Predicting Antibiotic Resistance in ICUs Patients by Applying Machine Learning in Vietnam

Abstract

Viet Tran Quoc,1 Dung Nguyen Thi Ngoc,2,3 Trung Nguyen Hoang,4 Hoa Vu Thi,4 Minh Tong Duc,4 Thanh Do Pham Nguyet,2 Thanh Nguyen Van,5 Diep Ho Ngoc,2 Giang Vu Son,6 Thanh Bui Duc7 1Intensive Care Unit, Military Hospital 175, Ho Chi Minh City, Vietnam; 2Department of Military Science and Training, Military Hospital 175, Ho Chi Minh City, Vietnam; 3Hanoi University of Public Health, Hanoi, Vietnam; 4Department of Military Hygiene, Vietnam Military Medical University, Hanoi, Vietnam; 5Department of General Planning, Military Hospital 175, Ho Chi Minh City, Vietnam; 6Department of Personnel, Military Hospital 175, Ho Chi Minh City, Vietnam; 7Institute of Trauma and Orthopedics, Military hospital 175, Ho Chi Minh City, VietnamCorrespondence: Thanh Bui Duc, Military Hospital 175, 786 Nguyen Kiem, 3 Ward, Go Vap District, Ho Chi Minh City, Vietnam, Tel [phone], Email [email protected]: Artificial Intelligence (AI) and machine learning (ML) are used extensively in HICs to detect and control antibiotic resistance (AMR) in laboratories and clinical institutions. ML is designed to predict outcome variables using an algorithm to enable “machines” to learn the “rules” from the data. ML is increasingly being applied in intensive care units to identify AMR and to assist empiric antibiotic therapy. This study aimed to evaluate the performance of ML models for predicting AMR bacteria and resistance to antibiotics in two Vietnamese hospitals.Patients and Methods: A cross-sectional study combined with retrospective was conducted from 1st January 2020 to 30th June 2022. Five models were developed to predict antibiotic resistance of bacterial infections of ICU patients. Two datasets were prepared to predict AMR bacteria and antibiotics with ML models. The performance of the prediction models was evaluated by various indicators (sensitivity, specificity, precision, accuracy, F1-score, PRC, AuROC, and NormMCC) to determine the optimal time point for data selection. Python version 3.8 was used for statistical analyses.Results: The accuracy, F1-score, AuROC, and normMMC of LightGBM, XGBoost, and Random Forest models were higher than those of other models in both datasets. In both datasets 1 and 2, accuracy, F1-score, AuROC and normMCC of the XGBoost model were the highest among five models (from 0.890 to 1.000). Only Random Forest models had specificity scores higher than 0.850. High scores of sensitivity, accuracy, precision, F1-score, and normMCC indicated that the models were making accurate predictions for datasets 1 and 2.Conclusion: XGBoost, LightGBM, and Random Forest were the best-performed machine learning models to predict antibiotic resistance of bacterial infections of ICUs patients using the patients’ EMRs.Keywords: antibiotic resistance, machine learning, XGBoost, LightGBM, random forest

Keywords

• antibiotic resistance
• machine learning
• xgboost
• lightgbm
• random forest.