AIMC Topic: Machine Learning

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Development and validation of machine-learning model based on dynamic tumor markers in predicting pathological complete response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer: a multicenter cohort study.

International journal of colorectal disease
OBJECTIVE: In this study, we constructed a new pCR predictor based on dynamic tumor marker changes before and after NCRT, the dynamic tumor marker score (DTMS), and combined it with other clinicopathological features to build a machine-learning model...

Construction of a predictive model for the risk of moderate-to-severe cancer-related fatigue in colorectal cancer chemotherapy patients: an interpretable machine learning approach.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
PURPOSE: This study aimed to analyze the influencing factors of moderate-to-severe cancer-related fatigue (CRF) in colorectal cancer (CRC) chemotherapy patients and to develop a predictive risk stratification model.

Machine learning based classification of imagined speech electroencephalogram data from the amplitude and phase spectrum of frequency domain EEG signal.

Biomedical physics & engineering express
Imagined speech classification involves decoding brain signals to recognize verbalized thoughts or intentions without actual speech production. This technology has significant implications for individuals with speech impairments, offering a means to ...

Low-Cost 3D-Printed Mazes with Open-Source ML Tracking for Mouse Behavior.

eNeuro
Behavioral neuroscience research often requires substantial financial investment in specialized equipment and software, creating barriers for new investigators and limiting the flexibility of established laboratories. This study explores how 3D print...

Construction of an automated machine learning-based predictive model for postoperative pulmonary complications risk in non-small cell lung cancer patients undergoing thoracoscopic surgery.

PloS one
OBJECTIVE: To develop a predictive framework integrating machine learning and clinical parameters for postoperative pulmonary complications (PPCs) in non-small cell lung cancer (NSCLC) patients undergoing video-assisted thoracic surgery (VATS).

Diabetic retinopathy as the primary predictor of mild cognitive impairment in type 2 diabetes: Insights from machine learning models.

PloS one
Mild cognitive impairment (MCI) is a significant and increasingly recognized problem in individuals with type 2 diabetes mellitus (T2DM). This study aims to develop a machine-learning model to predict MCI in patients with T2DMThe dataset was obtained...

Leveraging pre-trained computer vision models for accurate classification of meat freshness.

Food chemistry
Increasing concerns about food quality and safety have led to research into ways to assess meat freshness. Advances in deep learning, particularly image classification, enable up new possibilities for fast and non-destructive methods of evaluating me...

Construction and evaluation of prediction model for renal function recovery in acute kidney injury patients undergoing continuous renal replacement therapy based on machine learning algorithms.

Annals of medicine
The primary objective of this study is to employ machine learning (ML) algorithms to develop predictive models for renal function recovery in critically ill patients undergoing continuous renal replacement therapy (CRRT) due to acute kidney injury (...

Integrative Omics and AI-Driven Systems Biology: Multilayer Networks Decoding Health and Resilience.

Journal of proteome research
Honey bees () are vital pollinators essential for maintaining ecosystem stability and global food production, but they face escalating threats from pathogens, agrochemicals, and climate change. Although proteomics has advanced our understanding of be...

A Machine Learning-Assisted Liquid Crystal Droplet Array Platform for the Sensitive and Selective Detection of Per- and Polyfluoroalkyl Substances (PFAS) in Water.

ACS sensors
We report a machine learning (ML)-assisted liquid crystal (LC) droplet array platform for the detection of per- and polyfluoroalkyl substances (PFAS) in water. Our approach uses an autoencoder network to process thousands of images obtained from arra...