Using machine learning techniques to predict antimicrobial resistance in stone disease patients

Machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy

Patients after holmium laser lithotripsy have a certain probability of getting postoperative infection. An early and accurate diagnosis of postoperative infection allows a timely administration of appropriate antibiotic treatment. However, doctors can not accurately determine whether the patient has the infection. Here, a novel strategy is put forward to assist in predicting postoperative infection early by using machine learning methods. We retrospectively collected 1006 cases of patients with urinary stone after treatment of holmium laser lithotripsy from Zhejiang Provincial People's Hospital. Feature engineering was added to filter the important characteristics and Miceforest multiple imputation method was applied to tackle the missing data problem. We used 5-fold cross-validation to train and validate the six machine learning methods. Besides, we could also find key variables important to postoperative infection by explaining the model. The hyperparameters were constantly adjusted to achieve the best performance of model. The result showed that LR had a best performance in independent datasets with AUC of 0.734. And the SHAP values indicated that preoperative urine leukocyte count was the most important variable to the prediction. Our study enables accurate predictions of infection in urology perioperative periods, the key variables can be interpreted better and more accurately to support clinical decision making.

Artificial intelligence and its application in clinical microbiology

INTRODUCTION

Traditional microbiological diagnostics face challenges in pathogen identification speed and antimicrobial resistance (AMR) evaluation. Artificial intelligence (AI) offers transformative solutions, necessitating a comprehensive review of its applications, advancements, and integration challenges in clinical microbiology.

AREAS COVERED

This review examines AI-driven methodologies, including machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs), for enhancing pathogen detection, AMR prediction, and diagnostic imaging. Applications in virology (e.g. COVID-19 RT-PCR optimization), parasitology (e.g. malaria detection), and bacteriology (e.g. automated colony counting) are analyzed. A literature search was conducted using PubMed, Scopus, and Web of Science (2018-2024), prioritizing peer-reviewed studies on AI's diagnostic accuracy, workflow efficiency, and clinical validation.

EXPERT OPINION

AI significantly improves diagnostic precision and operational efficiency but requires robust validation to address data heterogeneity, model interpretability, and ethical concerns. Future success hinges on interdisciplinary collaboration to develop standardized, equitable AI tools tailored for global healthcare settings. Advancing explainable AI and federated learning frameworks will be critical for bridging current implementation gaps and maximizing AI's potential in combating infectious diseases.

Artificial intelligence in antimicrobial stewardship: a systematic review and meta-analysis of predictive performance and diagnostic accuracy

The increasing threat of antimicrobial resistance has prompted a need for more effective antimicrobial stewardship programs (AMS). Artificial intelligence (AI) and machine learning (ML) tools have emerged as potential solutions to enhance decision-making and improve patient outcomes in AMS. This systematic review and meta-analysis aims to evaluate the impact of AI in AMS and to assess its predictive performance and diagnostic accuracy. We conducted a comprehensive literature search across PubMed/MEDLINE, Scopus, EMBASE, and Web of Science to identify studies published up to July 2024. Studies included were observational, cohort, or retrospective, focusing on the application of AI/ML in AMS. The outcomes assessed were the area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). We calculated the mean pooled effect size (ES) and its 95% confidence interval (CI) using a random-effects model. The risk of bias was assessed using the QUADAS-AI tool, and the protocol was registered in PROSPERO. Out of 3,458 retrieved articles, 80 studies met the inclusion criteria. Our meta-analysis demonstrated that ML models exhibited strong predictive performance and diagnostic accuracy, with the following results: AUC [ES: 72.28 (70.42-74.14)], accuracy [ES: 74.97 (73.35-76.58)], sensitivity [ES: 76.89; (71.90-81.89)], specificity [ES: 73.77; (67.87-79.67)], NPV [ES:79.92 (76.54-83.31)], and PPV [ES: 69.41 (60.19-78.63)] across various AMS settings. AI and ML tools offer promising enhancements due to their strong predictive performance. The integration of AI into AMS could lead to more precise antimicrobial prescribing, reduced antimicrobial resistance, and better resource utilization.

