In the medical domain, identifying and expanding abbreviations in clinical texts is a vital task for both better human and machine understanding. It is a challenging task because many abbreviations are ambiguous especially for intensive care medicine texts, in which phrase abbreviations are frequently used. Besides the fact that there is no universal dictionary of clinical abbreviations and no universal rules for abbreviation writing, such texts are difficult to acquire, expensive to annotate and even sometimes, confusing to domain experts. This paper proposes a novel and effective approach ‐ exploiting taskoriented resources to learn word embeddings for expanding abbreviations in clinical notes. We achieved 82.27 accuracy, close to expert human performance.
The task of abbreviation disambiguation in biomedical documents has been studied by various researchers using supervised machine learning algorithms @cite_2 @cite_1 @cite_6 @cite_7 @cite_8 . However, the performance of these supervised methods mainly depends on a large amount of labeled data which is extremely difficult to obtain for our task since intensive care medicine texts are very rare resources in clinical domain due to the high cost of de-identification and annotation. proposed a distributional semantics-based approach for abbreviation expansion in Swedish but they focused only on expanding single words and cannot handle multi-word phrases. In contrast, we use word embeddings combined with task-oriented resources and knowledge, which can handle multiword expressions.
@cite_7 @cite_8 @cite_1 @cite_6 @cite_2
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Summary Background Mortality rate, the possible factors affecting mortality and intubation in patients with acute exacerbation of chronic obstructive pulmonary diseases (COPD) and hypercapnic respiratory failure (RF) are yet unclear. Objective To identify the possible factors affecting mortality and intubation in COPD patients. Design A prospective study using data obtained over the first 24h of respiratory intensive care unit (RICU) admission. Consecutive admissions of 656 patients were monitored and 151 of them who had acute exacerbation of COPD and hypercapnic RF were enrolled. Setting University hospital, Department of Chest Diseases, RICU. Results Mean age was 65.1 years. The mean APACHE II score was 23.7. Eighty-seven patients (57.6 ) received mechanical ventilation (MV) via an endotracheal tube for more than 24h. Twenty-two patients received non-invasive ventilation (NIV). Fifty patients died (33.1 ) in hospital during the study period. The mortality rate was 52.9 in patients in need of MV. In the multivariate analysis, the need for intubation, inadequate metabolic compensation for respiratory acidosis, and low (=bad) Glasgow Coma Score (GCS) were determined as independent factors associated with mortality. The low GCS (OR: 0.61; CI: 0.48–0.78) and high APACHE II score (OR: 1.24; CI: 1.11–1.38) were determined as factors associated with intubation. Conclusion The most important predictors related to hospital mortality were the need for invasive ventilation and complications to MV. Adequate metabolic compensation for respiratory acidosis at admittance is associated with better survival. A high APACHE II score and loss of consciousness (low GCS) were independent predictors of a need to intubate patients. Abbreviations are common in biomedical documents and many are ambiguous in the sense that they have several potential expansions. Identifying the correct expansion is necessary for language understanding and important for applications such as document retrieval. Identifying the correct expansion can be viewed as a Word Sense Disambiguation (WSD) problem. A WSD system that uses a variety of knowledge sources, including two types of information specific to the biomedical domain, is also described. This system was tested on a corpus of ambiguous abbreviations, created by automatically identifying the correct expansion in Medline abstracts, and found to identify the correct expansion with up to 99 accuracy. Motivation: Biological literature contains many abbreviations with one particular sense in each document. However, most abbreviations do not have a unique sense across the literature. Furthermore, many documents do not contain the long forms of the abbreviations. Resolving an abbreviation in a document consists of retrieving its sense in use. Abbreviation resolution improves accuracy of document retrieval engines and of information extraction systems. Results: We combine an automatic analysis of Medline abstracts and linguistic methods to build a dictionary of abbreviation sense pairs. The dictionary is used for the resolution of abbreviations occurring with their long forms. Ambiguous global abbreviations are resolved using support vector machines that have been trained on the context of each instance of the abbreviation sense pairs, previously extracted for the dictionary set-up. The system disambiguates abbreviations with a precision of 98.9 for a recall of 98.2 (98.5 accuracy). This performance is superior in comparison with previously reported research work. Availability: The abbreviation resolution module is available at http: www.ebi.ac.uk Rebholz software.html Contact: [email protected] Abbreviations and acronyms are widely used in the biomedical literature and many of them represent important biomedical concepts. Because many abbreviations are ambiguous (e.g., CAT denotes both chloramphenicol acetyl transferase and computed axial tomography, depending on the context), recognizing the full form associated with each abbreviation is in most cases equivalent to identifying the meaning of the abbreviation. This, in turn, allows us to perform more accurate natural language processing, information extraction, and retrieval. In this study, we have developed supervised approaches to identifying the full forms of ambiguous abbreviations within the context they appear. We first automatically assigned multiple possible full forms for each abbreviation; we then treated the in-context full-form prediction for each specific abbreviation occurrence as a case of word-sense disambiguation. We generated automatically a dictionary of all possible full forms for each abbreviation. We applied supervised machine-learning algorithms for disambiguation. Because some of the links between abbreviations and their corresponding full forms are explicitly given in the text and can be recovered automatically, we can use these explicit links to automatically provide training data for disambiguating the abbreviations that are not linked to a full form within a text. We evaluated our methods on over 150 thousand abstracts and obtain for coverage and precision results of 82p and 92p, respectively, when performed as tenfold cross-validation, and 79p and 80p, respectively, when evaluated against an external set of abstracts in which the abbreviations are not defined. Abstract Objective The aim of this study was to investigate relations among different aspects in supervised word sense disambiguation (WSD; supervised machine learning for disambiguating the sense of a term in a context) and compare supervised WSD in the biomedical domain with that in the general English domain. Methods The study involves three data sets (a biomedical abbreviation data set, a general biomedical term data set, and a general English data set). The authors implemented three machine-learning algorithms, including (1) naive Bayes (NBL) and decision lists (TDLL), (2) their adaptation of decision lists (ODLL), and (3) their mixed supervised learning (MSL). There were six feature representations (various combinations of collocations, bag of words, oriented bag of words, etc.) and five window sizes (2, 4, 6, 8, and 10). Results Supervised WSD is suitable only when there are enough sense-tagged instances with at least a few dozens of instances for each sense. Collocations combined with neighboring words are appropriate selections for the context. For terms with unrelated biomedical senses, a large window size such as the whole paragraph should be used, while for general English words a moderate window size between 4 and 10 should be used. The performance of the authors' implementation of decision list classifiers for abbreviations was better than that of traditional decision list classifiers. However, the opposite held for the other two sets. Also, the authors' mixed supervised learning was stable and generally better than others for all sets. Conclusion From this study, it was found that different aspects of supervised WSD depend on each other. The experiment method presented in the study can be used to select the best supervised WSD classifier for each ambiguous term.