PURPOSE Robust institutional tumor banks depend on continuous sample curation or else
subsequent biopsy or resection specimens are overlooked after initial enrollment. Curation …

PURPOSE Typically stored as unstructured notes, surgical pathology reports contain data
elements valuable to cancer research that require labor-intensive manual extraction …

Pathology reports are considered the gold standard in medical research due to their
comprehensive and accurate diagnostic information. Natural language processing (NLP) …

Objective We implement 2 different multitask learning (MTL) techniques, hard parameter
sharing and cross-stitch, to train a word-level convolutional neural network (CNN) …

Objective To help cancer registrars efficiently and accurately identify reportable cancer
cases. Material and Methods The Cancer Registry Control Panel (CRCP) was developed to …

Introduction Routinely collected healthcare data are a powerful research resource, but often
lack detailed disease-specific information that is collected in clinical free text such as …

Objective: We explored how a deep learning (DL) approach based on hierarchical attention
networks (HANs) can improve model performance for multiple information extraction tasks …

Objective Clinical registries—structured databases of demographic, diagnosis, and
treatment information—play vital roles in retrospective studies, operational planning, and …

PURPOSE Natural language processing (NLP) techniques have been adopted to reduce
the curation costs of electronic health records. However, studies have questioned whether …

Background: Free-text sections of pathology reports contain the most important information
from a diagnostic standpoint. However, this information is largely underutilized for computer …