AIMC Topic: Machine Learning

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Machine Learning Models for Data-Driven Prediction of Diabetes by Lifestyle Type.

International journal of environmental research and public health
The prevalence of diabetes has been increasing in recent years, and previous research has found that machine-learning models are good diabetes prediction tools. The purpose of this study was to compare the efficacy of five different machine-learning ...

A correlation-based feature analysis of physical examination indicators can help predict the overall underlying health status using machine learning.

Scientific reports
As a systematic investigation of the correlations between physical examination indicators (PEIs) is lacking, most PEIs are currently independently used for disease warning. This results in the general physical examination having limited diagnostic va...

Source discrimination of mine water based on the random forest method.

Scientific reports
Machine learning is one of the widely used techniques to pattern recognition. Use of the machine learning tools is becoming a more accessible approach for predictive model development in preventing engineering disaster. The objective of the research ...

Predicting cognitive impairment in chronic kidney disease patients using structural and functional brain network: An application study of artificial intelligence.

Progress in neuro-psychopharmacology & biological psychiatry
OBJECTIVE: To develop and validate artificial intelligence models for the prediction of cognitive impairment in chronic kidney disease (CKD) patients using structural and functional brain network.

The use of machine learning and artificial intelligence within pediatric critical care.

Pediatric research
The field of pediatric critical care has been hampered in the era of precision medicine by our inability to accurately define and subclassify disease phenotypes. This has been caused by heterogeneity across age groups that further challenges the abil...

Enhanced survival prediction using explainable artificial intelligence in heart transplantation.

Scientific reports
The most limiting factor in heart transplantation is the lack of donor organs. With enhanced prediction of outcome, it may be possible to increase the life-years from the organs that become available. Applications of machine learning to tabular data,...

One-Class Convolutional Neural Networks for Water-Level Anomaly Detection.

Sensors (Basel, Switzerland)
Companies that own water systems to provide water storage and distribution services always strive to enhance and efficiently distribute water to different places for various purposes. However, these water systems are likely to face problems ranging f...

Towards machine learning for hydrogel drug delivery systems.

Trends in biotechnology
Hydrogel drug delivery system development is complex and laborious, and machine learning (ML) techniques hold great promise in accelerating the process. We highlight recent advances and strategies for data collection and ML, and we discuss the potent...

A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations.

International journal of environmental research and public health
Personalized medicine requires an understanding of treatment effect heterogeneity. Evolving toward causal evidence for scenarios not studied in randomized trials necessitates a methodology using real-world evidence. Herein, we demonstrate a methodolo...

Ontology-based feature engineering in machine learning workflows for heterogeneous epilepsy patient records.

Scientific reports
Biomedical ontologies are widely used to harmonize heterogeneous data and integrate large volumes of clinical data from multiple sources. This study analyzed the utility of ontologies beyond their traditional roles, that is, in addressing a challengi...