Introduction
The wide adoption of electronic health records (EHRs) has led to an improvement in healthcare quality by electronically documenting a patient’s medical conditions, thoughts and actions among the care providers [1]. Those EHR data, with the vast majority being free-texts (e.g., clinical notes, discharge summaries, radiology reports, and pathology reports), have been utilized for primary and secondary purposes, such as documentation need in care process, clinical decision support, outcome improvement, biomedical research and epidemiologic monitoring of the nation’s health. The application of natural language processing (NLP) methods and resources to clinical and biomedical text has received growing attention over the past years, but progress has been limited by difficulties to access shared tools and resources, partially caused by patient privacy and data confidentiality constraints. Efforts to increase sharing and interoperability of the few existing resources are needed to facilitate the progress observed in the general NLP domain. Towards this goal, we organized the BioCreative/OHNLP Challenge 2018 workshop (https://sites.google.com/view/ohnlp2018/home) to promote community efforts on methodological advancements and data curation mechanisms in clinical NLP. The challenge consists of two independent clinical NLP tasks: 1) Family History Extraction; and 2) Clinical Semantic Textual Similarity. The top performing teams were invited to present their solutions during the BioCreative/OHNLP Challenge 2018 workshop in conjunction with the 9th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB) (http://acm-bcb.org/2018/) on August 30th, 2018. This supplement collects the system descriptions of top-performing solutions of the tasks.