Accuracy of stroke classification and retrieval classification as a function of number labelled. by Neva J. Bull (15447246)

Example of free-text and MetaMap-extracted veterinary record. by Guhan Ram Venkataraman (9009668)

Subjects:

Python code for the FRF and GFB models described in the text. by Christopher DiMattina (6475880)

Example death certificate free text. by Patrick J. Ward (7507163)

Subjects:

Classification performance across categories and methods. by Guhan Ram Venkataraman (9009668)

Subjects:

Classification results for both settings with the two best performing classifiers shown in bold text. by Jonathan Bryan (15285217)

Text classification to streamline online wildlife trade analyses by Oliver Stringham (5595401)

“…Code and data for text classification models association with "Text classification to streamline online wildlife trade analyses". by Stringham OC, Moncayo S, Hill KGW, Toomes A, Mitchell L, Ross JV, Cassey P. …”

Reported health outcomes in the included studies by ICD-11 main classification code. by Katherine D. Rogers (18388158)

Top-level coding mapping between ICD-9, ICD-10, and SNOMED-CT. by Guhan Ram Venkataraman (9009668)

Subjects:

Diagram of the training and evaluation design. by Guhan Ram Venkataraman (9009668)

Subjects:

F₁ scores using various training and validation dataset combinations for the “neoplasia” category. by Guhan Ram Venkataraman (9009668)

Subjects:

Database statistics of patients, records, and species (records with diagnosis). by Guhan Ram Venkataraman (9009668)

Subjects:

NLP (MetaMap) evaluation. by Guhan Ram Venkataraman (9009668)

Subjects:

Confusion matrix for MetaMap Lite evaluation on veterinary data. by Guhan Ram Venkataraman (9009668)

Subjects:

Average F₁ scores using various training and validation dataset combinations for all categories. by Guhan Ram Venkataraman (9009668)

Subjects:

F1 scores by category using various training configurations. by Guhan Ram Venkataraman (9009668)

Subjects:

Characteristics of patients based on case classification N = 100 (baseline validation cohort). by Ann-Marcia C. Tukpah (15270829)

Subjects:

Attention to women's rights in NGO press releases, 1996–2018: A curated, coded dataset of organizational attention to women and violence against women by Margarita Solntseva (22563608)

Subjects:

Proposed architecture for total CTRS score classification following a multi-task approach modeling each CTRS code. by Nikolaos Flemotomos (11597197)

Table 2_Validating the use of large language models for psychological text classification.docx by Hannah L. Bunt (20762777)

“…They also demonstrate the potential of using LLMs in rapid and cost-effective iterations over big qualitative datasets, enabling psychologists to explore and iteratively refine their concepts and operationalizations during manual coding and classifier development. Accordingly, as a secondary contribution, we demonstrate that LLMs enable an intellectual partnership with the researcher, defined by a synergistic and recursive text classification process where the LLM's generative nature facilitates validity checks. …”