AIMC Topic: Machine Learning

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Combining machine learning and molecular simulations to predict the stability of amorphous drugs.

The Journal of chemical physics
Amorphous drugs represent an intriguing option to bypass the low solubility of many crystalline formulations of pharmaceuticals. The physical stability of the amorphous phase with respect to the crystal is crucial to bring amorphous formulations into...

Practical Guide to Honest Causal Forests for Identifying Heterogeneous Treatment Effects.

American journal of epidemiology
"Heterogeneous treatment effects" is a term which refers to conditional average treatment effects (i.e., CATEs) that vary across population subgroups. Epidemiologists are often interested in estimating such effects because they can help detect popula...

Suggestion of statistical validation on feature importance of machine learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Feature importance methods are widely used in machine learning analysis for medical datasets as both primary and subsidiary tools. These methods aid in selecting biomarkers or markers indicating target diseases, and can provide valuable insight into ...

Bio-signals Collecting System for Fatigue Level Classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Fatigue is a risk factor that reduces quality of life and work efficiency, and threatens safety in a high-risk environment. However, fatigue is not yet precisely defined and is not a quantified concept as it relies on subjective evaluation. The purpo...

Memory Classifiers for Robust ECG Classification against Physiological Noise.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The development of sophisticated machine learning algorithms has made it possible to detect critical health conditions like cardiac arrhythmia, directly from electrocardiogram (ECG) recordings. Large-scale machine learning models, like deep neural ne...

Uniform Selection And Representation Matching: A Framework For Classifying Wound Healing Stage.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
We propose the Uniform Selection and Representation Matching framework, an image classification framework that leverages co-teaching, contrastive learning, representation matching, and uniform selection to perform accurate wound stage classification ...

Assessment of Driver's Stress using Multimodal Biosignals and Regularized Deep Kernel Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In this work, we classify the stress state of car drivers using multimodal physiological signals and regularized deep kernel learning. Using a driving simulator in a controlled environment, we acquire electrocardiography (ECG), electrodermal activity...

Classification of Mental Stress Levels using EEG Connectivity and Convolutional Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Classifying mental stress is important as it helps in identifying the type and severity of stress, which can inform the most appropriate treatment or intervention. In this study, we propose utilizing electroencephalography (EEG) signals with convolut...

Clinical Risk Prediction Models with Meta-Learning Prototypes of Patient Heterogeneity.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Hospitalized patients sometimes have complex health conditions, such as multiple diseases, underlying diseases, and complications. The heterogeneous patient conditions may have various representations. A generalized model ignores the differences amon...

Overcoming Data Scarcity in Human Activity Recognition.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Wearable sensors have become increasingly popular in recent years, with technological advances leading to cheaper, more widely available, and smaller devices. As a result, there has been a growing interest in applying machine learning techniques for ...