Electronic Health Records (EHRs) hold detailed longitudinal information about each patient's health status and general clinical history, a large portion of which is stored within the unstructured text. Temporal modelling of this medical history, which considers the sequence of events, can be used to forecast and simulate future events, estimate risk, suggest alternative diagnoses or forecast complications. While most prediction approaches use mainly structured data or a subset of single-domain forecasts and outcomes, we processed the entire free-text portion of EHRs for longitudinal modelling. We present Foresight, a novel GPT3-based pipeline that uses NER+L tools (i.e. MedCAT) to convert document text into structured, coded concepts, followed by providing probabilistic forecasts for future medical events such as disorders, medications, symptoms and interventions. Since large portions of EHR data are in text form, such an approach benefits from a granular and detailed view of a patient while introducing modest additional noise. On tests in two large UK hospitals (King's College Hospital, South London and Maudsley) and the US MIMIC-III dataset precision@10 of 0.80, 0.81 and 0.91 was achieved for forecasting the next biomedical concept. Foresight was also validated on 34 synthetic patient timelines by 5 clinicians and achieved relevancy of 97% for the top forecasted candidate disorder. Foresight can be easily trained and deployed locally as it only requires free-text data (as a minimum). As a generative model, it can simulate follow-on disorders, medications and interventions for as many steps as required. Foresight is a general-purpose model for biomedical concept modelling that can be used for real-world risk estimation, virtual trials and clinical research to study the progression of diseases, simulate interventions and counterfactuals, and for educational purposes.

基于深度学习的图像重建方法在许多成像方式中表现出令人印象深刻的经验表现。这些方法通常需要大量的高质量配对训练数据，这在医学成像中通常不可用。为了解决这个问题，我们为贝叶斯框架内的学习重建提供了一种新颖的无监督知识转移范式。提出的方法分为两个阶段学习重建网络。第一阶段训练一个重建网络，其中包括一组有序对，包括椭圆的地面真相图像和相应的模拟测量数据。第二阶段微调在没有监督的情况下将经过验证的网络用于更现实的测量数据。通过构造，该框架能够通过重建图像传递预测性不确定性信息。我们在低剂量和稀疏视图计算机断层扫描上提出了广泛的实验结果，表明该方法与几种最先进的监督和无监督的重建技术具有竞争力。此外，对于与培训数据不同的测试数据，与仅在合成数据集中训练的学习方法相比，所提出的框架不仅在视觉上可以显着提高重建质量，而且在PSNR和SSIM方面也可以显着提高重建质量。