AIMC Topic: Machine Learning

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Predicting Mortality in COVID-19 Patients Using 6 Machine Learning Algorithms.

Studies in health technology and informatics
In late 2019, COVID-19 appeared and has since spread worldwide as the new pandemic, causing more than 6 million deaths. In dealing with this global crisis, the contribution of Artificial Intelligence was also important through the possibilities of cr...

A Hybrid AI-Based Method for ICD Classification of Medical Documents.

Studies in health technology and informatics
Automatic document classification is a common problem that has successfully been addressed with machine learning methods. However, these methods require extensive training data, which is not always readily available. Additionally, in privacy-sensitiv...

Machine learning aided dimensionality reduction toward a resource efficient projective quantum eigensolver: Formal development and pilot applications.

The Journal of chemical physics
In recent times, a variety of hybrid quantum-classical algorithms have been developed that aim to calculate the ground state energies of molecular systems on Noisy Intermediate-Scale Quantum (NISQ) devices. Albeit the utilization of shallow depth cir...

[Advances in machine learning for predicting protein functions].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Proteins play a variety of functional roles in cellular activities and are indispensable for life. Understanding the functions of proteins is crucial in many fields such as medicine and drug development. In addition, the application of enzymes in gre...

[A method for photoplethysmography signal quality assessment fusing multi-class features with multi-scale series information].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Photoplethysmography (PPG) is often affected by interference, which could lead to incorrect judgment of physiological information. Therefore, performing a quality assessment before extracting physiological information is crucial. This paper proposed ...

BioAutoMATED: An end-to-end automated machine learning tool for explanation and design of biological sequences.

Cell systems
The design choices underlying machine-learning (ML) models present important barriers to entry for many biologists who aim to incorporate ML in their research. Automated machine-learning (AutoML) algorithms can address many challenges that come with ...