AIMC Topic: Machine Learning

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Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach.

Viruses
The clinical application of detecting COVID-19 factors is a challenging task. The existing named entity recognition models are usually trained on a limited set of named entities. Besides clinical, the non-clinical factors, such as social determinant ...

Efficient targeted learning of heterogeneous treatment effects for multiple subgroups.

Biometrics
In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzing treatment effect heterogeneity include estimating treatment effects in clinically relevant subgrou...

Exploring the Quality of Dynamic Open Government Data Using Statistical and Machine Learning Methods.

Sensors (Basel, Switzerland)
Dynamic data (including environmental, traffic, and sensor data) were recently recognized as an important part of Open Government Data (OGD). Although these data are of vital importance in the development of data intelligence applications, such as bu...

Novel machine learning approaches revolutionize protein knowledge.

Trends in biochemical sciences
Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large scale is no l...

Factorizing time-heterogeneous Markov transition for temporal recommendation.

Neural networks : the official journal of the International Neural Network Society
Temporal recommendation which recommends items to users with consideration of time information has been of wide interest in recent years. But huge event space, highly sparse user activities and time-heterogeneous dependency of temporal behaviors make...

Artificial intelligence and machine learning in cardiotocography: A scoping review.

European journal of obstetrics, gynecology, and reproductive biology
INTRODUCTION: Artificial intelligence (AI) is gaining more interest in the field of medicine due to its capacity to learn patterns directly from data. This becomes interesting for the field of cardiotocography (CTG) interpretation, since it promises ...

Physics-informed machine learning methods for biomass gasification modeling by considering monotonic relationships.

Bioresource technology
Machine learning methods have recently shown a broad application prospect in biomass gasification modeling. However, a significant drawback of the machine learning approaches is their poor physical interpretability when relying on limited experimenta...

Classification of Mental Stress from Wearable Physiological Sensors Using Image-Encoding-Based Deep Neural Network.

Biosensors
The human body is designed to experience stress and react to it, and experiencing challenges causes our body to produce physical and mental responses and also helps our body to adjust to new situations. However, stress becomes a problem when it conti...