Abstract
ObjectivesAs electronic mental health records become more widely available, several approaches have been suggested to automatically extract information from free‐text narrative aiming to support epidemiological research and clinical decision‐making. In this paper, we explore extraction of explicit mentions of symptom severity from initial psychiatric evaluation records. We use the data provided by the 2016 CEGS N‐GRID NLP shared task Track 2, which contains 541 records manually annotated for symptom severity according to the Research Domain Criteria.MethodsWe designed and implemented 3 automatic methods: a knowledge‐driven approach relying on local lexicalized rules based on common syntactic patterns in text suggesting positive valence symptoms; a machine learning method using a neural network; and a hybrid approach combining the first 2 methods with a neural network.ResultsThe results on an unseen evaluation set of 216 psychiatric evaluation records showed a performance of 80.1% for the rule‐based method, 73.3% for the machine‐learning approach, and 72.0% for the hybrid one.ConclusionsAlthough more work is needed to improve the accuracy, the results are encouraging and indicate that automated text mining methods can be used to classify mental health symptom severity from free text psychiatric notes to support epidemiological and clinical research.
Highlights
Recent developments in the use and availability of electronic medical records (EMRs) have triggered a number of opportunities for more efficient clinical decision support and epidemiological research
The main challenge is that clinical narrative is often written with a distinct style, seldom conforming to standard grammar, frequently with spelling and typing errors as well as common abbreviations and acronyms, with their meaning being often ambiguous depending on the context (Abbe et al, 2015; Dehghan, Keane, & Nenadic, 2013; Ford et al, 2016)
The approaches include (a) a knowledge‐driven methodology based on lexicalized rules combined with manually constructed dictionaries characterizing positive valence symptoms; (b) a neural network (NN) built on lexical and semantic features extracted from the text; and c) a hybrid approach that combined the best predictions between the rule‐based and NN methods
Summary
We designed and implemented 3 automatic methods: a knowledge‐driven approach relying on local lexicalized rules based on common syntactic patterns in text suggesting positive valence symptoms; a machine learning method using a neural network; and a hybrid approach combining the first 2 methods with a neural network
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