Abstract: Electronic Health Records 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. Existing approaches focus mostly on structured data and a subset of single-domain outcomes. We will explore how temporal modelling of patients from free text and structured data, using deep generative transformers can be used to forecast a wide range of future disorders, substances, procedures or findings.

Biography: Zeljko Kraljevic, Research Fellow in Health Informatics, IoPPN is working on large language models for the prediction of patients' future.

Twitter account: @zeljkokr

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