2.1. Mechanisms of Resistance

Carbapenem resistance arises through several mechanisms, primarily involving enzymatic degradation, efflux pumps, and porin mutations [8,9]. One of the most critical mechanisms is the production of carbapenemases, a group of enzymes that can hydrolyze carbapenems and other β-lactams, rendering these potent antibiotics ineffective. Notable carbapenemases include KPC (Klebsiella pneumoniae carbapenemase), NDM (New Delhi metallo-β-lactamase), VIM (Verona integron-encoded metallo-β-lactamase), and OXA-48 (Oxacillinase-48) [7,8]. These enzymes differ in their genetic origins and geographical distribution, but they all confer high levels of resistance, making infections difficult to treat [9]. The widespread dissemination of these enzymes is largely driven by plasmids, which facilitate the transfer of resistance genes across different bacterial species, thus exacerbating the problem of multidrug resistance [8,9].

Furthermore, efflux pumps are able to actively extrude a wide range of different antibiotics out of the bacterial cells, being one of the most important causes of resistance to carbapenems. In general, this decreases the intracellular drug content to subtherapeutic levels. This not only leads to reduced efficacy of carbapenems but also ensures that bacteria can survive even in environments of high antibiotic pressure. Overexpression of efflux pumps can be driven by genetic changes, or an alteration in regulation, often through the influence of the presence of antibiotics. This underscores the dynamic nature of bacterial adaptation [10].

Besides enzymatic degradation and active efflux mechanisms, the third important mechanism of resistance is changes in the permeability of bacterial cell membranes. It most frequently occurs in the form of mutations in the porin proteins, leading to decreased expression or a complete loss of specific porin channels, acting as portals for antibiotics to cross the bacterial cell membrane. These mutations result in a reduced uptake of carbapenems, preventing the antibiotics from reaching their intracellular targets, such as penicillin-binding proteins [11]. The combined effect of porin loss and cabapenemase production can result in extremely high levels of resistance, posing a significant challenge to treatment [12].

Resistance mechanisms are further complicated by the ability of bacteria to share resistance genes through horizontal gene transfer (HGT). This process allows for the rapid spread of resistance determinants between different bacteria, even across species and genera. HGT occurs through various means, including transformation, transduction, and conjugation, with conjugative plasmids being particularly important in the spread of carbapenem resistance [13]. The global spread of carbapenemase-producing Enterobacteriaceae (CPE) is a testament to the role of HGT in the dissemination of resistance, making it a critical factor in the ongoing battle against antibiotic resistance [7].

The combination of the above resistance mechanisms creates a complex challenge in the control of carbapenem-resistant infections (CRIs) [14]. A multifaceted approach involving new antibiotic development, use of combination therapy, and strict infection control practices is required to limit the dissemination of the resistant bacterial strains. In addition, ongoing surveillance and research on the molecular mechanisms of resistance maintain an edge in this continuing battle [12].

2.2. Epidemiology and Incidence of Carbapenem-Resistant Organisms

Resistance to carbapenem has escalated into a health problem worldwide, especially during the last decade. The most threatening ones are the carbapenem-resistant Enterobacteriaceae (CRE), carrying the most concerning ability to cause severe and frequently untreatable infections. The presence of these bacteria has been documented in multiple geographical areas, with strikingly high rates in countries that face specific problems related to poor antibiotic stewardship practices, as well as where overuse of antibiotics occurs both in healthcare and agriculture [15,16].

However, the effects of the emergence and spread of CRE have varied among different European countries. For example, in countries such as Greece and Italy, challenges related to CRE have been especially prominent [16]. The combination of factors, such as heavy antibiotic consumption, high rates of hospital-acquired infections, and poor infection control measures, has contributed to the wide dissemination of this type of resistance, with the majority of outbreaks occurring in healthcare environments [17]. Recent studies showed that hospital-acquired infections in Greece account for approximately 316,000 hospital bed days and EUR 73 million in costs due to resistant pathogens, particularly Klebsiella pneumoniae and Escherichia coli, with over 50% of K. pneumoniae isolates being carbapenem-resistant [18].

