Mining fall-related information in clinical notes: Comparison of rule-based and novel word embedding-based machine learning approaches.

Abstract

Natural language processing (NLP) of health-related data is still an expertise demanding, and resource expensive process. We created a novel, open source rapid clinical text mining system called NimbleMiner. NimbleMiner combines several machine learning techniques (word embedding models and positive only labels learning) to facilitate the process in which a human rapidly performs text mining of clinical narratives, while being aided by the machine learning components. This manuscript describes the general system architecture and user Interface and presents results of a case study aimed at classifying fall-related information (including fall history, fall prevention interventions, and fall risk) in homecare visit notes. We extracted a corpus of homecare visit notes (n = 1,149,586) for 89,459 patients from a large US-based homecare agency. We used a gold standard testing dataset of 750 notes annotated by two human reviewers to compare the NimbleMiner's ability to classify documents regarding whether they contain fall-related information with a previously developed rule-based NLP system. NimbleMiner outperformed the rule-based system in almost all domains. The overall F- score was 85.8% compared to 81% by the rule based-system with the best performance for identifying general fall history (F = 89% vs. F = 85.1% rule-based), followed by fall risk (F = 87% vs. F = 78.7% rule-based), fall prevention interventions (F = 88.1% vs. F = 78.2% rule-based) and fall within 2 days of the note date (F = 83.1% vs. F = 80.6% rule-based). The rule-based system achieved slightly better performance for fall within 2 weeks of the note date (F = 81.9% vs. F = 84% rule-based). NimbleMiner outperformed other systems aimed at fall information classification, including our previously developed rule-based approach. These promising results indicate that clinical text mining can be implemented without the need for large labeled datasets necessary for other types of machine learning. This is critical for domains with little NLP developments, like nursing or allied health professions.