Machine learning for predicting antimicrobial resistance in critical and high-priority pathogens: A systematic review considering antimicrobial susceptibility tests in real-world healthcare settings

BACKGROUND

Antimicrobial resistance (AMR) poses a worldwide health threat; quick and accurate identification of AMR enhances patient outcomes and reduces inappropriate antibiotic usage. The objective of this systematic review is to evaluate the efficacy of machine learning (ML) approaches in predicting AMR in critical and high-priority pathogens (CHPP), considering antimicrobial susceptibility tests in real-world healthcare settings.

METHODS

The search methodology encompassed the examination of several databases, such as PubMed/MEDLINE, EMBASE, Web of Science, SCOPUS, and SCIELO. An extensive electronic database search was conducted from the inception of these databases until November 2024.

RESULTS

After completing the final step of the eligibility assessment, the systematic review ultimately included 21 papers. All included studies were cohort observational studies assessing 688,107 patients and 1,710,867 antimicrobial susceptibility tests. GBDT, Random Forest, and XGBoost were the top-performing ML models for predicting antibiotic resistance in CHPP infections. GBDT exhibited the highest AuROC values compared to Logistic Regression (LR), with a mean value of 0.80 (range 0.77-0.90) and 0.68 (range 0.50-0.83), respectively. Similarly, Random Forest generally showed better AuROC values compared to LR (mean value 0.75, range 0.58-0.98 versus mean value 0.71, range 0.61-0.83). However, some predictors selected by these algorithms align with those suggested by LR.

CONCLUSIONS

ML displays potential as a technology for predicting AMR, incorporating antimicrobial susceptibility tests in CHPP in real-world healthcare settings. However, limitations such as retrospective methodology for model development, nonstandard data processing, and lack of validation in randomized controlled trials must be considered before applying these models in clinical practice.

The Role of Artificial Intelligence and Machine Learning Models in Antimicrobial Stewardship in Public Health: A Narrative Review

Antimicrobial resistance (AMR) poses a critical global health threat, necessitating innovative approaches in antimicrobial stewardship (AMS). Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in this domain, enabling data-driven interventions to optimize antibiotic use and combat resistance. This comprehensive review explores the multifaceted role of AI and ML models in enhancing antimicrobial stewardship efforts across healthcare systems. AI-powered predictive analytics can identify patterns of resistance, forecast outbreaks, and guide personalized antibiotic therapies by leveraging large-scale clinical and epidemiological data. ML algorithms facilitate rapid pathogen identification, resistance profiling, and real-time monitoring, enabling precise decision making. These technologies also support the development of advanced diagnostic tools, reducing the reliance on broad-spectrum antibiotics and fostering timely, targeted treatments. In public health, AI-driven surveillance systems improve the detection of AMR trends and enhance global monitoring capabilities. By integrating diverse data sources-such as electronic health records, laboratory results, and environmental data-ML models provide actionable insights to policymakers, healthcare providers, and public health officials. Additionally, AI applications in antimicrobial stewardship programs (ASPs) promote adherence to prescribing guidelines, evaluate intervention outcomes, and optimize resource allocation. Despite these advancements, challenges such as data quality, algorithm transparency, and ethical considerations must be addressed to maximize the potential of AI and ML in this field. Future research should focus on developing interpretable models and fostering interdisciplinary collaborations to ensure the equitable and sustainable integration of AI into antimicrobial stewardship initiatives.

Developing and validating a machine learning model to predict multidrug-resistant Klebsiella pneumoniae-related septic shock

Background

Multidrug-resistant Klebsiella pneumoniae (MDR-KP) infections pose a significant global healthcare challenge, particularly due to the high mortality risk associated with septic shock. This study aimed to develop and validate a machine learning-based model to predict the risk of MDR-KP-associated septic shock, enabling early risk stratification and targeted interventions.

Methods

A retrospective analysis was conducted on 1,385 patients with MDR-KP infections admitted between January 2019 and June 2024. The cohort was randomly divided into a training set (n = 969) and a validation set (n = 416). Feature selection was performed using LASSO regression and the Boruta algorithm. Seven machine learning algorithms were evaluated, with logistic regression chosen for its optimal balance between performance and robustness against overfitting.

Results

The overall incidence of MDR-KP-associated septic shock was 16.32% (226/1,385). The predictive model identified seven key risk factors: procalcitonin (PCT), sepsis, acute kidney injury, intra-abdominal infection, use of vasoactive medications, ventilator weaning failure, and mechanical ventilation. The logistic regression model demonstrated excellent predictive performance, with an area under the receiver operating characteristic curve (AUC) of 0.906 in the training set and 0.865 in the validation set. Calibration was robust, with Hosmer-Lemeshow test results of P = 0.065 (training) and P = 0.069 (validation). Decision curve analysis indicated substantial clinical net benefit.

