AI Medical Compendium Topic:
Data Mining

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Identification of the Best Anthropometric Predictors of Serum High- and Low-Density Lipoproteins Using Machine Learning.

IEEE journal of biomedical and health informatics
Serum high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol levels are associated with risk factors for various diseases and are related to anthropometric measures. However, controversy remains regarding the best anthropometric...

How to learn about gene function: text-mining or ontologies?

Methods (San Diego, Calif.)
As the amount of genome information increases rapidly, there is a correspondingly greater need for methods that provide accurate and automated annotation of gene function. For example, many high-throughput technologies--e.g., next-generation sequenci...

Identification and prioritization of disease candidate genes using biomedical named entity recognition and random forest classification.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: The elucidation of candidate genes is fundamental to comprehending intricate diseases, vital for early diagnosis, personalized treatment, and drug discovery. Traditional Disease Gene Identification methods encounter limitati...

Evaluating the effectiveness of biomedical fine-tuning for large language models on clinical tasks.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Large language models (LLMs) have shown potential in biomedical applications, leading to efforts to fine-tune them on domain-specific data. However, the effectiveness of this approach remains unclear. This study aims to critically evaluat...

DiMB-RE: mining the scientific literature for diet-microbiome associations.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To develop a corpus annotated for diet-microbiome associations from the biomedical literature and train natural language processing (NLP) models to identify these associations, thereby improving the understanding of their role in health a...

Optimized Drug-Drug Interaction Extraction With BioGPT and Focal Loss-Based Attention.

IEEE journal of biomedical and health informatics
Drug-drug interactions (DDIs) are a significant focus in biomedical research and clinical practice due to their potential to compromise treatment outcomes or cause adverse effects. While deep learning approaches have advanced DDI extraction, challeng...

Semi-Supervised PARAFAC2 Decomposition for Computational Phenotyping Using Electronic Health Records.

IEEE journal of biomedical and health informatics
Computational phenotyping uses data mining methods to extract clusters of clinical descriptors, known as phenotypes, from electronic health records (EHR). Tensor factorization methods are very effective in extracting meaningful patterns and have beco...

SNER: Semi-Supervised Named Entity Recognition for Large Volume of Diabetes Data.

IEEE journal of biomedical and health informatics
The medical literature and records on diabetes provide crucial resources for diabetes prevention and treatment. However, extracting entities from these textual diabetes data is crucial but challenging. Named entity recognition (NER) - an important co...

Enhancing biomedical relation extraction through data-centric and preprocessing-robust ensemble learning approach.

Database : the journal of biological databases and curation
The paper describes our biomedical relation extraction system, which is designed to participate in the BioCreative VIII challenge Track 1: BioRED Track, which emphasizes the relation extraction from biomedical literature. Our system employs an ensemb...