AIMC Topic: Machine Learning

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Using natural language processing to classify social work interventions.

The American journal of managed care
OBJECTIVES: Health care organizations are increasingly employing social workers to address patients' social needs. However, social work (SW) activities in health care settings are largely captured as text data within electronic health records (EHRs),...

Exploring the potential of transfer learning for metamodels of heterogeneous material deformation.

Journal of the mechanical behavior of biomedical materials
From the nano-scale to the macro-scale, biological tissue is spatially heterogeneous. Even when tissue behavior is well understood, the exact subject specific spatial distribution of material properties is often unknown. And, when developing computat...

Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes.

Proteins
Predicting the structure of multi-protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure prediction methods generally leverage predefined structural features to di...

Application of machine learning in the diagnosis of gastric cancer based on noninvasive characteristics.

PloS one
BACKGROUND: The diagnosis of gastric cancer mainly relies on endoscopy, which is invasive and costly. The aim of this study is to develop a predictive model for the diagnosis of gastric cancer based on noninvasive characteristics.

Predicting self-harm within six months after initial presentation to youth mental health services: A machine learning study.

PloS one
BACKGROUND: A priority for health services is to reduce self-harm in young people. Predicting self-harm is challenging due to their rarity and complexity, however this does not preclude the utility of prediction models to improve decision-making rega...

An enhanced approach to the robust discriminant analysis and class sparsity based embedding.

Neural networks : the official journal of the International Neural Network Society
In recent times, feature extraction attracted much attention in machine learning and pattern recognition fields. This paper extends and improves a scheme for linear feature extraction that can be used in supervised multi-class classification problems...

An Enhanced Protein Fold Recognition for Low Similarity Datasets Using Convolutional and Skip-Gram Features With Deep Neural Network.

IEEE transactions on nanobioscience
The protein fold recognition is one of the important tasks of structural biology, which helps in addressing further challenges like predicting the protein tertiary structures and its functions. Many machine learning works are published to identify th...

Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes.

BMC medical informatics and decision making
BACKGROUND: Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia. The avalanche of healthcare data is accelerating precision and personalized medicine. Artificial intelligence and algorithm-based approaches are be...