It is no different in Asia, as high levels of carbapenem resistance have been reported from a number of countries, such as India and China, where antibiotics are freely available without prescription, healthcare facilities are possibly limited or under-resourced, and there is massive use of antibiotics in agriculture and aquaculture. The carbapenemase-producing organisms (CPOs) have presented tremendous challenges to public health. In India, there have been reports of a high prevalence of CPOs, particularly those carrying the New Delhi metallo-β-lactamase (NDM) gene, in both community-acquired and nosocomial infections [15]. For instance, a study reported that 90.3% of carbapenem-resistant bacteria were carbapenemase producers, with NDM-1 being the most dominant at 47% [19]. Equally burdened by a huge population and significant infectious disease load, China has likewise reported increasing incidences of CRE. Between 2012 and 2016, 85.7% of CRE strains in China carried carbapenemase genes, with KPC being predominant in K. pneumoniae, and NDM in E. coli and Enterobacter cloacae. Moreover, the resistance rates of K. pneumoniae to meropenem in China have reached 24.2%, underscoring the severity of the problem [19].

The United States remains no exception to the impending threat of CR pathogens. Reports of CRE infections are increasing all over the country, posing significant threats to public health. The CDC has defined CRE as an urgent threat, and there has been much concern expressed over the potential to spread in healthcare settings. More than 2.8 million antibiotic-resistant infections occur in the U.S. each year, resulting in over 35,000 deaths. CRE, in particular, has been responsible for 13,100 cases and 1100 deaths annually [20]. The outbreaks of CRE in the U.S. healthcare facilities have been associated with high morbidity and mortality, pointing to the need for an improved infection control practice and antibiotic stewardship [20].

Besides CRE, other CR pathogens include Pseudomonas aeruginosa and Acinetobacter baumannii. These are part of the problem and considerably affect the outcomes of treatment, the prevalence of which has increased with time. Community-acquired pneumonia due to CR A. baumannii (CRAB) is also rampant in healthcare settings, especially in the ICUs, with resistance rates described at over 30–90% in regions such as Asia, Eastern Europe, and Latin America [21].

Even though it varies across different regions, CR P. aeruginosa (CR-PA) shows similar alarming resistance rates worldwide [22]. In the United States, CR-PA is a significant healthcare-associated pathogen, responsible for 10–30% of P. aeruginosa isolates. A study highlighted that carbapenemase-producing P. aeruginosa is frequently found in ventilator-associated pneumonia (VAP) and catheter-related urinary tract infections, contributing to longer hospital stays and higher mortality rates [22]. Both these non-fermenting CR pathogens, including A. baumannii, lead to increased healthcare burdens, accounting for over 80% of CR cases in a five-year U.S.-based study [23].

A systematic review of carbapenem resistance in animals, foods, and the environment on the African continent further emphasized the widespread nature of this issue. The review found a pooled prevalence of 19.1% across animal, environmental, and food ecosystems, highlighting Escherichia spp. (53.5%), Klebsiella spp. (35.4%), and Pseudomonas spp. (15.7%) as the predominant CRB species. The most common carbapenemase genes reported were from the blaOXA (52.4%) and blaNDM (40.5%) families. These findings suggest that animal–environment–food ecosystems play a significant role as reservoirs for CRE and other carbapenem-resistant bacteria, further driving their dissemination into human populations [24].

This global and multisectoral spread of carbapenem resistance highlights the urgent need for enhanced surveillance, infection control, and new antibiotics targeting both fermenting and non-fermenting CR pathogens, as well as stricter antibiotic stewardship practices across all sectors.

3.2. Traditional Epidemiological Approaches to Studying AMR

Traditional epidemiological methods have played a key role in understanding the dissemination and impact of AMR. Epidemiological approaches often involve observational studies, such as cohort, case-control, and cross-sectional studies, which help identify risk factors associated with the emergence and distribution of resistance. Surveillance systems, such as the WHO Global Antimicrobial Resistance Surveillance System (GLASS), collect and analyze data on AMR trends in different regions, relying on laboratory data to monitor resistance models [16]. These systems provide critical information to guide public health interventions and antibiotic stewardship programs.

Statistical modeling is another cornerstone of traditional epidemiological methods in AMR research. Regression models, for instance, are commonly used to examine the relationship between antibiotic exposure and the emergence of resistance, adjusting for potential confounding factors. While traditional approaches have provided valuable insights into the epidemiology of AMR, they often rely on certain assumptions and may struggle to capture the complexity and non-linearity of resistance dynamics [31]. Nonetheless, these methods remain fundamental for generating evidence that guides effective public health strategies to combat AMR.

