Salīdzināt metodes

	Klīniskā teksta ieguve	Tekstu klasifikācija
Nozare	Teksta ieguve	Teksta ieguve
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2000s–2020s (established domain; BioBERT milestone 2020)	—
Autors	Community-driven; foundational work by i2b2/UTHealth NLP challenges and BioBERT (Lee et al., 2020)	—
Tips	NLP information-extraction pipeline	Supervised NLP classification task
Pirmavots	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.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Citi nosaukumi	clinical NLP, clinical information extraction, Klinik Metin Madenciliği	text categorization, document classification, topic classification, metin sınıflandırma
Saistītās	5	4
Kopsavilkums	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.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.