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Unified Medical Language System

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NSSC: a neuro-symbolic AI system for enhancing accuracy of named entity recognition and linking from oncologic clinical notes.

Medical & biological engineering & computing
Accurate recognition and linking of oncologic entities in clinical notes is essential for extracting insights across cancer research, patient care, clinical decision-making, and treatment optimization. We present the Neuro-Symbolic System for Cancer ...

Mapping vaccine names in clinical trials to vaccine ontology using cascaded fine-tuned domain-specific language models.

Journal of biomedical semantics
BACKGROUND: Vaccines have revolutionized public health by providing protection against infectious diseases. They stimulate the immune system and generate memory cells to defend against targeted diseases. Clinical trials evaluate vaccine performance, ...

Explanatory argumentation in natural language for correct and incorrect medical diagnoses.

Journal of biomedical semantics
BACKGROUND: A huge amount of research is carried out nowadays in Artificial Intelligence to propose automated ways to analyse medical data with the aim to support doctors in delivering medical diagnoses. However, a main issue of these approaches is t...

Automatic Mapping of Terminology Items with Transformers.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Biomedical ontologies are a key component in many systems for the analysis of textual clinical data. They are employed to organize information about a certain domain relying on a hierarchy of different classes. Each class maps a concept to items in a...

Retrieval augmentation of large language models for lay language generation.

Journal of biomedical informatics
The complex linguistic structures and specialized terminology of expert-authored content limit the accessibility of biomedical literature to the general public. Automated methods have the potential to render this literature more interpretable to read...

Extracting cancer concepts from clinical notes using natural language processing: a systematic review.

BMC bioinformatics
BACKGROUND: Extracting information from free texts using natural language processing (NLP) can save time and reduce the hassle of manually extracting large quantities of data from incredibly complex clinical notes of cancer patients. This study aimed...

Selective UMLS knowledge infusion for biomedical question answering.

Scientific reports
One of the artificial intelligence applications in the biomedical field is knowledge-intensive question-answering. As domain expertise is particularly crucial in this field, we propose a method for efficiently infusing biomedical knowledge into pretr...

Localizing in-domain adaptation of transformer-based biomedical language models.

Journal of biomedical informatics
In the era of digital healthcare, the huge volumes of textual information generated every day in hospitals constitute an essential but underused asset that could be exploited with task-specific, fine-tuned biomedical language representation models, i...

Multimodal learning on graphs for disease relation extraction.

Journal of biomedical informatics
Disease knowledge graphs have emerged as a powerful tool for artificial intelligence to connect, organize, and access diverse information about diseases. Relations between disease concepts are often distributed across multiple datasets, including uns...

Ontology-driven and weakly supervised rare disease identification from clinical notes.

BMC medical informatics and decision making
BACKGROUND: Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to few cases available for machine learning and the need for ...