4.2. Key Algorithms and Applications

Machine learning algorithms vary in complexity and application. Some commonly used algorithms for building predictive models to forecast AMR trends, evaluate resistance risk, and provide decision support for treatment planning include the following:

• Linear regression, which is used for predicting a continuous target variable based on one or more predictor variables [44].

• Decision trees, with a flowchart-like structure, where each internal node represents a decision based on an attribute, and each leaf node represents an outcome [45].

• Support vector machine (SVM), which is a classification method that finds the hyperplane that best separates the data into classes [46].

• Neural networks and deep learning models are inspired by the human brain’s structure, capable of learning complex patterns from large datasets, used extensively in image and speech recognition. In the context of deep learning, an artificial neural network with more than one hidden layer is referred to as deep learning, distinguishing it from simpler models with fewer layers [47].

A diagram that outlines some commonly used algorithms in predictive modeling of AMR is shown in Figure 1.

Open in a separate window

Figure 1

A diagram that outlines some commonly used algorithms in predictive modeling of AMR.

4.3. Bridging Terminology: Aligning Epidemiology and Machine Learning Concepts

In the fields of epidemiology and ML, certain terminologies and concepts align closely, despite their usage in different contexts. In epidemiology and biostatistics, terms such as dependent variable, outcome variable, and response variable are used to refer to the variable that is being measured or predicted. In ML and statistical modeling, this concept corresponds to the label or class that the model aims to predict. Conversely, independent variables, predictor variables, and explanatory variables in epidemiology are equivalent to features in ML, which are the inputs or attributes used to predict the label [31].

A common tool in epidemiological studies is the contingency table or 2 × 2 table, which displays the relationship between two categorical variables. In ML, this is referred to as a confusion matrix, which is used to evaluate the performance of classification models by showing the actual versus predicted classifications. Sensitivity in epidemiology, also known as recall in ML, measures the true-positive rate of a test. The positive predictive value in epidemiology, which assesses how many of the positive test results are true positives, is analogous to precision in ML [31].

When discussing outcome groups, the majority class in ML represents the outcome group with the highest frequency, while the minority class refers to the outcome group with the lowest frequency. In epidemiology, this concept is reflected in the proportion of cases in each category of the outcome variable, particularly when the outcome is categorical. This is similar to the concept of class balance in machine learning, which denotes the distribution of cases among different classes [31]. Understanding these terms and their equivalents across both fields improves clarity and facilitates effective communication when merging epidemiological insights in ML methods.

5.1. Data Sources and Preprocessing Techniques

Data sources: Both machine learning and epidemiology rely on several data sources to offer good models and meaningful outputs. Some of the main categories these data sources fall under include the following:

• Genomic data. Genomic sequences, which comprise DNA or RNA of both pathogens and hosts, are helpful in the identification of genetic markers responsible for specific traits, such as drug resistance and virulence, which become indispensable for full comprehension of infectious disease mechanisms and epidemiology [52].

• Clinical data. Patients’ electronic health records (EHRs) are a rich source of vital information, such as demographics, diagnosis, treatment, and results/outcomes. This dataset captures detailed patient histories that can be used to track disease progression and treatment responses [53].

• Environmental data. Environmental factors, such as air quality, water quality, and climatic variables, may affect dissemination of infectious diseases. Such information may even indicate changing environmental conditions and, thus, the impact on disease transmission [54].

• Sociodemographic data. Information on aspects such as the population’s economic status, density, and education level is critical for understanding disease transmission within populations. More so, such elements can bring to light health-related disparities and susceptibilities [55].

Preprocessing techniques for data preparation: To ensure the accuracy and usability of these diverse datasets, several preprocessing steps are critical, as follows:

• Data cleaning. Identification and correction of errors, inconsistencies, or incompleteness. Cleaning the data assures dependability and quality within the data and, hence, validation of ML models [56].

• Normalization. When several datasets are combined, normalization is necessary to standardize their scale. Certain algorithms are sensitive to the range of the data; thus, normalization ensures that no feature dominates the model because of a difference in its scale [57].

• Feature selection, which determines the most relevant variables. This, in turn, aids model performance by reducing dimensionality and weeding out insignificant or redundant data. It is totally focused on the most important part of the data and yields a higher performance with low computational complexity [58]. Figure 2 outlines the steps for a machine learning workflow for predictive modeling of AMR. This workflow illustrates how machine learning models handle diverse datasets for predicting AMR trends and providing clinical decision support.

