AIMC Topic: Machine Learning

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Early identification of potentially reversible cancer cachexia using explainable machine learning driven by body weight dynamics: a multicenter cohort study.

The American journal of clinical nutrition
BACKGROUND: Cachexia is associated with multiple adverse outcomes in cancer. However, clinical decision-making for oncology patients at the cachexia stage presents significant challenges.

A machine learning approach for estimating Eastern Asian origins from massive screening of Y chromosomal short tandem repeats polymorphisms.

International journal of legal medicine
Inferring the ancestral origin of DNA evidence recovered from crime scenes is crucial in forensic investigations, especially in the absence of a direct suspect match. Ancestry informative markers (AIMs) have been widely researched and commercially de...

Machine Learning to Detect Cervical Spine Fractures Missed by Radiologists on CT: Analysis Using Seven Award-Winning Models From the 2022 RSNA Cervical Spine Fracture AI Challenge.

AJR. American journal of roentgenology
Available data on radiologists' missed cervical spine fractures are based primarily on studies using human reviewers to identify errors on reevaluation; such studies do not capture the full extent of missed fractures. The purpose of this study was ...

Machine learning-based automated waste sorting in the construction industry: A comparative competitiveness case study.

Waste management (New York, N.Y.)
This article presents a comparative analysis of the circularity and cost-efficiency of two distinct construction material recycling processes: ML-based automated sorting (MLAS) and conventional sorting technologies. Empirical data was collected from ...

Utilizing natural language processing to identify pediatric patients experiencing status epilepticus.

Seizure
PURPOSE: Compare the identification of patients with established status epilepticus (ESE) and refractory status epilepticus (RSE) in electronic health records (EHR) using human review versus natural language processing (NLP) assisted review.

Developing machine-learning-based amyloidogenicity predictors with Cross-Beta DB.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: The importance of protein amyloidogenesis, associated with various diseases and functional roles, has driven the creation of computational predictors of amyloidogenicity. The accuracy of these predictors, particularly those utilizing ar...

Unveiling neural activity changes in mild cognitive impairment using microstate analysis and machine learning.

Journal of Alzheimer's disease : JAD
BACKGROUND: Mild cognitive impairment (MCI) is recognized as a condition that may increase the risk of developing Alzheimer's disease (AD). Understanding the neural correlates of MCI is crucial for elucidating its pathophysiology and developing effec...

CovCysPredictor: Predicting Selective Covalently Modifiable Cysteines Using Protein Structure and Interpretable Machine Learning.

Journal of chemical information and modeling
Targeted covalent inhibition is a powerful therapeutic modality in the drug discoverer's toolbox. Recent advances in covalent drug discovery, in particular, targeting cysteines, have led to significant breakthroughs for traditionally challenging targ...

Raman Spectroscopy and Exosome-Based Machine Learning Predicts the Efficacy of Neoadjuvant Therapy for HER2-Positive Breast Cancer.

Analytical chemistry
Early prediction of the neoadjuvant therapy efficacy for HER2-positive breast cancer is crucial for personalizing treatment and enhancing patient outcomes. Exosomes, which play a role in tumor development and treatment response, are emerging as poten...

Toward Resolving Heterogeneous Mixtures of Nanocarriers in Drug Delivery Systems through Light Scattering and Machine Learning.

ACS nano
Nanocarriers (NCs) have emerged as a revolutionary approach in targeted drug delivery, promising to enhance drug efficacy and reduce toxicity through precise targeting and controlled release mechanisms. Despite their potential, the clinical adoption ...