Automatic COVID-19 pregnant patient prognosis algorithm

我们社会从COVID-19大流行中恢复过来、保护患者生命和恢复正常生活的能力不仅取决于我们早期诊断的能力，还取决于我们预测哪些患者面临严重危及生命的并发症的高风险，从而从早期干预中受益的能力。我们预测这一点的能力在怀孕患者的情况下变得极具挑战性，他们的免疫系统发生了变化，导致疾病的各种不同表现和进展。为了更好地了解COVID-19在妊娠人群中的进展情况，我们需要一个关于先前妊娠COVID-19病例的大型数据库，目前尚无此类数据库。

Our society's ability to recover from the COVID-19 pandemic, preserve patient lives, and resume normal routines depends on our ability to not only diagnose early, but also to predict which patients are at high risk of severe life-threatening complications and would, therefore, benefit from early intervention. Our ability to predict this becomes extremely more challenging in the case of pregnant patients, whose immunity is altered, leading to various and different manifestations and progression of their disease. To better understand the progression of COVID-19 in the pregnant population, we require a large data repository of prior pregnant COVID-19 cases, which is currently unavailable. To address this, our short term aim is to create a large COVID-19 in pregnancy repository from National and International sources, while ensuring there is no data duplication. Once a repository is created, it will still be very challenging to analyze the dataset manually due to non-trivial relationships between patient symptoms, clinical results, and disease outcomes. Thus, our aim is to use advanced artificial intelligence approaches with our large data repository to create a fast and reliable COVID-19 disease prognostication system for pregnant patients while accounting for possible changes in the virus' behaviour over time. As a final stage in this project, we will bring the algorithm into clinical use at Mount Sinai Hospital, one of the largest obstetric hospitals in Canada and North America, and to all Ontario partner hospitals. The repository together with the algorithm are expected to radically transform Canadian and, potentially, international healthcare providers' ability to identify, manage and treat cases of COVID-19 in pregnant patients. They will provide us with a deeper understanding of the interactions between the various pregnant patient parameters in dictating disease outcome.