AIMC Topic: Machine Learning

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Machine learning models outperform manual result review for the identification of wrong blood in tube errors in complete blood count results.

International journal of laboratory hematology
INTRODUCTION: Wrong blood in tube (WBIT) errors are a significant patient-safety issue encountered by clinical laboratories. This study assessed the performance of machine learning models for the identification of WBIT errors affecting complete blood...

Unified Deep Learning Model for Multitask Reaction Predictions with Explanation.

Journal of chemical information and modeling
There is significant interest and importance to develop robust machine learning models to assist organic chemistry synthesis. Typically, task-specific machine learning models for distinct reaction prediction tasks have been developed. In this work, w...

FILM: Filtering and Machine Learning for Malware Detection in Edge Computing.

Sensors (Basel, Switzerland)
Machine learning with static-analysis features extracted from malware files has been adopted to detect malware variants, which is desirable for resource-constrained edge computing and Internet-of-Things devices with sensors; however, this learned mod...

Test Strategy Optimization Based on Soft Sensing and Ensemble Belief Measurement.

Sensors (Basel, Switzerland)
Resulting from the short production cycle and rapid design technology development, traditional prognostic and health management (PHM) approaches become impractical and fail to match the requirement of systems with structural and functional complexity...

A clarification of the nuances in the fairness metrics landscape.

Scientific reports
In recent years, the problem of addressing fairness in machine learning (ML) and automatic decision making has attracted a lot of attention in the scientific communities dealing with artificial intelligence. A plethora of different definitions of fai...

Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany.

Scientific reports
During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and interpretable forecasts of disease incidents are a vital tool for policymakers to manage healthcare resou...

Implicitly perturbed Hamiltonian as a class of versatile and general-purpose molecular representations for machine learning.

Nature communications
Unraveling challenging problems by machine learning has recently become a hot topic in many scientific disciplines. For developing rigorous machine-learning models to study problems of interest in molecular sciences, translating molecular structures ...

Precision measurement of cardiac structure and function in cardiovascular magnetic resonance using machine learning.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
BACKGROUND: Measurement of cardiac structure and function from images (e.g. volumes, mass and derived parameters such as left ventricular (LV) ejection fraction [LVEF]) guides care for millions. This is best assessed using cardiovascular magnetic res...

Network analytics and machine learning for predicting length of stay in elderly patients with chronic diseases at point of admission.

BMC medical informatics and decision making
BACKGROUND: An aging population with a burden of chronic diseases puts increasing pressure on health care systems. Early prediction of the hospital length of stay (LOS) can be useful in optimizing the allocation of medical resources, and improving he...

Short-Text Classification Detector: A Bert-Based Mental Approach.

Computational intelligence and neuroscience
With the continuous development of the Internet, social media based on short text has become popular. However, the sparsity and shortness of essays will restrict the accuracy of text classification. Therefore, based on the Bert model, we capture the ...