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Background Clinical Natural Language Processing (NLP) systems require a semantic schema comprised of domain-specific concepts, their lexical variants, and associated modifiers to accurately extract information from clinical texts. An NLP system leverages this schema to structure concepts and extract meaning from the free texts. In the clinical domain, creating a semantic schema typically requires input from both a domain expert, such as a clinician, and an NLP expert who will represent clinical concepts created from the clinician’s domain expertise into a computable format usable by an NLP system. The goal of this work is to develop a web-based tool, Knowledge Author, that bridges the gap between the clinical domain expert and the NLP system development by facilitating the development of domain content represented in a semantic schema for extracting information from clinical free-text. Results Knowledge Author is a web-based, recommendation system that supports users in developing domain content necessary for clinical NLP applications. Knowledge Author’s schematic model leverages a set of semantic types derived from the Secondary Use Clinical Element Models and the Common Type System to allow the user to quickly create and modify domain-related concepts. Features such as collaborative development and providing domain content suggestions through the mapping of concepts to the Unified Medical Language System Metathesaurus database further supports the domain content creation process. Two proof of concept studies were performed to evaluate the system’s performance. The first study evaluated Knowledge Author’s flexibility to create a broad range of concepts. A dataset of 115 concepts was created of which 87 (76 %) were able to be created using Knowledge Author. The second study evaluated the effectiveness of Knowledge Author’s output in an NLP system by extracting concepts and associated modifiers representing a clinical element, carotid stenosis, from 34 clinical free-text radiology reports using Knowledge Author and an NLP system, pyConText. Knowledge Author’s domain content produced high recall for concepts (targeted findings: 86 %) and varied recall for modifiers (certainty: 91 % sidedness: 80 %, neurovascular anatomy: 46 %). Conclusion Knowledge Author can support clinical domain content development for information extraction by supporting semantic schema creation by domain experts. Electronic supplementary material The online version of this article (doi:10.1186/s13326-016-0086-9) contains supplementary material, which is available to authorized users.

Graphical abstractDisplay Omitted Highlights? Annotated 180 clinical reports to indicate anaphor-antecedent pairs. ? Identity was the most frequent relation, with set/subset and part/whole too. ? Accurate resolution will require extensive domain knowledge. ? Annotations can be used to develop anaphoric resolution algorithms. MotivationExpressions that refer to a real-world entity already mentioned in a narrative are often considered anaphoric. For example, in the sentence "The pain comes and goes," the expression "the pain" is probably referring to a previous mention of pain. Interpretation of meaning involves resolving the anaphoric reference: deciding which expression in the text is the correct antecedent of the referring expression, also called an anaphor. We annotated a set of 180 clinical reports (surgical pathology, radiology, discharge summaries, and emergency department) from two institutions to indicate all anaphor-antecedent pairs. ObjectiveThe objective of this study is to describe the characteristics of the corpus in terms of the frequency of anaphoric relations, the syntactic and semantic nature of the members of the pairs, and the types of anaphoric relations that occur. Understanding how anaphoric reference is exhibited in clinical reports is critical to developing reference resolution algorithms and to identifying peculiarities of clinical text that may alter the features and methodologies that will be successful for automated anaphora resolution. ResultsWe found that anaphoric reference is prevalent in all types of clinical reports, that annotations of noun phrases, semantic type, and section headings may be especially important for automated resolution of anaphoric reference, and that separate modules for reference resolution may be required for different report types, different institutions, and different types of anaphors. Accurate resolution will probably require extensive domain knowledge-especially for pathology and radiology reports with more part/whole and set/subset relations. ConclusionWe hope researchers will leverage the annotations in this corpus to develop automated algorithms and will add to the annotations to generate a more extensive corpus.

We found substantial annotator variability in identifying supporting, refuting, or uncertain evidence for the diagnosis of pneumonia in clinical text. Future work will expand these methods to a larger case sample and incorporate a more formal linguistic analysis to identify specific lexical cues thereby extending existing taxonomies of uncertainty and improving automated NLP algorithms.