Knowledge Author: Facilitating user-driven, Domain content development to support clinical information extraction

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

DEVELOPING A CLINICAL LINGUISTIC FRAMEWORK FOR PROBLEM LIST GENERATION FROM CLINICAL TEXT

Regulatory institutions such as the Institute of Medicine and Joint Commission endorse problem lists as an effective method to facilitate transitions of care for patients. In practice, the problem list is a common model for documenting a care provider's medical reasoning with respect to a problem and its status during patient care. Although natural language processing (NLP) systems have been developed to support problem list generation, encoding many information layers - morphological, syntactic, semantic, discourse, and pragmatic - can prove computationally expensive. The contribution of each information layer for accurate problem list generation has not been formally assessed. We would expect a problem list generator that relies on natural language processing would improve its performance with the addition of rich semantic features We hypothesize that problem list generation can be approached as a two-step classification problem - problem mention status (Aim One) and patient problem status (Aim Two) classification. In Aim One, we will automatically classify the status of each problem mention using semantic features about problems described in the clinical narrative. In Aim Two, we will classify active patient problems from individual problem mentions and their statuses. We believe our proposal is significant in two ways. First, our experiments will develop and evaluate semantic features, some commonly modeled and others not in the clinical text. The annotations we use will be made openly available to other NLP researchers to encourage future research on this task and other related problems including foundational NLP algorithms (assertion classification and coreference resolution) and applied clinical applications (patient timeline and record visualization). Second, by generating and evaluating existing NLP systems, we are building an open-source problem list generator and demonstrating the performance for problem list generation using these features