Covera Health, Inc.

Jefferson Chen

Benjamin L Odry

Daniel Robert Elgort

Ron Vianu

Benjamin Sellman Suutari

Gregory Allen Dubbin

W Nathaniel Brown

Nathaniel W Brown

A natural language understanding method begins with a radiological report text containing clinical findings. Errors in the text are corrected by analyzing character-level optical transformation costs weighted by a frequency analysis over a corpus corresponding to the report text. For each word within the report text, a word embedding is obtained, character-level embeddings are determined, and the word and character-level embeddings are concatenated to a neural network which generates a plurality of NER tagged spans for the report text. A set of linked relationships are calculated for the NER tagged spans by generating masked text sequences based on the report text and determined pairs of potentially linked NER spans. A dense adjacency matrix is calculated based on attention weights obtained from providing the one or more masked text sequences to a Transformer deep learning network, and graph convolutions are then performed over the calculated dense adjacency matrix.

Computer-implemented natural language understanding of medical reports

Growing community of inventors

New York, NY, United States of America

Jefferson Chen