Detecting the scope of negations in clinical notes
Authors: Giuseppe Attardi, Vittoria Cozza, Daniele Sartiano (Università di Pisa, 2015)
Topic: In-domain IR and IE

Abstract
We address the problem of automatically detecting the scope of negations and speculations in clinical notes, by proposing a machine-learning algorithm that analyzes the dependency tree of a sentence. Given a negative/speculative cue, the algorithm tries to extend the boundary of the scope towards the left and the right, by navigating through the parse tree. We report on experiments with the algorithm using the Bioscope corpus.

Introduction
Clinical notes are a vast potential source of information for healthcare systems, from whose analysis valuable data can be extracted for clinical data mining tasks, for example confirming or rejecting a diagnosis, predicting drug risks or estimating the effectiveness of treatments. Clinical notes are written in informal natural language, where, besides annotating evidence collected during a patient visit, physician report historical facts about the patient and suggested or discarded hypothesis. Annotations about dismissed hypotheses or evidence about the absence of a phenomenon are particularly abundant in these notes and should be recognized as such in order to avoid misleading conclusions. A standard keyword based search engine might for example return many irrelevant documents where a certain symptom is mentioned but it does not affect the patient. Medical records are currently analysed by clinical experts, who read and annotate them manually. In some countries like Spain, it has become mandatory by law for all medical records to be annotated with the mentions of any relevant reported fact, associated with their official ICD9 code. To assign the right ICD9 code, it is of critical importance to recognize the kind of context of each mention: assertive, negative or speculative. In the BioScope corpus, a collection of bio-medical text, one out of eight sentences indeed contains negations (Vincze et al. (2008)). In order to automate the process of annotation of clinical notes, the following steps can be envisaged: 1. recognition of medical entities, by exploiting techniques of named entity (NE); 2. normalization and association to a unique official concept identifier to their key terminology from UMLS metathesaurus (O. Bodenreider, 2004); 3. detection of negative or speculative scope. NE recognition and normalization steps can be performed by relying on shallow analysis of texts (for an exhaustive and updated overview of the state of the art, see Pradhan et al. (2014)). The identification of negative or speculative scope, instead, cannot just rely on such simple text analysis techniques, and would require identifying relations between parts, by means of a deeper syntactic-semantic analysis of sentences. This work presents a novel algorithm that learns to determine the boundaries of negative and speculative scopes, by navigating the parse tree of a sentence and by exploiting machine learning techniques that rely on features extracted from the analysis of the parse tree.

Conclusion
We have described a two-step approach to speculation and negation detection. The scope detection step exploits the structure of sentences as represented by its dependency parse tree. The novelty with respect to previous approaches also exploiting dependency parses is that the tree is used as a guide in the choice of how to extend the current scope. This avoids producing spurious scopes, for example discontiguous ones. The algorithm also may gather partial subtrees of the parse. This provides more resilience and flexibility. The accuracy of the algorithm of course depends on the accuracy of the dependency parser, both in the production of the training corpus and in the analysis. We used a fast transition-based dependency parser trained on the Genia corpus, which turned out to be adequate for the task. Indeed in experiments on the BioScope corpus the algorithm achieved accuracy scores above the state of the art.