Simple Summary Many patient clinical characteristics, such as diagnosis dates, biomarker
status, and therapies received, are only available as unstructured text in electronic health …

Simple Summary Obtaining and structuring information about the characteristics, treatments,
and outcomes of people living with cancer for research purposes is difficult and resource …

Background: As artificial intelligence (AI) continues to advance with breakthroughs in natural
language processing (NLP) and machine learning (ML), such as the development of models …

Most detailed patient information in real-world data (RWD) is only consistently available in
free-text clinical documents. Manual curation is expensive and time consuming. Developing …

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

Introduction Real-world evidence is important in regulatory and funding decisions. Manual
data extraction from electronic health records (EHRs) is time-consuming and challenging to …

Although randomized controlled trials (RCTs) are the gold standard for establishing the
efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real …

Objectives Increased adoption of electronic health records has resulted in increased
availability of free text clinical data for secondary use. A variety of approaches to obtain …

Electronic health records (EHR) contain a lot of valuable information about individual
patients and the whole population. Besides structured data, unstructured data in EHRs can …

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