Negation Detection for Clinical Text Mining in Russian
Developing predictive modeling in medicine requires additional features from unstructured clinical texts. In Russia, there are no instruments for natural language processing to cope with problems of medical records. This paper is devoted to a module of negation detection. The corpus-free machine learning method is based on gradient boosting classifier is used to detect whether a disease is denied, not mentioned or presented in the text. The detector classifies negations for five diseases and shows average F-score from 0.81 to 0.93. The benefits of negation detection have been demonstrated by predicting the presence of surgery for patients with the acute coronary syndrome.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningNegationNegation DetectionSimilar Papers 제목 키워드 기반
Automated Spelling Correction for Clinical Text Mining in Russian
The main goal of this paper is to develop a spell checker module for clinical text in Russian. The described approach combines string distance measure algorithms with technics of machine learning embedding methods. Our o…
BIG-bench Machine LearningNegationSpelling CorrectionNegation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish.
Negation is a linguistic universal that poses difficulties for cognitive and computational processing. Despite many advances in text analytics, negation resolution remains an acute and continuously researched question in…
Machine TranslationNegationNegation DetectionNegation Scope Resolution+2Negation Detection in Clinical Reports Written in German
An important subtask in clinical text mining tries to identify whether a clinical finding is expressed as present, absent or unsure in a text. This work presents a system for detecting mentions of clinical findings that …
Named Entity Recognition (NER)NegationNegation DetectionRelation ExtractionAnnotation of negation in the IULA Spanish Clinical Record Corpus
This paper presents the IULA Spanish Clinical Record Corpus, a corpus of 3,194 sentences extracted from anonymized clinical records and manually annotated with negation markers and their scope. The corpus was conceived a…
Medical DiagnosisNegationNegation DetectionTerm ExtractionUnsupervised Domain Adaptation for Clinical Negation Detection
Detecting negated concepts in clinical texts is an important part of NLP information extraction systems. However, generalizability of negation systems is lacking, as cross-domain experiments suffer dramatic performance l…
Domain AdaptationNegationNegation DetectionUnsupervised Domain Adaptation