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

该项目特别侧重于管理对全球健康的单一最大威胁，即对抗生素具有耐药性的细菌引起的感染日益增加的负担（抗菌素耐药性，AMR）。医生（人类）无法确切地知道在紧急情况下该使用哪种抗生素。事实上，从我们早期的工作来看，他们开出的抗生素有20%的几率是错误的，因为细菌对某些常见的感染有抵抗力。无论致病细菌对某些抗生素是否具有耐药性，严重的细菌感染看起来都是一样的，必须根据非常有限的信息选择第一种抗生素，如果患者显示出感染已经扩散到全身的迹象，就必须给予败血症管理的第一个“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，而广谱抗生素应该保留用于已知的耐药感染。微生物学证实哪些抗生素在可用时是有效的，需要时间（通常为2-3天），这对于尽量减少不必要的药物暴露来说太晚了，而且由于患者使用广谱抗生素正在“好转”，因此经常被忽视。对于许多严重感染，我们根本不知道是否存在抗生素耐药性，因为没有采集生物样本，或者是在开始使用抗生素对这些样本进行消毒后采集的。在不知道任何替代品的情况下，由于担心抗菌素耐药性的存在，通常继续使用广谱抗生素。急诊室的决定和第一种抗生素的选择似乎是最重要的决定，不仅是为了生存败血症，也是为了解决日益严重的抗生素耐药性问题所需的抗菌药物管理。计算机科学有可能安全地开启成功的抗微生物药物管理，在第一剂抗生素耐药性。大多数AMR感染来自生活在肠道、胆道和泌尿系统中的“革兰氏阴性”细菌，因此在早期的工作中，我们使用了相关的临床和微生物数据集，这些数据集来自那些因血液和尿液中的这些病原体而需要紧急入院的患者。第一步是查看在急诊时使用了哪些抗生素，给病人开的抗生素是他们的细菌感染有抗药性的（处方不足），以及当另一种窄谱抗生素替代方案同样有效时，使用广谱抗生素的频率（处方过量）。利用病人的电子健康记录（EHR），一个经过训练能在大量数据中发现模式的计算机系统（或人工智能，AI），被允许以与医生相同的比率（约20%的时间）少开处方，也可以减少约40%的广谱抗生素的使用，因为预期哪些病人不太可能有AMR感染。这项强有力的概念验证工作显示了人工智能在逐步改变个性化医疗和第一次和最重要剂量的抗菌药物管理方面的巨大潜力（完整论文doi:10.1093/jac/dkaa222）。在抗菌素耐药性方面采取人工智能的下一步措施。通常生活在皮肤和其他地方的许多其他（“革兰氏阳性”）细菌能够引起严重感染和抗菌素耐药性。我们也可以针对这些问题开发、测试和建模人工智能算法，并扩大人工智能的潜力，帮助更多的紧急医院演示。即使许多感染没有得到微生物学上的证实，我们也可以从电子病历中准确地推断出信息，并应用人工智能支持的处方模式。与临床结果的联系还可以更广泛地评估人工智能可能对患者护理和医疗保健资源产生的影响。最终，我们需要一种工具，让一线临床医生安全地开处方，同时减少任何不必要的广谱抗生素的使用。

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.