AI Medical Compendium Topic

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Data Mining

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Vocabulary Matters: An Annotation Pipeline and Four Deep Learning Algorithms for Enzyme Named Entity Recognition.

Journal of proteome research
Enzymes are indispensable in many biological processes, and with biomedical literature growing exponentially, effective literature review becomes increasingly challenging. Natural language processing methods offer solutions to streamline this process...

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus.

PloS one
Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worrying...

Identifying Main Themes in Diabetes Management Interviews Using Natural Language Processing-Based Text Mining.

Computers, informatics, nursing : CIN
This study aimed to identify the main themes from exit interviews of adult patients with type 2 diabetes after completion of a diabetes education program. Eighteen participants with type 2 diabetes completed an exit interview regarding their program ...

Data mining and machine learning in HIV infection risk research: An overview and recommendations.

Artificial intelligence in medicine
In the contemporary era, the applications of data mining and machine learning have permeated extensively into medical research, significantly contributing to areas such as HIV studies. By reviewing 38 articles published in the past 15 years, the stud...

TGIN: Document-level event extraction with two-phase graph inference network.

Neural networks : the official journal of the International Neural Network Society
Document-level event extraction aims to extract event records from a whole document that contain numerous entities scattered across multiple sentences. Efficiently modeling the interactions among these entities is crucial. However, previous methods s...

AlpaPICO: Extraction of PICO frames from clinical trial documents using LLMs.

Methods (San Diego, Calif.)
In recent years, there has been a surge in the publication of clinical trial reports, making it challenging to conduct systematic reviews. Automatically extracting Population, Intervention, Comparator, and Outcome (PICO) from clinical trial studies c...

Language model based on deep learning network for biomedical named entity recognition.

Methods (San Diego, Calif.)
Biomedical Named Entity Recognition (BioNER) is one of the most basic tasks in biomedical text mining, which aims to automatically identify and classify biomedical entities in text. Recently, deep learning-based methods have been applied to Biomedica...

SubGE-DDI: A new prediction model for drug-drug interaction established through biomedical texts and drug-pairs knowledge subgraph enhancement.

PLoS computational biology
Biomedical texts provide important data for investigating drug-drug interactions (DDIs) in the field of pharmacovigilance. Although researchers have attempted to investigate DDIs from biomedical texts and predict unknown DDIs, the lack of accurate ma...

Natural language processing (NLP) to facilitate abstract review in medical research: the application of BioBERT to exploring the 20-year use of NLP in medical research.

Systematic reviews
BACKGROUND: Abstract review is a time and labor-consuming step in the systematic and scoping literature review in medicine. Text mining methods, typically natural language processing (NLP), may efficiently replace manual abstract screening. This stud...

Text mining of hypertension researches in the west Asia region: a 12-year trend analysis.

Renal failure
More than half of the world population lives in Asia and hypertension (HTN) is the most prevalent risk factor found in Asia. There are numerous articles published about HTN in Eastern Mediterranean Region (EMRO) and artificial intelligence (AI) metho...