Conclusion

This study presents a validated, high-performing predictive model for MDR-KP-associated septic shock, offering a valuable tool for early clinical decision-making. Prospective, multi-center studies are recommended to further evaluate its clinical applicability and effectiveness in diverse settings.

Artificial intelligence in urolithiasis: a systematic review of utilization and effectiveness

PURPOSE

Mirroring global trends, artificial intelligence advances in medicine, notably urolithiasis. It promises accurate diagnoses, effective treatments, and forecasting epidemiological risks and stone passage. This systematic review aims to identify the types of AI models utilised in urolithiasis studies and evaluate their effectiveness.

METHODS

The study was registered with PROSPERO. Pubmed, EMBASE, Google Scholar, and Cochrane Library databases were searched for relevant literature, using keywords such as 'urology,' 'artificial intelligence,' and 'machine learning.' Only original AI studies on urolithiasis were included, excluding reviews, unrelated studies, and non-English articles. PRISMA guidelines followed.

RESULTS

Out of 4851 studies initially identified, 71 were included for comprehensive analysis in the application of AI in urolithiasis. AI showed notable proficiency in stone composition analysis in 12 studies, achieving an average precision of 88.2% (Range 0.65-1). In the domain of stone detection, the average precision remarkably reached 96.9%. AI's accuracy rate in predicting spontaneous ureteral stone passage averaged 87%, while its performance in treatment modalities such as PCNL and SWL achieved average accuracy rates of 82% and 83%, respectively. These AI models were generally superior to traditional diagnostic and treatment methods.

CONCLUSION

The consolidated data underscores AI's increasing significance in urolithiasis management. Across various dimensions-diagnosis, monitoring, and treatment-AI outperformed conventional methodologies. High precision and accuracy rates indicate that AI is not only effective but also poised for integration into routine clinical practice. Further research is warranted to establish AI's long-term utility and to validate its role as a standard tool in urological care.

Artificial intelligence and prescription of antibiotic therapy: present and future

INTRODUCTION

In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exempt from its rapid diffusion, and various machine learning (ML) techniques, from logistic regression to deep neural networks and large language models, have been explored in the literature to support decisions regarding antibiotic prescription.

AREAS COVERED

In this narrative review, we discuss promises and challenges of the application of ML-based clinical decision support systems (ML-CDSSs) for antibiotic prescription. A search was conducted in PubMed up to April 2024.

EXPERT OPINION

Prescribing antibiotics is a complex process involving various dynamic phases. In each of these phases, the support of ML-CDSSs has shown the potential, and also the actual ability in some studies, to favorably impacting relevant clinical outcomes. Nonetheless, before widely exploiting this massive potential, there are still crucial challenges ahead that are being intensively investigated, pertaining to the transparency of training data, the definition of the sufficient degree of prediction explanations when predictions are obtained through black box models, and the legal and ethical framework for decision responsibility whenever an antibiotic prescription is supported by ML-CDSSs.

Proof of concept study on early forecasting of antimicrobial resistance in hospitalized patients using machine learning and simple bacterial ecology data

Antibiotic resistance in bacterial pathogens is a major threat to global health, exacerbated by the misuse of antibiotics. In hospital practice, results of bacterial cultures and antibiograms can take several days. Meanwhile, prescribing an empirical antimicrobial treatment is challenging, as clinicians must balance the antibiotic spectrum against the expected probability of susceptibility. We present here a proof of concept study of a machine learning-based system that predicts the probability of antimicrobial susceptibility and explains the contribution of the different cofactors in hospitalized patients, at four different stages prior to the antibiogram (sampling, direct examination, positive culture, and species identification), using only historical bacterial ecology data that can be easily collected from any laboratory information system (LIS) without GDPR restrictions once the data have been anonymised. A comparative analysis of different state-of-the-art machine learning and probabilistic methods was performed using 44,026 instances over 7 years from the Hôpital Européen Marseille, France. Our results show that multilayer dense neural networks and Bayesian models are suitable for early prediction of antibiotic susceptibility, with AUROCs reaching 0.88 at the positive culture stage and 0.92 at the species identification stage, and even 0.82 and 0.92, respectively, for the least frequent situations. Perspectives and potential clinical applications of the system are discussed.

