  Automatically summarizing patients' main problems from daily progress notes
using natural language processing methods helps to battle against information
and cognitive overload in hospital settings and potentially assists providers
with computerized diagnostic decision support. Problem list summarization
requires a model to understand, abstract, and generate clinical documentation.
In this work, we propose a new NLP task that aims to generate a list of
problems in a patient's daily care plan using input from the provider's
progress notes during hospitalization. We investigate the performance of T5 and
BART, two state-of-the-art seq2seq transformer architectures, in solving this
problem. We provide a corpus built on top of progress notes from publicly
available electronic health record progress notes in the Medical Information
Mart for Intensive Care (MIMIC)-III. T5 and BART are trained on general domain
text, and we experiment with a data augmentation method and a domain adaptation
pre-training method to increase exposure to medical vocabulary and knowledge.
Evaluation methods include ROUGE, BERTScore, cosine similarity on sentence
embedding, and F-score on medical concepts. Results show that T5 with domain
adaptive pre-training achieves significant performance gains compared to a
rule-based system and general domain pre-trained language models, indicating a
promising direction for tackling the problem summarization task.

Computation and Language;Artificial Intelligence