AIMC Topic: Machine Learning

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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...

Is There a Role for Machine Learning in Liquid Biopsy for Brain Tumors? A Systematic Review.

International journal of molecular sciences
The paucity of studies available in the literature on brain tumors demonstrates that liquid biopsy (LB) is not currently applied for central nervous system (CNS) cancers. The purpose of this systematic review focused on the application of machine lea...

Population-Based Applications and Analytics Using Patient-Reported Outcome Measures.

The Journal of the American Academy of Orthopaedic Surgeons
The intersection of big data and artificial intelligence (AI) has resulted in advances in numerous areas, including machine learning, computer vision, and natural language processing. Although there are many potentially transformative applications of...

Development of a 2D-QSAR Model for Tissue-to-Plasma Partition Coefficient Value with High Accuracy Using Machine Learning Method, Minimum Required Experimental Values, and Physicochemical Descriptors.

European journal of drug metabolism and pharmacokinetics
BACKGROUND: The demand for physiologically based pharmacokinetic (PBPK) model is increasing currently. New drug application (NDA) of many compounds is submitted with PBPK models for efficient drug development. Tissue-to-plasma partition coefficient (...

XRecon: An Explainbale IoT Reconnaissance Attack Detection System Based on Ensemble Learning.

Sensors (Basel, Switzerland)
IoT devices have grown in popularity in recent years. Statistics show that the number of online IoT devices exceeded 35 billion in 2022. This rapid growth in adoption made these devices an obvious target for malicious actors. Attacks such as botnets ...

Predicting congenital syphilis cases: A performance evaluation of different machine learning models.

PloS one
BACKGROUND: Communicable diseases represent a huge economic burden for healthcare systems and for society. Sexually transmitted infections (STIs) are a concerning issue, especially in developing and underdeveloped countries, in which environmental fa...

The Adelaide Score: An artificial intelligence measure of readiness for discharge after general surgery.

ANZ journal of surgery
BACKGROUND: This study aimed to examine the performance of machine learning algorithms for the prediction of discharge within 12 and 24 h to produce a measure of readiness for discharge after general surgery.

Generating synthetic personal health data using conditional generative adversarial networks combining with differential privacy.

Journal of biomedical informatics
A large amount of personal health data that is highly valuable to the scientific community is still not accessible or requires a lengthy request process due to privacy concerns and legal restrictions. As a solution, synthetic data has been studied an...

Challenges in Developing a Real-Time Bee-Counting Radar.

Sensors (Basel, Switzerland)
Detailed within is an attempt to implement a real-time radar signal classification system to monitor and count bee activity at the hive entry. There is interest in keeping records of the productivity of honeybees. Activity at the entrance can be a go...

A diabetes prediction model based on Boruta feature selection and ensemble learning.

BMC bioinformatics
BACKGROUND AND OBJECTIVE: As a common chronic disease, diabetes is called the "second killer" among modern diseases. Currently, there is no medical cure for diabetes. We can only rely on medication for auxiliary treatment. However, many diabetic pati...