Title: Distantly supervised end-to-end medical entity extraction from electronic health records with human-level quality. Abstract: Medical  entity  extraction  (EE)  is  a  standard  procedure  used  as  a  first  stage  inmedical texts processing. Usually Medical EE is a two-step process: named entityrecognition (NER) and named entity normalization (NEN). We propose a novelmethod of doing medical EE from electronic health records (EHR) as a single-step multi-label classification task by fine-tuning a transformer model pretrainedon a large EHR dataset. Our model is trained end-to-end in an distantly supervisedmanner using targets automatically extracted from medical knowledge base.  Weshow that our model learns to generalize for entities that are present frequentlyenough,  achieving human-level classification quality for most frequent entities.Our  work  demonstrates  that  medical  entity  extraction  can  be  done  end-to-endwithout  human  supervision  and  with  human  quality  given  the  availability  of  alarge enough amount of unlabeled EHR and a medical knowledge base.