Recent years have witnessed widespread adoption of machine learning (ML)/deep learning
(DL) techniques due to their superior performance for a variety of healthcare applications …

Importance Natural language processing (NLP) has the potential to accelerate translation of
cancer treatments from the laboratory to the clinic and will be a powerful tool in the era of …

A long-running goal of the clinical NLP community is the extraction of important variables
trapped in clinical notes. However, roadblocks have included dataset shift from the general …

Despite the popularity of open innovation in recent years, studies examining the impact of
open innovation upon firm performance have shown mixed results. Previous empirical work …

In supervised learning model development, domain experts are often used to provide the
class labels (annotations). Annotation inconsistencies commonly occur when even highly …

The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a
wave of opportunities in computational healthcare. The interactive nature of these models …

Background: Clinical natural language processing (cNLP) systems are of crucial importance
due to their increasing capability in extracting clinically important information from free text …

Supplying a comprehensive overview of healthcare analytics research, Healthcare Data
Analytics provides an understanding of the analytical techniques currently available to solve …

Objectives The rapid expansion of biomedical literature necessitates automated techniques
to discern relationships between biomedical concepts from extensive free text. Such …

Asking annotators to explain “why” they labeled an instance yields annotator rationales:
natural language explanations that provide reasons for classifications. In this work, we …