Developing artificial intelligence (AI) for clinical antimicrobial stewardship in an era of increasing antimicrobial resistance (AMR).

该项目特别关注管理全球健康面临的最大威胁，即由耐药细菌(抗菌素耐药性，AMR)引起的感染造成的日益沉重的负担。医生(人类)不能可靠地知道在紧急情况下使用哪种抗生素。事实上，从我们早期的工作中，他们错误地开出了一种抗生素，这种抗生素对某些常见类型的感染具有抗药性，大约有20%的时间是错误的。无论引起细菌对某些抗生素是否具有抗药性，严重的细菌感染看起来都是一样的，而且必须根据非常有限的信息选择第一种抗生素，如果患者表现出感染的迹象，就必须给予脓毒症治疗的第一个“黄金”小时。可以理解的是，这种“高风险”的不确定性促进了广谱抗生素的使用，这些抗生素应该保留下来，用于已知的耐药感染。微生物确认哪些抗生素有效，如果有的话，需要时间(通常是2-3天)，这对于最大限度地减少不必要的药物暴露来说已经太晚了，而且经常被忽视，因为患者使用广谱抗生素的情况正在“好转”。对于许多严重的感染，我们根本不知道是否存在AMR，因为生物样本没有被采集，或者它们是在开始使用抗生素对这些样本进行消毒后采集的。在不知道任何替代药物的情况下，由于担心AMR的存在，广谱抗生素经常被继续使用。急诊室的决定和第一种抗生素的选择似乎是唯一最重要的决定，不仅对于存活下来的脓毒症，而且对于解决日益严重的AMR问题所需的抗菌素管理。计算机科学有可能在第一剂就安全地解锁AMR的成功的抗菌素管理。大多数AMR感染都是由肠道、胆道和泌尿系统中的“革兰氏阴性”细菌引起的，因此在早期的工作中，我们使用了来自需要紧急住院的患者的临床和微生物学数据集，这些患者需要紧急住院治疗血液和尿液中的这些病原体。第一步是查看在紧急情况下给予哪些抗生素，给患者开一种细菌感染耐药的抗生素的频率(处方不足)，以及在另一种窄谱抗生素替代品同样有效的情况下(过量开处方)使用广谱抗生素的频率。使用患者的电子健康记录(EHR)，一种经过培训的计算机系统(或人工智能，AI)，在海量数据中找到模式，允许以与医生相同的速度(约20%的时间)开不到处方，也可以减少约40%的广谱抗生素的使用，因为预计哪些患者不太可能感染AMR。这项强大的概念验证工作显示了人工智能在第一次也是最重要的一剂(DOI：10.1093/jac/dkaa222)中逐步改变个性化药物和抗菌药物管理的巨大潜力。为AMR采取人工智能的下一步。许多其他(“革兰氏阳性”)细菌，通常生活在皮肤和其他地方，能够产生严重的感染和AMR。我们也可以针对这些问题开发、测试和建模人工智能算法，并扩大人工智能帮助更多紧急医院演示的潜力。尽管许多感染没有得到微生物的确认，但我们可以准确地从EHR中推断信息，并应用人工智能支持的处方模式。与临床结果的联系也将使人们对人工智能可能对患者护理和医疗资源产生的影响进行更广泛的评估。最终，我们需要一种工具，让一线临床医生安全地开出处方，但减少任何不必要的广谱抗生素的启动。

This project has a specific focus in managing the single greatest threat to global health, the increasing burden from infections caused by bacteria that are resistant to antibiotics (antimicrobial resistance, AMR).Doctors (humans) can't reliably know which antibiotic to administer in an emergency. In fact, from our earlier work they get it wrong about 20% of the time by prescribing an antibiotic that bacteria are resistant to for certain common types of infection. A serious bacterial infection will look the same whether the causative bacteria are resistant to certain antibiotics or not, and the first antibiotic must be selected on very limited information and be given the first 'golden' hour of sepsis management if the patient shows signs of an infection that has spread through the body. Understandably, this 'high stakes' uncertainty promotes the use of broad-spectrum antibiotics which should be held in reserve for known drug-resistant infections. Microbiological confirmation of which antibiotics are effective, when available, takes time (typically 2-3 days) which is too late for minimising un-necessary drug exposure and is often disregarded since the patient is 'getting better' on their broad-spectrum antibiotic. For many severe infections we simply never know if AMR was present, because biological samples are not taken or they were taken after the initiation of antibiotics that sterilise these samples. In the absence of knowing any alternatives, broad-spectrum antibiotics are often continued for fear of AMR being present. The emergency room decision and choice of first antibiotic seems to be the single-most important decision, not just for surviving sepsis but also for the antimicrobial stewardship needed to tackle the increasing problem of AMR. Computer science has the potential to safely unlock successful antimicrobial stewardship for AMR at the first dose. Most AMR infections arise from 'gram-negative' bacteria that live in the gut, biliary and urinary systems, so in earlier work we used linked clinical and microbiological datasets from patients who needed emergency hospital admission for these pathogens in the blood and urine. The first step was to look at which antibiotics were given at emergency presentation, how often a patient was prescribed an antibiotic that their bacterial infection was resistant to (under-prescribing), and how often a broad-spectrum antibiotic was used when another, narrow-spectrum, antibiotic alternative would have been equally effective (over-prescribing). Using a patient's electronic health record (EHR), a computer system (or artificial intelligence, AI) trained in finding patterns in vast amounts of data, that was allowed to under-prescribe at the same rate as doctors (about 20% of the time), could also reduce the use of broad-spectrum antibiotics by about 40% by anticipation of which patients were unlikely to have an AMR infection. This powerful proof-of-concept work shows the huge potential for AI in making stepwise changes towards personalised medicine and antimicrobial stewardship at the first and most important dose (full paper doi:10.1093/jac/dkaa222).Taking the next steps in AI for AMR. Many other ('gram-positive') bacteria that typically live on the skin and elsewhere are capable of serious infection and AMR. We can develop, test and model AI algorithms against these too, and broaden the potential for AI to help with more emergency hospital presentations. And even though many infections are not confirmed microbiologically, we can accurately infer information from the EHR and apply what an AI-supported prescribing pattern would look like. Linkage with clinical outcomes would also give a wider assessment of the impact AI might have on patient care and healthcare resources. Ultimately, we need a tool for front-line clinicians to safely prescribe but reduce any un-necessary initiation of broad-spectrum antibiotics.