AIMC Topic: Machine Learning

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Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients: A Promising Approach to Personalized Medicine.

International journal of molecular sciences
The COVID-19 pandemic has presented an unprecedented challenge to the healthcare system. Identifying the genomics and clinical biomarkers for effective patient stratification and management is critical to controlling the spread of the disease. Omics ...

Stable isotope and trace element analyses with non-linear machine-learning data analysis improved coffee origin classification and marker selection.

Journal of the science of food and agriculture
BACKGROUND: This study investigated the geographical origin classification of green coffee beans from continental to country and regional levels. An innovative approach combined stable isotope and trace element analyses with non-linear machine learni...

High-Level CNN and Machine Learning Methods for Speaker Recognition.

Sensors (Basel, Switzerland)
Speaker Recognition (SR) is a common task in AI-based sound analysis, involving structurally different methodologies such as Deep Learning or "traditional" Machine Learning (ML). In this paper, we compared and explored the two methodologies on the DE...

Machine-learning screening of luminogens with aggregation-induced emission characteristics for fluorescence imaging.

Journal of nanobiotechnology
Due to the excellent biocompatible physicochemical performance, luminogens with aggregation-induced emission (AIEgens) characteristics have played a significant role in biomedical fluorescence imaging recently. However, screening AIEgens for special ...

A method for real-time mechanical characterisation of microcapsules.

Biomechanics and modeling in mechanobiology
Characterising the mechanical properties of flowing microcapsules is important from both fundamental and applied points of view. In the present study, we develop a novel multilayer perceptron (MLP)-based machine learning (ML) approach, for real-time ...

Predicting heart failure in-hospital mortality by integrating longitudinal and category data in electronic health records.

Medical & biological engineering & computing
Heart failure is a life-threatening syndrome that is diagnosed in 3.6 million people worldwide each year. We propose a deep fusion learning model (DFL-IMP) that uses time series and category data from electronic health records to predict in-hospital ...

Human-Centered Design to Address Biases in Artificial Intelligence.

Journal of medical Internet research
The potential of artificial intelligence (AI) to reduce health care disparities and inequities is recognized, but it can also exacerbate these issues if not implemented in an equitable manner. This perspective identifies potential biases in each stag...

Bat4RCT: A suite of benchmark data and baseline methods for text classification of randomized controlled trials.

PloS one
Randomized controlled trials (RCTs) play a major role in aiding biomedical research and practices. To inform this research, the demand for highly accurate retrieval of scientific articles on RCT research has grown in recent decades. However, correctl...

AiZynthTrain: Robust, Reproducible, and Extensible Pipelines for Training Synthesis Prediction Models.

Journal of chemical information and modeling
We introduce the AiZynthTrain Python package for training synthesis models in a robust, reproducible, and extensible way. It contains two pipelines that create a template-based one-step retrosynthesis model and a RingBreaker model that can be straigh...