AIMC Topic: Machine Learning

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Predicting high lymph node positivity risk factors in nasopharyngeal carcinoma patients: A multi-model approach.

Medicine
Identifying patients at high risk of an elevated lymph node ratio (LNR) is critical for optimizing the management of nasopharyngeal carcinoma (NPC), as LNR, defined as the ratio of metastatic to examined lymph nodes, serves as a key prognostic indica...

Personalized prediction of post-SMILE refractive outcomes using a machine-learning nomogram.

Medicine
This study aimed to construct a personalized, machine learning-driven nomogram capable of predicting refractive outcomes following small incision lenticule extraction (SMILE). A total of 1253 eyes from 632 patients who underwent SMILE to correct myop...

Precision identification of endometrial malignancy and precancerous lesions: Development of a machine learning model incorporating multidimensional clinical and imaging parameters.

Medicine
To develop and validate a machine learning (ML) model integrating multidimensional clinical, pathomic, and ultrasound radiomic parameters for precise identification of endometrial malignancy and precancerous lesions, with a focus on addressing the di...

Unraveling risk factors and transcriptomic signatures in liver cancer progression and mortality through machine learning and bioinformatics.

Briefings in functional genomics
Liver cancer (LC) is the second leading cause of cancer-related deaths globally, yet the molecular mechanisms linking its progression with associated risk factors (RFs) remain poorly understood. To address this, we developed an integrative multi-stag...

Diagnosing migraine from genome-wide genotype data: a machine learning analysis.

Brain : a journal of neurology
Migraine has an assumed polygenic basis, but the genetic risk variants identified in genome-wide association studies only explain a proportion of the heritability. We aimed to develop machine learning models, capturing non-additive and interactive ef...

A generic pipeline for CADD score generation: chickenCADD and turkeyCADD.

G3 (Bethesda, Md.)
Combined Annotation Dependent Depletion (CADD) is a machine learning approach used to predict the deleteriousness of genetic variants across a genome. By integrating diverse genomic features, CADD assigns a PHRED-like rank score to each potential var...

Estimating recombination using only the allele frequency spectrum.

Genetics
Standard methods for estimating the population recombination parameter, ρ, are dependent on sampling individual genotypes and calculating various types of disequilibria. However, recent machine learning (ML) approaches to estimating recombination hav...

[Artificial intelligence empowering sports medicine].

Zhonghua yi xue za zhi
The rapid advancement of artificial intelligence (AI) technologies, particularly deep learning algorithms and hardware devices, has profoundly transformed diagnostic and therapeutic paradigms in sports medicine. This article reviews the applications ...

Advanced prediction of heart failure risk in elderly diabetic and hypertensive patients using nine machine learning models and novel composite indices: insights from NHANES 2003-2016.

European journal of preventive cardiology
AIMS: As the global population ages, cardiovascular diseases, particularly heart failure (HF), have become leading causes of mortality and disability among elderly patients. Diabetes and hypertension are major risk factors for cardiovascular diseases...

Adaptive modelling approach for predicting causes of death: insights from verbal autopsy data in Tanzania.

International health
BACKGROUND: The World Health Organization (WHO) has approved the use of a verbal autopsy (VA), a survey-based approach to generate out-of-hospital causes of death (CoDs). Through this study, an adaptive Bayesian networks machine learning model was de...