The records of a clinical encounter can be extensive and complex, thus placing a premium on tools that can extract and summarize relevant information.
We propose an efficient and robust neural solution for ITN leveraging transformer based seq2seq models and FST-based text normalization techniques for data preparation.
Due to advances in machine learning and artificial intelligence (AI), a new role is emerging for machines as intelligent assistants to radiologists in their clinical workflows.
no code implementations • • Olga Kovaleva, Chaitanya Shivade, Satyan Kashyap, a, Karina Kanjaria, Joy Wu, Deddeh Ballah, Adam Coy, Alex Karargyris, ros, Yufan Guo, David Beymer Beymer, Anna Rumshisky, V Mukherjee, ana Mukherjee
Using MIMIC-CXR, an openly available database of chest X-ray images, we construct both a synthetic and a real-world dataset and provide baseline scores achieved by state-of-the-art models.
MEDIQA 2019 includes three tasks: Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and Question Answering (QA) in the medical domain.
In this working notes paper, we describe IBM Research AI (Almaden) team's participation in the ImageCLEF 2019 VQA-Med competition.
State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs.
We seek to address the lack of labeled data (and high cost of annotation) for textual entailment in some domains.