The Role of a Deep-Learning Method for Negation Detection in Patient Cohort Identification from Electroencephalography Reports.

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Abstract

Detecting negation in biomedical texts entails the automatic identification of negation cues (e.g. “never”, “not”, “no longer”) as well as the scope of these cues. When medical concepts or terms are identified within the scope of a negation cue, their polarity is inferred as “negative”. All the other concepts or words receive a positive polarity. Correctly inferring the polarity is essential for patient cohort retrieval systems, as all inclusion criteria need to be automatically assigned positive polarity, whereas exclusion criteria should receive negative polarity. Motivated by the recent development of techniques using deep learning, we have experimented with a neural negation detection technique and compared it against an existing neural polarity recognition system, which were incorporated in a patient cohort system operating on clinical electroencephalography (EEG) reports. Our experiments indicate that the neural negation detection method produces better patient cohorts then the polarity recognition method.