Abstract:Mental Health Records (MHRs) contain freetext documentation about patients' suicide and suicidality. In this paper, we address the problem of determining whether grammatic variants (inflections) of the word "suicide" are affirmed or negated. To achieve this, we populate and annotate a dataset with over 6,000 sentences originating from a large repository of MHRs. The resulting dataset has high InterAnnotator Agreement (κ 0.93). Furthermore, we develop and propose a negation detection method that leverages synta… Show more
“…Resnik et al (2015a) proved that such approaches can be successfully used in identifying users with depression, who have self-disclosed their mental illnesses on Twitter. In general, a clear distinction in the lexical and syntactic structure of the language used by individuals with different mental disorders, as well as between individuals within a control group, can be identified throughout the literature mentioned above, as well as from the explorative analysis conducted by Gkotsis et al (2016). Due to the reliability of the lexical and behavioral features used in many of the models mentioned above, our proposed solution also focused on these feature categories.…”
“…Resnik et al (2015a) proved that such approaches can be successfully used in identifying users with depression, who have self-disclosed their mental illnesses on Twitter. In general, a clear distinction in the lexical and syntactic structure of the language used by individuals with different mental disorders, as well as between individuals within a control group, can be identified throughout the literature mentioned above, as well as from the explorative analysis conducted by Gkotsis et al (2016). Due to the reliability of the lexical and behavioral features used in many of the models mentioned above, our proposed solution also focused on these feature categories.…”
“…However, as Action Polarity is defined in terms of the interaction between an individual and a specific environment, it adds a layer of complexity to noninteractive physiological observations. Gkotsis et al (2016) investigate using parsing-based scoping limitations for negation detection in complex clinical statements, though their focus is specifically on mentions of suicide.…”
Assessing how individuals perform different activities is key information for modeling health states of individuals and populations. Descriptions of activity performance in clinical free text are complex, including syntactic negation and similarities to textual entailment tasks. We explore a variety of methods for the novel task of classifying four types of assertions about activity performance: Able, Unable, Unclear, and None (no information). We find that ensembling an SVM trained with lexical features and a CNN achieves 77.9% macro F1 score on our task, and yields nearly 80% recall on the rare Unclear and Unable samples. Finally, we highlight several challenges in classifying performance assertions, including capturing information about sources of assistance, incorporating syntactic structure and negation scope, and handling new modalities at test time. Our findings establish a strong baseline for this novel task, and identify intriguing areas for further research.
“…Negation detection determines whether a clinical finding mentioned in a narrative is 12 present or absent, usually using the sentence mentioning the concept as input [1]. Many 13 methodologies have been applied to the task of negation detection, traditionally using rule-based 14 methods, with a more recent focus on machine learning algorithms. The performance of rule-based 15 methods for negation in comparison to machine learning methods is hotly contested.…”
Section: Introductionmentioning
confidence: 99%
“…46 One such approach, DEEPEN [11], operates upon concepts that NegEx determines to be 47 negated [11]. Other dependency-based algorithms make no use of NegEx, such as NegBio, 48 negation-detection, and DepNeg [12][13][14], reported to exhibit an improved precision over syntactic 49 approaches. However, an independent assessment showed that ConText maintained its performance 50 over a novel dataset, while the other approaches did not [2].…”
AbstractBackgroundNegation detection is an important task in biomedical text mining. Particularly in clinical settings, it is of critical importance to determine whether findings mentioned in text are present or absent. Rule-based negation detection algorithms are a common approach to the task, and more recent investigations have resulted in the development of rule-based systems utilising the rich grammatical information afforded by typed dependency graphs. However, interacting with these complex representations inevitably necessitates complex rules, which are time-consuming to develop and do not generalise well. We hypothesise that a heuristic approach to determining negation via dependency graphs could offer a powerful alternative.ResultsWe describe and implement an algorithm for negation detection based on grammatical distance from a negatory construct in a typed dependency graph. To evaluate the algorithm, we develop two testing corpora comprised of sentences of clinical text extracted from the MIMIC-III database and documents related to hypertrophic cardiomyopathy patients routinely collected at University Hospitals Birmingham NHS trust. Gold-standard validation datasets were built by a combination of human annotation and examination of algorithm error. Finally, we compare the performance of our approach with four other rule-based algorithms on both gold-standard corpora.ConclusionsThe presented algorithm exhibits the best performance by f-measure over the MIMIC-III dataset, and a similar performance to the syntactic negation detection systems over the HCM dataset. It is also the fastest of the dependency-based negation systems. Our results show that dependency-based algorithms, utilising a single heuristic, can be powerful and stable methods for negation detection in clinical text, requiring minimal training and adaptation between datasets. While NegEx retains an extremely high performance in some cases, the presented approach may be more robust to more complex text descriptions. As such, it could present a drop-in replacement or augmentation for syntactic negation components in clinical text-mining pipelines, particularly for cases where adaptation and rule development is not required or possible.
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