitors, selective anticholinergic gastric acid secretion inhibitors, gastrin receptor antagonist gastric acid secretion inhibitors, colloidal bismuth gastric mucosal protectants, prostaglandins and their derivatives gastric mucosal protectants, other agents for treating peptic ulcers, agents for eradicating Helicobacter pylori , gastrointestinal motility drugs, M-receptor antagonist gastrointestinal spasmolytics, other types of gastrointestinal spasmolytics, digestants, gastro-kinetic agents, thiazide antiemetic agents, antihistamine antiemetic agents, dopamine or 5-hydroxytryptamine receptor antiemetic agents, other types of antiemetic agents, emetics, bulking laxatives, irritant laxatives, lubricating laxatives, softening laxatives, antidiarrheal agents, probiotics, prebiotics, synbiotics, cell regeneration-promoting agents, transaminase-lowering and hepatoprotective agents, choleretic and hepatoprotective agents, basic metabolic agents for hepatic and cholalic diseases, detoxification and hepatoprotective agents, anti-inflammatory and hepatoprotective agents, antiviral agents for hepatic and cholalic diseases, 5-aminosalicylic acids for inflammatory bowel diseases, other agents for inflammatory bowel diseases, other digestive system drugs, agents for promoting blood coagulation system function, coagulation factor preparations, agents for inhibiting fibrinolytic system, hemostatic agents acting on blood vessels, agents for promoting thrombocytopoiesis, other coagulants, heparin anticoagulants, vitamin K antagonist anticoagulants, citrate anticoagulants, fibrinolytic agents, direct factor IIa inhibitors, direct factor Xa inhibitors, antiplatelet agents, other anticoagulants, plasma and plasma substitutes, iron anti-anemia agents, folic acid anti-anemia agents, other anti-anemia agents, traditional leukocyte growth-promoting agents, biological products for leukocyte growth promotion, plant extracts for leukocyte growth promotion, antiplatelet agents, loop diuretics, thiazine diuretics, potassium-sparing diuretics and carbonic anhydrase inhibitors, acidic salt diuretics, xanthine compound diuretics, agents for the treatment of diabetes insipidus, a receptor antagonists for the treatment of benign prostatic hyperplasia, 5α reductase inhibitors for the treatment of benign prostatic hyperplasia, androgen receptor antagonist drugs for the treatment of benign prostatic hyperplasia, posterior pituitary uterotonic agents, ergot uterotonic agents, prostaglandin uterotonic agents, agents promoting cervical ripening, anti-preterm labor agents, dopamine receptor agonist lactation-reducing agents, estrogen lactation-reducing agents, pituitary hormones and related agents, glucocorticoid agents, mineralocorticoid agents, weak androgenic agents, androgenic and anabolic agents, estrogenic agents, progestogenic agents, estrogen receptor modulators, gonadotropin agents, short-acting oral contraceptives, long-acting contraceptives, external contraceptives, male contraceptives, glucagon, ultra-short-acting insulin, short-acting insulin, medium-acting insulin, long-acting insulin, ultra-long-acting insulin, premixed insulin, sulfonylurea hypoglycemic agents, insulin secretagogue hypoglycemic agents, α-glucosidase inhibitor hypoglycemic agents, biguanide hypoglycemic agents, thiazolidinedione hypoglycemic agents, DDP-4 inhibitor hypoglycemic agents, SGLT-2 inhibitor hypoglycemic agents, GLP-1 inhibitor hypoglycemic agents, anti-obesity agents, thyroid hormone agents, anti-thyroid agents, bone resorption inhibitors, bone formation promoters, drugs for hyperosteogeny, antihistamines, agents for blocking histamine release, other anti-allergic agents, calcineurin inhibitors, anti-proliferative agents, polyclonal or monoclonal antibody immunosuppressants, traditional Chinese medicine immunosuppressants, immune enhancers, alkylating agents, anti-metabolic anti-tumor agents, anti-tumor antibiotics, plant-derived anti-tumor agents and their derivatives, anti-tumor hormone agents, anti-tumor targeted drugs, other anti-tumor agents and adjuvant therapy agents, vitamin A or vitamin D agents, vitamin B agents, vitamin C and other vitamins, enzyme agents, other biochemical preparations, electrolyte balance regulators, acid-base balance regulators, other agents for regulating water or electrolyte or acid-base balance, compound electrolyte infusion and dialysis solution, general enteral nutrition agents, disease-specific enteral nutrition agents, amino acid parenteral nutrition agents, fat emulsion parenteral nutrition agents, other types of parenteral nutrition agents, agents for the treatment of glaucoma, agents for the treatment of dry eye, vascular endothelial growth factor inhibitor ophthalmic agents, agents for the treatment of cataract, other ophthalmic agents, otorhinolaryngological and dental agents, anti-infective dermatological agents, disinfectant and antiseptic dermatological agents, skin cleansers, corticosteroid dermatological agents, disinfectant and antiseptic astringents, detoxification agents for metal poisoning, detoxification agents for organophosphate poisoning, detoxification agents for cyanide poisoning, detoxification agents for organic fluorine poisoning, detoxification agents for benzodiazepine poisoning, detoxification agents for morphine poisoning, detoxification agents for acetaminophen poisoning and other detoxification agents, agents for the prevention and treatment of radiation sickness, tract contrast media and intravascular drug delivery enhancement contrast media, gastrointestinal contrast media, bronchial contrast media, lymph contrast media, MRI contrast media, ultrasound contrast media, organ function examination and other diagnostic agents, biological products for prevention, biological products for treatment, in vivo diagnostic reagents, formula for relieving superficies syndrome with pungent and warm natured drugs in internal medicine, formula for 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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;

the algorithm of the machine learning model is selected from the boosting ensemble learning models.

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 algorithm of the machine learning model is selected from Random Forest.

The present invention also provides an ADR prediction system for elderly patients, which comprises:

the data acquisition and storage module, used for acquiring and storing ADR data of elderly patients; the prediction module, integrating an ADR prediction model for elderly patients, obtained by the above method for constructing an ADR prediction model for elderly patients, which is used to calculate the ADR-related characteristic data of the elderly patients, thereby obtaining the prediction results of ADR occurrence in elderly patients; the result output module, used to output the real-time prediction results from the prediction module, facilitating auxiliary decision-making for medication plans.

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.

FIG. 1 . The visualization results of different machine learning models in Example 1; wherein, (A) is the Receiver Operating Characteristic (ROC) curve, and (B) is the precision-recall curve, with “AUC” representing the area under the ROC curve.

FIG. 2 . The visualization results of different machine learning models in Example 2; wherein, (A) is the Receiver Operating Characteristic (ROC) curve, and (B) is the precision-recall curve, with “AUC” representing the area under the ROC curve.

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.