A systematic review and meta-analysis identify 22 studies that used various methods to predict colonisation with antibiotic-resistant bacteria at the time of hospital admission. The models were chosen to focus on MRSA and CPO colonisation. The “performance” of these tools varied widely, with a sensitivity of 15–100% and specificity of 46–98.6% for MRSA, and sensitivity of 30–81.3% and specificity of 79.8–99.9% for CPO. I think my main take-away from this that simple risk tools for predicting colonisation with MRSA and CPO (which are often used to determine who to test) are pretty blunt instruments. However, the more advanced tools making use of big datasets and machine learning can take us forward in predicting the risk of MRSA and CPO colonisation at the time of admission.
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Using machine learning to super-charge anti-infective drug discovery: the case of Halicin
Yes, it’s true. There is more to HCAI & AMR (and this blog) than COVID-19! To prove it, I’m posting on something different today – the use of AI to streamline the anti-infective drug discovery process. Scientists at MIT have used machine learning (aka “deep learning”) to improve the drug discovery process, by predicting antimicrobial activity in molecules that are different from known antibiotics. This process has yielded Halicin, a promising candidate molecule for a broad-spectrum antimicrobial agent – which is, of course, a long way from clinical trials!
Are the robots taking over? The role of machine learning and AI in tackling infectious diseases
I attended a brilliant seminar at Imperial College last week on the role of machine learning and artificial intelligence (AI) in infectious management, and to a lesser extent, infection prevention and control. There’s so much potential for this exciting technology to revolutionise the way we identify, treat, and prevent the spread of infectious diseases. But, there’s also some risks – some are already asking whether the robots are taking over, and whether that is an entirely good thing!
Reflections on Infection Prevention and Control 