Machine Learning and Antibiotic Management

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.

Predicting over-the-counter antibiotic use in rural Pune, India, using machine learning methods

OBJECTIVES

Over-the-counter (OTC) antibiotic use can cause antibiotic resistance, threatening global public health gains. To counter OTC use, this study used machine learning (ML) methods to identify predictors of OTC antibiotic use in rural Pune, India.

METHODS

The features of OTC antibiotic use were selected using stepwise logistic, lasso, random forest, XGBoost, and Boruta algorithms. Regression and tree-based models with all confirmed and tentatively important features were built to predict the use of OTC antibiotics. Five-fold cross-validation was used to tune the models' hyperparameters. The final model was selected based on the highest area under the curve (AUROC) with a 95% confidence interval (CI) and the lowest log-loss.

RESULTS

In rural Pune, the prevalence of OTC antibiotic use was 35.9% (95% CI, 31.6 to 40.5). The perception that buying medicines directly from a medicine shop/pharmacy is useful, using antibiotics for eye-related complaints, more household members consuming antibiotics, and longer duration and higher doses of antibiotic consumption in rural blocks and other social groups were confirmed as important features by the Boruta algorithm. The final model was the XGBoost+Boruta model with 7 predictors (AUROC, 0.934; 95% CI, 0.891 to 0.978; log-loss, 0.279) log-loss.

CONCLUSIONS

XGBoost+Boruta, with 7 predictors, was the most accurate model for predicting OTC antibiotic use in rural Pune. Using OTC antibiotics for eye-related complaints, higher consumption of antibiotics and the perception that buying antibiotics directly from a medicine shop/pharmacy is useful were identified as key factors for planning interventions to improve awareness about proper antibiotic use.

Chinese experts' consensus on the application of intensive care big data

The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.

Decisions regarding antibiotic prescribing for acute sinusitis in Norwegian general practice. A qualitative focus group study

BACKGROUND

Acute sinusitis is a frequent reason for primary care visits. Most patients recover within two weeks without antibiotic treatment. Despite this, about 50% of patients with acute sinusitis in Norwegian general practice are still prescribed antibiotics. We do not know the reason behind this discrepancy.

AIM

To explore the clinical decision-making process and reasons for treatment with antibiotics for acute sinusitis among Norwegian general practitioners (GPs).

METHODS

Five focus group interviews were conducted (N = 25) in different parts of Norway, including GPs of various age, gender, and experience. The interviews were analysed using Systematic Text Condensation.

RESULTS

The results showed a very diverse management of acute sinusitis among GPs, with decisions regarding antibiotics not always aligning with guideline recommendations. Many of the GPs did not agree with the Norwegian guidelines for antibiotics and chose something other than phenoxymethylpenicillin as their first choice. Clinical predictors emphasized in decision-making were pain complaints and patient exhaustion. Pragmatic factors such as weekday, travel plans, or a full waiting room could also influence the decision.

CONCLUSION

GPs found it difficult to identify when patients would benefit from antibiotic treatment for acute sinusitis, and different strategies were used to make prescribing decisions. For several GPs the degree of pain was one of the decisive reasons for antibiotic prescribing, however the guidelines for antibiotics do not give sufficient advice regarding pain treatment. These results suggest a need for revaluation of guideline contents and the way they are communicated to GPs.