Developing machine learning systems worthy of trust for infection science: a requirement for future implementation into clinical practice

Predictive analytics for early detection of hospital-acquired complications: An artificial intelligence approach

BACKGROUND

Hospital-acquired complications (HACs) have an adverse impact on patient recovery by impeding their path to full recovery and increasing healthcare costs.

OBJECTIVE

The aim of this study was to create a HAC risk prediction machine learning (ML) framework using hospital administrative data collections within North Metropolitan Health Service (NMHS), Western Australia.

METHOD

A retrospective cohort study was performed among 64,315 patients between July 2020 to June 2022 to develop an automated ML framework by inputting HAC and the healthcare site to obtain site-specific predictive algorithms for patients admitted to the hospital in NMHS. Univariate analysis was used for initial feature screening for 270 variables. Of these, 77 variables had significant relationship with any HAC. After excluding non-contemporaneous data, 37 variables were included in developing the ML framework based on logistic regression (LR), decision tree (DT) and random forest (RF) models to predict occurrence of four specific HACs: delirium, aspiration pneumonia, pneumonia and urinary tract infection.

RESULTS

All models exhibited similar performance with area under the curve scores around 0.90 for both training and testing datasets. For sensitivity, DT and RF exceeded LR performance while on average, false positives were lowest for LR-based models. Patient's length of stay, Charlson Index, operation length and intensive care unit stay were common predictors.

CONCLUSION

Integrating ML-based risk detection systems into clinical workflows can potentially enhance patient safety and optimise resource allocation. LR-based models exhibited best performance.

IMPLICATIONS

We have successfully developed a "real-time" risk prediction model, where patient risk scores are calculated and reviewed daily.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

Applying Machine Learning Techniques to Implementation Science

Machine learning (ML) approaches could expand the usefulness and application of implementation science methods in clinical medicine and public health settings. The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, such as predicting what will work best, for whom, under what circumstances, and with what predicted level of support, and what and when adaptation or deimplementation are needed. We describe how ML approaches could be used and discuss challenges that implementation scientists and methodologists will need to consider when using ML throughout the stages of implementation.

A systematic approach to microbial forensics

The coronavirus disease 2019 pandemic accelerated developments in biotechnology that underpin infection science. These advances present an opportunity to refresh the microbial forensic toolkit. Integration of novel analytical techniques with established forensic methods will speed up acquisition of evidence and better support lines of enquiry. A critical part of any such investigation is demonstration of a robust causal relationship and attribution of responsibility for an incident. In the wider context of a formal investigation into agency, motivation and intent, the quick and efficient assembly of microbiological evidence sets the tone and tempo of the entire investigation. Integration of established and novel analytical techniques from infection science into a systematic approach to microbial forensics will therefore ensure that major perspectives are correctly used to frame and shape the evidence into a clear narrative, while recognizing that forensic hypothesis generation, testing and refinement comprise an iterative process. Development of multidisciplinary training exercises that use this approach will enable translation into practice and efficient implementation when the need arises.