The world is in the midst of a pandemic. We still know little about the disease COVID-19 or about the virus (SARS-CoV-2) that causes it. We do not have a vaccine or a treatment (aside from managing symptoms). We do not know if recovery from COVID-19 produces immunity, and if so for how long, hence we do not know if “herd immunity” will eventually reduce the risk or if a successful vaccine can be developed – and this knowledge may be a long time coming. In the meantime, the COVID-19 pandemic is presenting enormous challenges to medical research, and to clinical trials in particular. This paper identifies some of those challenges and suggests ways in which machine learning can help in response to those challenges. We identify three areas of challenge: ongoing clinical trials for non-COVID-19 drugs; clinical trials for repurposing drugs to treat COVID-19, and clinical trials for new drugs to treat COVID-19. Within each of these areas, we identify aspects for which we believe machine learning can provide invaluable assistance.

世界正处于大流行之中。我们对 COVID-19 疾病或引起该疾病的病毒 (SARS-CoV-2) 仍然知之甚少。我们没有疫苗或治疗方法（除了控制症状）。我们不知道从 COVID-19 中恢复后是否会产生免疫力，如果会产生免疫力，会持续多久，因此我们不知道“群体免疫”最终是否会降低风险，或者是否可以开发出成功的疫苗——而这一知识可能是好久不见了。与此同时，COVID-19 大流行给医学研究，特别是临床试验带来了巨大的挑战。本文确定了其中一些挑战，并提出了机器学习可以帮助应对这些挑战的方法。我们确定了三个挑战领域：正在进行的非 COVID-19 药物临床试验；重新利用药物治疗 COVID-19 的临床试验，以及治疗 COVID-19 新药的临床试验。在每个领域中，我们都确定了我们认为机器学习可以提供宝贵帮助的方面。