AIMC Topic: Machine Learning

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Multi-task machine learning models for simultaneous prediction of tissue-to-blood partition coefficients of chemicals in mammals.

Environmental research
Tissue-to-blood partition coefficients (P) are crucial for assessing the distribution of chemicals in organisms. Given the lack of experimental data and laborious nature of experimental methods, there is an urgent need to develop efficient predictive...

A cluster-based ensemble approach for congenital heart disease prediction.

Computer methods and programs in biomedicine
BACKGROUND: One of the most prevalent birth disorders is congenital heart diseases (CHD). Although CHD risk factors have been the subject of numerous studies, their propensity to cause CHD has not been tested. Particularly few research has attempted ...

Hybrid deep learning approach to improve classification of low-volume high-dimensional data.

BMC bioinformatics
BACKGROUND: The performance of machine learning classification methods relies heavily on the choice of features. In many domains, feature generation can be labor-intensive and require domain knowledge, and feature selection methods do not scale well ...

Applications of machine learning and deep learning in SPECT and PET imaging: General overview, challenges and future prospects.

Pharmacological research
The integration of positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging techniques with machine learning (ML) algorithms, including deep learning (DL) models, is a promising approach. This integration enha...

Domain-adaptive neural networks improve supervised machine learning based on simulated population genetic data.

PLoS genetics
Investigators have recently introduced powerful methods for population genetic inference that rely on supervised machine learning from simulated data. Despite their performance advantages, these methods can fail when the simulated training data does ...

Cooperative Learning for Personalized Context-Aware Pain Assessment From Wearable Data.

IEEE journal of biomedical and health informatics
Despite the promising performance of automated pain assessment methods, current methods suffer from performance generalization due to the lack of relatively large, diverse, and annotated pain datasets. Further, the majority of current methods do not ...

Machine learning-based classification analysis of knowledge worker mental stress.

Frontiers in public health
The aim of this study is to analyze the performance of classifying stress and non-stress by measuring biosignal data using a wearable watch without interfering with work activities at work. An experiment is designed where participants wear a Galaxy W...

Enhancing economic competitiveness analysis through machine learning: Exploring complex urban features.

PloS one
Urban economic competitiveness is a fundamental indicator for assessing the level of urban development and serves as an effective approach for understanding regional disparities. Traditional economic competitiveness research that relies solely on tra...

A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring.

Nature biomedical engineering
The complex relationships between continuously monitored health signals and therapeutic regimens can be modelled via machine learning. However, the clinical implementation of the models will require changes to clinical workflows. Here we outline Clin...

Leveraging automated approaches to categorize birth defects from abstracted birth hospitalization data.

Birth defects research
BACKGROUND: The Surveillance for Emerging Threats to Pregnant People and Infants Network (SET-NET) collects data abstracted from medical records and birth defects registries on pregnant people and their infants to understand outcomes associated with ...