AIMC Topic: Machine Learning

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Machine Learning Prediction of Financial Toxicity in Patients with Resected Lung Cancer.

Journal of the American College of Surgeons
BACKGROUND: Financial toxicity (FT) refers to the financial stress and detrimental impact on quality of life experienced by patients due to treatment cost. In patients with resected lung cancer (LC), we sought to identify those at risk of developing ...

Machine Learning-Based Screening of Cosmetic Ingredients Identifies Vat Blue 6 as a Thyroid Hormone Receptor β Disruptor.

Environmental science & technology
Thyroid disorders are among the most prevalent endocrine conditions worldwide, exhibiting a rising incidence and disproportionately affecting women. In this study, we hypothesized that cosmetics may contain previously unidentified thyroid-disrupting ...

On-Mask Magnetoelastic Sensor Network for Self-Powered Respiratory Monitoring.

ACS nano
Respiratory monitoring is crucial because it provides key insights into a person's health and physiological conditions. Conventional respiratory sensing is significantly challenged by the presence of water vapor in exhaled breath. An on-mask magnetoe...

Machine Learning-Based Retention Time Prediction Tool for Routine LC-MS Data Analysis.

Journal of chemical information and modeling
Accurate retention time () prediction models can significantly improve liquid chromatography-mass spectrometry (LC-MS) data analysis widely used in chemical synthesis. As hundreds of thousands of syntheses are performed annually at Enamine, a large a...

Noninvasive real-time monitoring of cellular spatiotemporal dynamics via machine learning-enhanced electrical impedance spectroscopy.

Science advances
Monitoring cellular spatiotemporal dynamics is essential for understanding complex biological processes such as organ development and cancer progression. Using live-cell fluorescence microscopy to track cellular dynamics is often limited by dye-induc...

Which explanations do clinicians prefer? A comparative evaluation of XAI understandability and actionability in predicting the need for hospitalization.

BMC medical informatics and decision making
BACKGROUND: This study aims to address the gap in understanding clinicians' attitudes toward explainable AI (XAI) methods applied to machine learning models using tabular data, commonly found in clinical settings. It specifically explores clinicians'...

A novel machine learning architecture to improve classification of intermediate cases in health: workflow and case study for public health.

BMC bioinformatics
BACKGROUND: The practice of medicine has evolved significantly during the past decade, with the emergence of Machine Learning (ML) that offers the opportunity of personalized patient-tailored care. However, ML models still face some challenges when c...

An end-to-end interpretable machine-learning-based framework for early-stage diagnosis of gallbladder cancer using multi-modality medical data.

BMC cancer
BACKGROUND: The accurate early-stage diagnosis of gallbladder cancer (GBC) is regarded as one of the major challenges in the field of oncology. However, few studies have focused on the comprehensive classification of GBC based on multiple modalities....

A comprehensive machine learning for high throughput Tuberculosis sequence analysis, functional annotation, and visualization.

Scientific reports
With human guidance, computers now use machine learning (ML) in artificial intelligence (AI) to learn from data, detect trends, and make predictions. Software can adapt and improve with new information. Imaging scans leverage pattern recognition to p...

Analysis of the mechanism of physical activity enhancing well-being among college students using artificial neural network.

Scientific reports
This study explores the impact mechanism of college students' sports behavior on their well-being by constructing an Artificial Neural Network (ANN) model. The study employs an ANN architecture that combines a Long Short-Term Memory (LSTM) network an...