A machine learning approach using stone volume to predict stone-free status at ureteroscopy

INTRODUCTION

To develop a predictive model incorporating stone volume along with other clinical and radiological factors to predict stone-free (SF) status at ureteroscopy (URS).

MATERIAL AND METHODS

Retrospective analysis of patients undergoing URS for kidney stone disease at our institution from 2012 to 2021. SF status was defined as stone fragments < 2 mm at the end of the procedure confirmed endoscopically and no evidence of stone fragments > 2 mm at XR KUB or US KUB at 3 months follow up. We specifically included all non-SF patients to optimise our algorithm for identifying instances with residual stone burden. SF patients were also randomly sampled over the same time period to ensure a more balanced dataset for ML prediction. Stone volumes were measured using preprocedural CT and combined with 19 other clinical and radiological factors. A bagged trees machine learning model with cross-validation was used for this analysis.

RESULTS

330 patients were included (SF: n = 276, not SF: n = 54, mean age 59.5 ± 16.1 years). A fivefold cross validated RUSboosted trees model has an accuracy of 74.5% and AUC of 0.82. The model sensitivity and specificity were 75% and 72.2% respectively. Variable importance analysis identified total stone volume (17.7% of total importance), operation time (14.3%), age (12.9%) and stone composition (10.9%) as important factors in predicting non-SF patients. Single and cumulative stone size which are commonly used in current practice to guide management, only represented 9.4% and 4.7% of total importance, respectively.

CONCLUSION

Machine learning can be used to predict patients that will be SF at the time of URS. Total stone volume appears to be more important than stone size in predicting SF status. Our findings could be used to optimise patient counselling and highlight an increasing role of stone volume to guide endourological practice and future guidelines.

Machine learning to predict antimicrobial resistance: future applications in clinical practice?

INTRODUCTION

Machine learning (ML) is increasingly being used to predict antimicrobial resistance (AMR). This review aims to provide physicians with an overview of the literature on ML as a means of AMR prediction.

METHODS

References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, ACM Digital Library, and IEEE Xplore Digital Library up to December 2023.

RESULTS

Thirty-six studies were included in this review. Thirty-two studies (32/36, 89 %) were based on hospital data and four (4/36, 11 %) on outpatient data. The vast majority of them were conducted in high-resource settings (33/36, 92 %). Twenty-four (24/36, 67 %) studies developed systems to predict drug resistance in infected patients, eight (8/36, 22 %) tested the performances of ML-assisted antibiotic prescription, two (2/36, 6 %) assessed ML performances in predicting colonization with carbapenem-resistant bacteria and, finally, two assessed national and international AMR trends. The most common inputs were demographic characteristics (25/36, 70 %), previous antibiotic susceptibility testing (19/36, 53 %) and prior antibiotic exposure (15/36, 42 %). Thirty-three (92 %) studies targeted prediction of Gram-negative bacteria (GNB) resistance as an output (92 %). The studies included showed moderate to high performances, with AUROC ranging from 0.56 to 0.93.

CONCLUSION

ML can potentially provide valuable assistance in AMR prediction. Although the literature on this topic is growing, future studies are needed to design, implement, and evaluate the use and impact of ML decision support systems.

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

Introduction

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.

Simple and Rapid Discrimination of Methicillin-Resistant Staphylococcus aureus Based on Gram Staining and Machine Vision

Methicillin-resistant Staphylococcus aureus (MRSA) is a clinical threat with high morbidity and mortality. Here, we describe a new simple, rapid identification method for MRSA using oxacillin sodium salt, a cell wall synthesis inhibitor, combined with Gram staining and machine vision (MV) analysis. Gram staining classifies bacteria as positive (purple) or negative (pink) according to the cell wall structure and chemical composition. In the presence of oxacillin, the integrity of the cell wall for methicillin-susceptible S. aureus (MSSA) was destroyed immediately and appeared Gram negative. In contrast, MRSA was relatively stable and appeared Gram positive. This color change can be detected by MV. The feasibility of this method was demonstrated in 150 images of the staining results for 50 clinical S. aureus strains. Based on effective feature extraction and machine learning, the accuracies of the linear linear discriminant analysis (LDA) model and nonlinear artificial neural network (ANN) model for MRSA identification were 96.7% and 97.3%, respectively. Combined with MV analysis, this simple strategy improved the detection efficiency and significantly shortened the time needed to detect antibiotic resistance. The whole process can be completed within 1 h. Unlike the traditional antibiotic susceptibility test, overnight incubation is avoided. This new strategy could be used for other bacteria and represents a new rapid method for detection of clinical antibiotic resistance. IMPORTANCE Oxacillin sodium salt destroys the integrity of the cell wall of MSSA immediately, appearing Gram negative, whereas MRSA is relatively stable and still appears Gram positive. This color change can be detected by microscopic examination and MV analysis. This new strategy has significantly reduced the time to detect resistance. The results show that using oxacillin sodium salt combined with Gram staining and MV analysis is a new, simple and rapid method for identification of MRSA.

