NegEx, a rule-based algorithm that detects negations in English clinical text, was translated into Swedish and evaluated on clinical text written in Swedish. The NegEx algorithm detects negations through the use of trigger phrases, which indicate that a preceding or following concept is negated. A list of English trigger phrases was translated into Swedish, taking grammatical differences betwee...

A conditional random fields model was trained to detect medical complaints in Japanese health record text. Tokenisation was applied by using the dependency parser CaboCha and the conditional random fields model was trained on tokens in a window size of two preceding and three following tokens, as well as on part-of-speech, vocabulary mapping, header name, frequent suffix, orthography and presen...

Despite efforts to develop models for extracting medical concepts from clinical notes, there are still some challenges in particular be able relate dates. The high number of notes written each single patient, the use negation, speculation, and different date formats cause ambiguity that has solved reconstruct patient’s natural history. In this paper, we concentrate on narratives cancer diagnosi...

We argue that under the stable model semantics default negation can be read as explicit with update. show dynamic logic programming which is based on negation, even in heads, interpreted a variant of updates only. As corollaries, we get an easy description generalized and normal where initially negated literals are updated. These results discussed respect to understanding logic.

Conditional random fields were trained to detect marker words for negation and speculation in two corpora belonging to two very different domains: clinical text and consumer review text. For the corpus of clinical text, marker words for speculation and negation were detected with results in line with previously reported interannotator agreement scores. This was also the case for speculation mar...

This paper describes the system created by the University of Texas at Dallas for contentbased medical record retrieval submitted to the TREC 2011 Medical Records Track. Our system builds a query by extracting keywords from a given topic using a Wikipedia-based approach we use regular expressions to extract age, gender, and negation requirements. Each query is then expanded by relying on UMLS, S...

This paper reports on a simple system for resolving the scope of negation in the closed track of the *SEM 2012 Shared Task. Cue detection is performed using regular expression rules extracted from the training data. Both scope tokens and negated event tokens are resolved using a Conditional Random Field (CRF) sequence tagger – namely the SimpleTagger library in the MALLET machine learning toolk...

Speculation and negation are important information to identify text factuality. In this paper, we propose a Convolutional Neural Network (CNN)-based model with probabilistic weighted average pooling to address speculation and negation scope detection. In particular, our CNN-based model extracts those meaningful features from various syntactic paths between the cues and the candidate tokens in b...

(Background) We developed in previous work a biomedical data warehouse for mining clinical reports by full text search engine. But the lack of consideration of negation and family history generate noise. (Methods) We developed an algorithm to detect the French notion of negation in medical reports and he context of family history. The semantic enrichment reflects this new semantic information. ...