Covera Health, Inc.

Ron Vianu

Daniel Robert Elgort

Benjamin L Odry

Richard J Herzog

Benjamin Sellman Suutari

Gregory Allen Dubbin

JinHyeong Park

James Robert Browning

Bryce Eakin

Robert Allen Epstein

Elizabeth Sweeney

Mattia Ciollaro

Andre Perunicic

Grant Langseth

W Nathaniel Brown

Tarmo Henrik Aijo

Irwin Keller

Jefferson Chen

Murray Becker

John Peloquin

Scott Schwartz

Xiaojin Dong

Greg Dubbin

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

In an embodiment, a computer-implemented process comprises accessing a plurality of digitally stored, unstructured medical diagnostic data; digitally displaying a first subset of the medical diagnostic data, the first subset of the medical diagnostic data including at least a first set of diagnostic reports, using a computer display device, concurrently with digitally displaying one or more quality control checklists that are specific to a medical discipline represented in the first set of diagnostic reports; receiving digital input specifying one or more errors in the first set of diagnostic reports and digitally storing the digital input in association with the first subset of medical diagnostic data; training a hierarchical Bayesian machine learning model using the digital input and the first subset of medical diagnostic data; evaluating the hierarchical Bayesian machine learning model, after training, for a second subset of the medical diagnostic data, the second subset being different from the first subset, to result in outputting one or more provider error rate data; applying a grading algorithm to the one or more provider error rate data to yield one or more output provider quality score values.

Computer-implemented detection and statistical analysis of errors by healthcare providers

For training data pairs comprising training text (a radiological report) and training images (radiological images associated with the radiological report), a first encoder network determines word embeddings for the training text. A concept is generated from the operation of layers of the first encoder network, which is regularized by a first loss between the generated concept and a labeled concept for the training text. A second encoder network determines features for the training image. A heatmap is generated from the operation of layers of the second encoder network, which is regularized by a second loss between the generated heatmap and a labeled heatmap for the training image. A categorical cross entropy loss is calculated between a diagnostic quality category (classified by an error encoder) and a labeled diagnostic quality category for the training data pair. A total loss function comprising the first, second, and categorical cross entropy losses is minimized.

Computer-implemented machine learning for detection and statistical analysis of errors by healthcare providers

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New York, NY, United States of America

Ron Vianu