  

  Open in a separate window

  Figure 2

  Machine learning workflow for predictive modeling of AMR.

Challenges and solutions in data integration: Integrating ML with epidemiological data allows for more precise modeling of disease dynamics and resistance patterns. For example, linking genomic data with clinical outcomes can reveal genetic determinants of drug resistance, while environmental and sociodemographic data provide a broader context within public health frameworks [59,60,61]. Genomic data, in particular, have become an important component of epidemiological analysis, especially in understanding the molecular mechanisms behind resistance and disease transmission. The integration with traditional epidemiological approaches has transformed how infectious diseases, such as carbapenem resistance, are studied [62].

However, combining these diverse data sources poses several challenges. A key issue is data format inconsistencies, as genomic, clinical, and environmental datasets often use different formats, complicating integration. Standardizing formats can help streamline the integration process and improve the efficiency of analysis.

Another challenge is data privacy and security, particularly when dealing with sensitive clinical and sociodemographic information. Additionally, missing data present a hurdle for comprehensive analysis. All these challenges and possible solutions are discussed in Section 7.

5.2. Predictive Modeling

Development of predictive models: ML has become a powerful tool in the fight against carbapenem resistance by enabling the development of sophisticated predictive models. These models utilize various ML techniques to analyze large and complex datasets, aiming to forecast resistance patterns and guide effective interventions [63,64,65]. Supervised learning algorithms are commonly employed, where models are trained on historical data that include information on infection cases, antibiotic usage, and resistance outcomes [66]. This training helps the models identify patterns and correlations that indicate the likelihood of resistance [67,68].

Key features incorporated into these predictive models include patient demographics, clinical history, hospital environment, and the genetic characteristics of pathogens [69]. By considering these variables, ML models provide valuable insights into the emergence and spread of carbapenem-resistant infections. For example, models can predict how changes in antibiotic prescribing practices or hospital infection control measures might impact resistance trends [70]. This proactive approach allows healthcare providers to implement timely and targeted interventions to manage and mitigate the spread of resistance [71,72,73].

Evaluation metrics for model performance: The performance of predictive models is evaluated using various metrics to ensure their accuracy and reliability. Common evaluation metrics include the following:

• Accuracy. The proportion of true results (both true positives and true negatives) among the total number of cases examined. It indicates the overall correctness of the model [74].

• Precision. The proportion of true-positive results among all positive results predicted by the model. It measures the model’s ability to correctly identify true resistance cases without including false positives [75].

• Recall (sensitivity). The proportion of true-positive results among all actual positive cases. It assesses the model’s ability to detect true resistance cases [76].

• F1 score. The harmonic mean of precision and recall, providing a single metric that balances both. It is particularly useful when the data are imbalanced, meaning the number of positive cases is much smaller than the number of negative cases [77].

• Area under the receiver operating characteristic (ROC) curve (AUROC). A plot of the true-positive rate against the false-positive rate at various threshold settings. The AUROC provides a single measure of the model’s ability to discriminate between positive and negative cases [78]. In the context of predicting antibiotic resistance, it measures how effectively the model can differentiate between cases where bacteria are resistant to an antibiotic and cases where they are not.

Case studies demonstrating successful predictions and early detection of resistance trends: Several case studies exemplify the successful application of ML in predicting and managing carbapenem resistance. Machine learning has proven highly effective in predicting BSIs and detecting antimicrobial resistance trends, particularly for carbapenem-resistant Gram-negative bacteria (CRGNB) in intensive care unit (ICU) patients. In a multicenter study from China, a random forest algorithm achieved an AUROC of 0.88 for CRGNB prediction and 0.86 for overall BSI prediction, enabling early intervention and targeted antibiotic therapies [63]. Similarly, a 2022 study demonstrated that ML algorithms, particularly random forest, could predict CRGNB carriage with 85.92% accuracy [79]. These models integrate variables, such as prior antibiotic use, mechanical ventilation, and invasive procedures, to provide real-time monitoring of resistance patterns. The early detection of CRGNB infections allows hospitals to optimize antimicrobial stewardship, reduce unnecessary use of broad-spectrum antibiotics, and better target high-risk patients, significantly improving clinical outcomes and infection control efforts.

Another study focused on developing and validating a ML-based algorithm to predict CR bacterial infections at the time of culture collection, achieving a sensitivity of 30%, a positive predictive value (PPV) of 30%, and a negative predictive value (NPV) of 99%, with Pseudomonas species accounting for 58% of the resistant infections. Integration of the model into the EHR system could enable real-time predictions, improving antibiotic stewardship by allowing early intervention and reducing unnecessary use of last-resort antibiotics [80].