Urinary Tract Infections: Prevention, Diagnosis, and Treatment

Urinary tract infections (UTIs) are common pathologies that can affect patients of every age and background [...].

A 2-Year Audit on Antibiotic Resistance Patterns from a Urology Department in Greece

PURPOSE

The high incidence of urinary tract infections (UTIs), often in nosocomial environments, is a major cause of antimicrobial resistance (AMR). The dissemination of antibiotic-resistant infections results in very high health and economic burdens for patients and healthcare systems, respectively. This study aims to determine and present the antibiotic resistance profiles of the most common pathogens in a urology department in Greece.

METHODS

During the period 2019-2020, we included 12,215 clinical samples of blood and urine specimens that tested positive for the following pathogens: Escherichia coli, Enterococcus faecium, Enterococcus faecalis, Proteus mirabilis, Klebsiella pneumoniae, or Pseudomonas aeruginosa, as these are the most commonly encountered microbes in a urology department.

RESULTS

The analysis revealed a 22.30% mean resistance rate of E. coli strains with a 76.42% resistance to ampicillin and a 54.76% resistance rate to ciprofloxacin in the two-year period. It also showed an approximately 19% resistance rate of P. mirabilis strains and a mean resistance rate of 46.205% of K. pneumoniae strains, with a decreasing trend during the four semesters (p-value < 0.001), which presented an 80% resistance rate to ampicillin/sulbactam and 73.33% to ciprofloxacin. The resistance to carbapenems was reported to be 39.82%. The analysis revealed a 24.17% mean resistance rate of P. aeruginosa with a declining rate over the two-year period (p-value < 0.001). The P. aeruginosa strains were 38% resistant to fluoroquinolones and presented varying resistance against carbapenems (31.58% against doripenem and 19.79% against meropenem). Regarding the Enteroccocal strains, a 46.91% mean resistance was noted for E. faecium with 100% resistance to ampicillin, and a 24.247% mean resistance rate for E. faecalis strains that were 41% resistant to ciprofloxacin. Both types showed 100% sensitivity to linezolid.

CONCLUSIONS

The dissemination of antibiotic-resistant pathogens poses the need to implement surveillance programs and, consequently, to develop strategies to prevent the emergence of such pathogens in order to optimize patient outcomes.

Innovations in Kidney Stone Removal

Urolithiasis is a common clinical condition, and surgical treatment is performed with different minimally invasive procedures, such as ureteroscopy, shockwave lithotripsy and percutaneous nephrolithotomy. Although the transition from open surgery to endourological procedures to treat this condition has been a paradigm shift, ongoing technological advancements have permitted further improvement of clinical outcomes with the development of modern equipment. Such innovations in kidney stone removal are new lasers, modern ureteroscopes, development of applications and training systems utilizing three-dimensional models, artificial intelligence and virtual reality, implementation of robotic systems, sheaths connected to vacuum devices and new types of lithotripters. Innovations in kidney stone removal have led to an exciting new era of endourological options for patients and clinicians alike.

Artificial Intelligence Applications in Clinical Chemistry

Artificial intelligence (AI) applications are an area of active investigation in clinical chemistry. Numerous publications have demonstrated the promise of AI across all phases of testing including preanalytic, analytic, and postanalytic phases; this includes novel methods for detecting common specimen collection errors, predicting laboratory results and diagnoses, and enhancing autoverification workflows. Although AI applications pose several ethical and operational challenges, these technologies are expected to transform the practice of the clinical chemistry laboratory in the near future.

Using Machine Learning to Predict Antimicrobial Resistance-A Literature Review

Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician's point of view.