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| Rudarenje kliničkog teksta× | Ekstrakcija informacija× | |
|---|---|---|
| Područje | Rudarenje teksta | Rudarenje teksta |
| Obitelj | Process / pipeline | Process / pipeline |
| Godina nastanka≠ | 2000s–2020s (established domain; BioBERT milestone 2020) | — |
| Tvorac≠ | Community-driven; foundational work by i2b2/UTHealth NLP challenges and BioBERT (Lee et al., 2020) | — |
| Vrsta≠ | NLP information-extraction pipeline | NLP structured-information task |
| Temeljni izvor≠ | 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. DOI ↗ | Cowie, J. & Lehnert, W. (1996). Information Extraction. Communications of the ACM. DOI ↗ |
| Drugi nazivi | clinical NLP, clinical information extraction, Klinik Metin Madenciliği | IE, structured information extraction, Bilgi Çıkarma (Information Extraction) |
| Srodne≠ | 5 | 4 |
| Sažetak≠ | 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. | Information extraction (IE) is a natural-language-processing task that converts unstructured text into structured information — such as events, relations, and attributes — so that facts buried in free-form documents become machine-readable records. The task was consolidated in early surveys by Cowie and Lehnert (1996) and later by Grishman (2012). |
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