AIMC Topic: Machine Learning

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Automatic extraction of ranked SNP-phenotype associations from text using a BERT-LSTM-based method.

BMC bioinformatics
Extraction of associations of singular nucleotide polymorphism (SNP) and phenotypes from biomedical literature is a vital task in BioNLP. Recently, some methods have been developed to extract mutation-diseases affiliations. However, no accessible met...

Predicting Unreported Micronutrients From Food Labels: Machine Learning Approach.

Journal of medical Internet research
BACKGROUND: Micronutrient deficiencies represent a major global health issue, with over 2 billion individuals experiencing deficiencies in essential vitamins and minerals. Food labels provide consumers with information regarding the nutritional conte...

Explainable Artificial Intelligence and Cardiac Imaging: Toward More Interpretable Models.

Circulation. Cardiovascular imaging
Artificial intelligence applications have shown success in different medical and health care domains, and cardiac imaging is no exception. However, some machine learning models, especially deep learning, are considered black box as they do not provid...

Open Data and transparency in artificial intelligence and machine learning: A new era of research.

F1000Research
Artificial Intelligence (AI) and machine learning are the current forefront of computer science and technology. AI and related sub-disciplines, including machine learning, are essential technologies which have enabled the widespread use of technolog...

Artificial intelligence: Augmenting telehealth with large language models.

Journal of telemedicine and telecare
This brief editorial describes an emerging area of machine learning technology called large language models (LLMs). LLMs, such as ChatGPT, are the technological disruptor of this decade. They are going to be integrated into search engines (Bing and G...

Multi-event survival analysis through dynamic multi-modal learning for ICU mortality prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Survival analysis is widely applied for assessing the expected duration of patient status towards event occurrences such as mortality in healthcare domain, which is generally considered as a time-to-event problem. Patients w...

Lifelong learning with Shared and Private Latent Representations learned through synaptic intelligence.

Neural networks : the official journal of the International Neural Network Society
This paper explores a novel lifelong learning method with Shared and Private Latent Representations (SPLR), which are learned through synaptic intelligence. To solve a sequence of tasks, by considering the entire parameter learning trajectory, SPLR c...

Identification of DNA-binding proteins by Kernel Sparse Representation via L-matrix norm.

Computers in biology and medicine
An understanding of DNA-binding proteins is helpful in exploring the role that proteins play in cell biology. Furthermore, the prediction of DNA-binding proteins is essential for the chemical modification and structural composition of DNA, and is of ...

Piloting an automated clinical trial eligibility surveillance and provider alert system based on artificial intelligence and standard data models.

BMC medical research methodology
BACKGROUND: To advance new therapies into clinical care, clinical trials must recruit enough participants. Yet, many trials fail to do so, leading to delays, early trial termination, and wasted resources. Under-enrolling trials make it impossible to ...

A voting-based machine learning approach for classifying biological and clinical datasets.

BMC bioinformatics
BACKGROUND: Different machine learning techniques have been proposed to classify a wide range of biological/clinical data. Given the practicability of these approaches accordingly, various software packages have been also designed and developed. Howe...