AIMC Topic: Data Mining

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Pre-trained models, data augmentation, and ensemble learning for biomedical information extraction and document classification.

Database : the journal of biological databases and curation
Large volumes of publications are being produced in biomedical sciences nowadays with ever-increasing speed. To deal with the large amount of unstructured text data, effective natural language processing (NLP) methods need to be developed for various...

Pelvic Injury Discriminative Model Based on Data Mining Algorithm.

Fa yi xue za zhi
OBJECTIVES: To reduce the dimension of characteristic information extracted from pelvic CT images by using principal component analysis (PCA) and partial least squares (PLS) methods. To establish a support vector machine (SVM) classification and iden...

Characterizing Infant Mortality Using Data Mining - A Case Study in Two Brazilian States - Santa Catarina and Amapá.

Studies in health technology and informatics
Infant mortality is characterized by the death of young children under the age of one, and it is an issue affecting millions of children in the world. The objective of this article is to employ concepts of knowledge discovery in databases, specifical...

Towards the Application of Machine Learning in Emergency Informatics.

Studies in health technology and informatics
Emergency care is one of the cornerstone parts of the world health organization's action plan. Rapid response and immediate care are considered in agile emergency care. Artificial intelligence (AI) and informatics have been applied to fulfill these r...

Automated extraction of genes associated with antibiotic resistance from the biomedical literature.

Database : the journal of biological databases and curation
The detection of bacterial antibiotic resistance phenotypes is important when carrying out clinical decisions for patient treatment. Conventional phenotypic testing involves culturing bacteria which requires a significant amount of time and work. Who...

A Hybrid Protocol for Identifying Comorbidity-Based Potential Drugs for COVID-19 Using Biomedical Literature Mining, Network Analysis, and Deep Learning.

Methods in molecular biology (Clifton, N.J.)
Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) has spread on an unprecedented scale around the globe. Despite of 141,975 published papers on COVID-19 and several hundreds of new studies carri...

Text Mining and Machine Learning Protocol for Extracting Human-Related Protein Phosphorylation Information from PubMed.

Methods in molecular biology (Clifton, N.J.)
In the modern health care research, protein phosphorylation has gained an enormous attention from the researchers across the globe and requires automated approaches to process a huge volume of data on proteins and their modifications at the cellular ...

Combining Literature Mining and Machine Learning for Predicting Biomedical Discoveries.

Methods in molecular biology (Clifton, N.J.)
The major outcomes and insights of scientific research and clinical study end up in the form of publication or clinical record in an unstructured text format. Due to advancements in biomedical research, the growth of published literature is getting t...

Biomedical Literature Mining for Repurposing Laboratory Tests.

Methods in molecular biology (Clifton, N.J.)
Epidemiological studies identifying biological markers of disease state are valuable, but can be time-consuming, expensive, and require extensive intuition and expertise. Furthermore, not all hypothesized markers will be borne out in a study, suggest...

Application of Artificial Intelligence in Drug Discovery.

Current pharmaceutical design
Due to the heap of data sets available for drug discovery, modern drug discovery has taken the shape of big data. Usage of Artificial intelligence (AI) can help to modify drug discovery based on big data to precised, knowledgeable data. The pharmaceu...