DUP estimation requires knowledge about when psychosis symptoms first started (symptom onset), and when psychosis treatment was initiated. Hence, our label generation method not only minimized the annotation task but is also sufficiently reliable for building temporal type classifiers.ĭuration of untreated psychosis (DUP) is an important clinical construct in the field of mental health, as longer DUP can be associated with worse intervention outcomes. Experimental results show that the produced examples improve classification models by up to 14.0% accuracy points. We then developed a classification model on these training examples and compared our automatically created examples with existing manually annotated data. Through a human evaluation, we verified that 98.7% of the sampled labels match the hand-crafted labels. We prepared several simple rules to determine temporal type labels from sentence pairs, and automatically created a training set for this task. Ambiguity in Japanese time expressions is comparatively easily resolved using their associated English words. Inspired by an annotation projection technique, we associate Japanese time expressions with their corresponding English words. ” To build a supervised classifier for this ambiguity while minimizing the annotation burden, we introduce an automatic label generation method using a bilingual corpus. One of the most representative cases is date–duration ambiguity arising from the commonly used time expression, “** 日. In Japanese, time expressions are often unaccompanied by explicit temporal markers, and thus their temporal types are not always obvious. While a few general-purpose measures of data quality that do not require external data most of these focus on the measurement of noise. There are similarities in the data quality dimensions used to characterize structured data and UTD. Multiple NLP techniques have been proposed to preprocess UTD, with some differences in techniques applied to EMR data. Data quality topics for articles about EMR data included misspelled words, security (i.e., de-identification), word variability, sources of noise, quality of annotations, and ambiguity of abbreviations. Common preprocessing methods included removal of extraneous text elements such as stop words, punctuation, and numbers, word tokenization, and parts of speech tagging. Almost 20% of the articles were published in health science journals. Study data were presented using a narrative synthesis.Ī total of 41 articles were included in the scoping review over 50% were published between 20. Information extracted from the studies included article characteristics (i.e., year of publication, journal discipline), data characteristics, types of preprocessing methods, and data quality topics. Scopus, Web of Science, ProQuest, and EBSCOhost were searched for literature relevant to the study objective. Our objective was to systematically document current research and practices about NLP preprocessing methods to describe or improve the quality of UTD, including UTD found in EMR databases.Ī scoping review was undertaken of peer-reviewed studies published between December 2002 and January 2021. Different NLP methods are used to preprocess UTD and may affect data quality. UTD are typically prepared for analysis (i.e., preprocessed) and analyzed using natural language processing (NLP) techniques. Data quality can impact the usefulness of UTD for research. Unstructured text data (UTD) are increasingly found in many databases that were never intended to be used for research, including electronic medical record (EMR) databases. Almost all empirical articles (85.4%) described preprocessing methods to improve NLP algorithm performance. A variety of types of text data were represented in the selected articles including EMRs (i.e., clinical notes, progress notes, patient safety records ), lexical documents (i.e., language treebanks which are bodies of text that have been parsed semantically and syntactically, WordNet database ), organizational documents (i.e., maintenance logs/data, accident reports, requirements documentation ), abstracts and scientific articles (i.e., PubMed and various engineering journals ), various bodies of text (corpora) (i.e., non-language corpora, non-medical/medical/biomedical corpora, language corpus ), social media data (i.e., Twitter, meme tracker from various social media websites ), product reviews (i.e., general product, Chinese tourism, Amazon product ), and news articles (i.e., magazines, newswires, consumer reports ).
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