השוואת שיטות

	זיהוי שלילה	כריית טקסט קליני
תחום	כריית טקסט	כריית טקסט
משפחה	Process / pipeline	Process / pipeline
שנת המקור	2001 (NegEx); scope learning formalised by 2009	2000s–2020s (established domain; BioBERT milestone 2020)
הוגה השיטה	Chapman et al. (NegEx algorithm, 2001); Morante & Daelemans (scope learning, 2009)	Community-driven; foundational work by i2b2/UTHealth NLP challenges and BioBERT (Lee et al., 2020)
סוג	NLP information-extraction task	NLP information-extraction pipeline
מקור מכונן	Chapman, W.W., Bridewell, W., Hanbury, P., Cooper, G.F., & Buchanan, B.G. (2001). A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries. Journal of the American Medical Informatics Association, 8(6), 606-614.	Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240.
כינויים	negation scope identification, negation cue detection, Olumsuzlama Tespiti (Negation Detection)	clinical NLP, clinical information extraction, Klinik Metin Madenciliği
קשורות	6	5
תקציר	Negation detection is a natural-language-processing task that locates negation cues in text — words or phrases such as 'no', 'not', 'without', or 'denies' — and determines the span of text (the scope) whose meaning those cues invert. Formalised for clinical text by Chapman et al. (2001) with the NegEx algorithm and extended to scope learning in biomedical literature by Morante and Daelemans (2009), the method is essential wherever the difference between a finding being present and its being explicitly ruled out carries real consequences.	Clinical text mining is a specialised branch of natural language processing that extracts structured clinical facts — diagnoses, symptoms, medications, treatments, and ICD codes — from unstructured healthcare documents such as discharge summaries, progress notes, and radiology reports. Grounded in biomedical NLP models like BioBERT (Lee et al., 2020) and the i2b2/UTHealth shared-task benchmarks (Stubbs & Uzuner, 2015), it converts free-text clinical narratives into machine-readable data suitable for clinical decision support and health analytics.