Despite limitations, including reliance on single-center datasets, in most of the studies, these models showed promise for broader application, particularly in high-risk healthcare settings, as they can be easily retrained with additional data to reflect changing microbiological trends. This early detection is crucial for improving antimicrobial stewardship, reducing the unnecessary use of broad-spectrum antibiotics, and focusing treatments on high-risk patient groups. As these tools evolve, they promise to further optimize infection control strategies and enhance patient outcomes in hospital settings [81].

5.3. Epidemiological Insights

Enhancing traditional epidemiological analysis with machine learning: Machine learning (ML) significantly enhances traditional epidemiological analysis by providing advanced tools for data processing, pattern recognition, and predictive modeling [31,82]. Traditional epidemiology often relies on statistical methods that may not fully capture complex interactions within large datasets [31]. In contrast, ML algorithms can handle high-dimensional data, identify non-linear relationships, and uncover hidden patterns that are not apparent through conventional methods. This capability allows for more accurate risk assessments and targeted interventions.

Identifying risk factors and transmission patterns: ML models can integrate diverse data sources, such as genomic, clinical, environmental, and sociodemographic data, to identify risk factors and transmission patterns of AMR. For example, ML algorithms can analyze patient records to determine the factors associated with higher risks of infection with antibiotic-resistant pathogens. By mapping these factors, ML helps in understanding how resistance spreads within communities and healthcare settings [71,83,84,85].

Real-time surveillance and outbreak prediction: Real-time surveillance and outbreak prediction have become increasingly effective through the application of ML algorithms [86,87]. These algorithms excel at continuously analyzing incoming data to detect early signs of an outbreak, providing timely alerts to public health authorities and enabling them to take immediate action [88,89]. Predictive models can forecast the spread of antimicrobial resistance based on current trends, allowing for proactive measures to mitigate the impact of potential outbreaks. ML-driven surveillance systems, in particular, offer significant advantages in monitoring hospital data for unusual antibiotic resistance patterns, facilitating rapid responses to emerging threats [90,91]. For instance, a study by Caglayan developed a predictive framework using ML to identify ICU patients at risk of colonization with multi-drug-resistant organisms, including CRE [92]. The analysis of 4670 ICU admissions showed that the best-performing model achieved 82% sensitivity and 83% specificity. Among the key risk factors identified were prior stays in long-term care facilities and recent isolation procedures. This tool can be instrumental for clinicians in implementing timely infection control measures for high-risk patients, ultimately improving patient outcomes and preventing the spread of resistant infections.

5.4. Real-World Applications

Examples of machine learning in hospitals and public health: Machine learning has made notable strides in improving health outcomes by analyzing large-scale data to predict AMR trends. For instance, ML models have been applied to monitor and predict outbreaks of multidrug-resistant tuberculosis (MDR-TB). By integrating diverse data sources, such as patient records, radiomic features (such as cavitation), and sociodemographic information, ML enhances the ability to track and control the spread of MDR-TB more effectively than traditional methods [93,94]. This approach allows public health officials to allocate resources more efficiently, focusing on high-risk areas and implementing targeted interventions to curb resistance. Such predictive modeling empowers public health systems to act preemptively, potentially reducing the overall burden of drug-resistant infections.

Success stories and lessons learned from integrating machine learning with epidemiology: There are several success stories where ML integration with traditional epidemiological tools has led to significant improvements in patient care and infection control. For instance, in one large healthcare system, ML algorithms outperformed traditional scoring systems, such as the Modified Early Warning Score (MEWS), Sequential Organ Failure Assessment (SOFA), and Systemic Inflammatory Response Syndrome (SIRS), in predicting severe sepsis. The ML model used only patient age and six vital signs from electronic health records (EHRs) to enhance early sepsis detection, while also reducing alarm fatigue, a prevalent concern in patient safety [95]. This demonstrates the ability of ML to provide more precise alerts, improving both detection and clinical workflow.

