AIMC Topic: Machine Learning

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Discriminative Detection for Multiple Volatile Organic Compounds via Dynamic Temperature Modulation Based on Mixed Potential Gas Sensor.

ACS sensors
Gas sensors combined with artificial intelligence capable of distinguishing multiple odors hold great promise in volatile organic compounds (VOCs) discriminative detection. However, various issues such as large size, high expenses, and mutual interfe...

Machine Learning-Guided Cobalt@Copper Dual-Metal Electrochemical Sensor for Urinary Creatinine Detection.

ACS sensors
By utilizing the synergistic effects of a dual-metal cobalt@copper electrode and advanced machine learning algorithms, we have developed a reliable and cost-effective electrochemical sensor for creatinine monitoring. The sensor's active surface was f...

CorrAdjust unveils biologically relevant transcriptomic correlations by efficiently eliminating hidden confounders.

Nucleic acids research
Correcting for confounding variables is often overlooked when computing RNA-RNA correlations, even though it can profoundly affect results. We introduce CorrAdjust, a method for identifying and correcting such hidden confounders. CorrAdjust selects a...

SCIG: Machine learning uncovers cell identity genes in single cells by genetic sequence codes.

Nucleic acids research
Deciphering cell identity genes is pivotal to understanding cell differentiation, development, and cell identity dysregulation involving diseases. Here, we introduce SCIG, a machine-learning method to uncover cell identity genes in single cells. In a...

Predictive value of machine learning for PD-L1 expression in NSCLC: a systematic review and meta-analysis.

World journal of surgical oncology
BACKGROUND: As machine learning (ML) continuously develops in cancer diagnosis and treatment, some researchers have attempted to predict the expression of programmed death ligand-1 (PD-L1) in non-small cell lung cancer (NSCLC) by ML. However, there i...

A machine learning-based risk prediction model for diabetic oral ulceration.

BMC oral health
BACKGROUND: Diabetic oral ulceration (DOU) is a prevalent and debilitating complication among diabetic patients, significantly impairing their quality of life and imposing substantial economic burdens. Studies indicate that over 90% of diabetic patie...

Machine learning prediction model with shap interpretation for chronic bronchitis risk assessment based on heavy metal exposure: a nationally representative study.

BMC pulmonary medicine
BACKGROUND: Chronic bronchitis (CB), as a core precursor of Chronic Obstructive Pulmonary Disease (COPD), is crucial for global disease burden prevention and control. Although the association between heavy metal exposure and respiratory damage has be...

Bio inspired feature selection and graph learning for sepsis risk stratification.

Scientific reports
Sepsis remains a leading cause of mortality in critical care settings, necessitating timely and accurate risk stratification. However, existing machine learning models for sepsis prediction often suffer from poor interpretability, limited generalizab...

Enhancing healthcare AI stability with edge computing and machine learning for extubation prediction.

Scientific reports
The advancement of the Internet of Medical Things (IoMT) has revolutionized data acquisition and processing in critical care settings. Given the pivotal role of ventilators, accurately predicting extubation outcomes is essential to optimize patient c...

Improved CKD classification based on explainable artificial intelligence with extra trees and BBFS.

Scientific reports
Chronic kidney disease is a persistent ailment marked by the gradual decline of kidney function. Its classification primarily relies on the estimated glomerular filtration rate and the existence of kidney damage. The kidney disease improving global o...