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Found 4 papers, 3 papers with code
Benchmarking and Analyzing In-context Learning, Fine-tuning and Supervised Learning for Biomedical Knowledge Curation: a focused study on chemical entities of biological interest
Five setups were designed to assess ML and FT model performance across different data availability scenarios. Datasets for curation tasks included: task 1 (620, 386), task 2 (611, 430), and task 3 (617, 381), maintaining a 50:50 positive versus negative ratio.
Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notes
Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes.
Quantifying Health Inequalities Induced by Data and AI Models
Extensive analyses were carried out to quantify health inequalities (a) embedded in two real-world ICU datasets; (b) induced by AI models trained for two resource allocation scenarios.
Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision
The identification of rare diseases from clinical notes with Natural Language Processing (NLP) is challenging due to the few cases available for machine learning and the need of data annotation from clinical experts.