Similarly, the InSight algorithm, developed at the University of California, San Francisco Medical Center, achieved impressive results in detecting sepsis and septic shock. It reached an area under the receiver operating characteristic curve (AUROC) of 0.92 for sepsis detection and 0.96 for predicting septic shock four hours before onset [96]. This ML model not only outperformed existing sepsis scoring systems but also proved to be robust in the face of missing data, adaptable across institutions through transfer learning, and generalizable to various clinical settings. Its strong performance underscores the potential of ML to drive improvements in early diagnosis and intervention, which are critical in conditions such as sepsis, where early treatment significantly improves outcomes.

Machine learning has also contributed to better antibiotic stewardship programs. One study demonstrated the effectiveness of the XGBoost algorithm (https://xgboost.readthedocs.io/en/latest/index.html, accessed on 19 August 2024) in predicting antibiotic resistance for three Gram-negative bacteria: Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa. Using data from 15,695 hospital admissions in the UK, the ML model slightly outperformed clinicians in selecting appropriate antibiotics, achieving an AUROC of 0.70 [97]. Importantly, this approach could reduce the use of broad-spectrum antibiotics by up to 40%, a key step in combating the development of further antibiotic resistance. Despite these promising results, the study called for further validation through prospective trials to ensure its effectiveness and acceptance in clinical practice.

Another notable example of ML-driven antibiotic stewardship is the successful reduction of extended-spectrum beta-lactamase (ESBL)-targeted therapies in hospital settings. An ML program identified patients at low risk for ESBL-producing pathogens, allowing for more targeted use of antibiotics instead of relying on broad empirical carbapenem use [72]. This precision-guided treatment approach not only helps preserve the efficacy of carbapenems but also reduces the risk of fostering additional resistance.

The lessons learned from these implementations emphasize several key points for integrating ML into healthcare. First, high-quality data are essential for accurate and reliable predictions. Second, interdisciplinary collaboration between data scientists, healthcare professionals, and public health officials is critical to ensure that ML models are not only computationally robust but also clinically relevant. Third, continuous evaluation and refinement of ML models are necessary to maintain their effectiveness, especially as healthcare environments and microbial landscapes evolve. Effective communication between all stakeholders ensures that the full potential of ML can be realized in improving patient outcomes and public health [98].

6.2. Future Directions

Potential advancements in machine learning algorithms and computational power: Advancements in ML algorithms and computational power are poised to significantly enhance the capabilities of epidemiological studies. The development of more sophisticated algorithms, such as deep learning models with improved architectures, can lead to better performance in detecting and predicting patterns of AMR [109]. Additionally, the increasing availability of high-performance computing resources, including cloud-based platforms and specialized hardware, such as GPUs and TPUs, allows for the processing of larger datasets and the training of more complex models [110].

Opportunities for integrating other emerging technologies (e.g., AI and big data analytics) with epidemiology: The integration of other emerging technologies with epidemiology presents numerous opportunities for advancing the field. Artificial intelligence (AI), big data analytics, and the Internet of Things (IoT) can provide valuable insights into the spread and control of AMR. For example, AI can enhance the analysis of genomic data to identify resistance genes, while big data analytics can uncover trends and correlations in vast datasets from various sources, including electronic health records and environmental sensors [111]. The IoT can facilitate real-time monitoring of environmental conditions and the spread of infectious diseases, enabling more timely and effective public health interventions [112].

Recommendations for policy and practice to maximize the impact of these interdisciplinary approaches: To maximize the impact of interdisciplinary approaches combining ML and epidemiology, several key recommendations should be implemented, as follows:

• Standardization of data collection. The establishment of standardized protocols for data collection and reporting is critical. This will enhance the quality and comparability of datasets, which are vital for the effectiveness of ML models in epidemiological studies [113].

• Investment in infrastructure. Governments and organizations should invest in the necessary infrastructure, including high-performance computing resources and secure data storage solutions, to support the integration of ML and epidemiological methods [114].

• Interdisciplinary collaboration. It is essential to foster collaboration among data scientists, epidemiologists, healthcare professionals, and policymakers. Such interdisciplinary partnerships can drive the creation of effective and practical solutions for tackling antimicrobial resistance (AMR) and other public health challenges [40].

• Ethical and regulatory frameworks. Developing comprehensive ethical and regulatory frameworks is crucial. These frameworks should address privacy concerns, data security, and the responsible use of AI and ML technologies, thereby ensuring public trust and the successful deployment of these approaches in real-world settings [115].

We acknowledge the use of the AI tool Grammarly for language refinement and coherence in the preparation of this manuscript. The intellectual content, writing, analysis, and interpretations remain entirely the work of the authors.