AIMC Topic: Machine Learning

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Insights into the transformation of natural organic matter during UV/peroxydisulfate treatment by FT-ICR MS and machine learning: Non-negligible formation of organosulfates.

Water research
Natural organic matter (NOM) is a major sink of radicals in advanced oxidation processes (AOPs) and understanding the transformation of NOM is important in water treatment. By using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR ...

Machine Learning Model Based on Radiomics for Preoperative Differentiation of Jaw Cystic Lesions.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: This study aims to use machine learning techniques together with radiomics methods to build a preoperative predictive diagnostic model from spiral computed tomography (CT) images. The model is intended for the differential diagnosis of com...

Exploring optical descriptors for rapid estimation of coastal sediment organic carbon and nearby land-use classifications via machine learning models.

Marine pollution bulletin
This study utilizes ultraviolet and fluorescence spectroscopic indices of dissolved organic matter (DOM) from sediments, combined with machine learning (ML) models, to develop an optimized predictive model for estimating sediment total organic carbon...

Predicting operative time for metabolic and bariatric surgery using machine learning models: a retrospective observational study.

International journal of surgery (London, England)
BACKGROUND: Predicting operative time is essential for scheduling surgery and managing the operating room. This study aimed to develop machine learning (ML) models to predict the operative time for metabolic and bariatric surgery (MBS) and to compare...

Machine learning prediction model of major adverse outcomes after pediatric congenital heart surgery: a retrospective cohort study.

International journal of surgery (London, England)
BACKGROUND: Major adverse postoperative outcomes (APOs) can greatly affect mortality, hospital stay, care management and planning, and quality of life. This study aimed to evaluate the performance of five machine learning (ML) algorithms for predicti...

Real-time classification of tumour and non-tumour tissue in colorectal cancer using diffuse reflectance spectroscopy and neural networks to aid margin assessment.

International journal of surgery (London, England)
BACKGROUND: Colorectal cancer is the third most commonly diagnosed malignancy and the second leading cause of mortality worldwide. A positive resection margin following surgery for colorectal cancer is linked with higher rates of local recurrence and...

A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study.

International journal of surgery (London, England)
BACKGROUND: Axillary lymph nodes (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). The aim of this study was to construct a radiogenomic multimodal model, based on machine learning and whole-transcriptome sequencing (WTS), ...

Uncovering Predictors of Low Hippocampal Volume: Evidence from a Large-Scale Machine-Learning-Based Study in the UK Biobank.

Neuroepidemiology
INTRODUCTION: Hippocampal atrophy is an established biomarker for conversion from the normal ageing process to developing cognitive impairment and dementia. This study used a novel hypothesis-free machine-learning approach, to uncover potential risk ...