AIMC Topic: Machine Learning

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Multi-instance learning based artificial intelligence model to assist vocal fold leukoplakia diagnosis: A multicentre diagnostic study.

American journal of otolaryngology
OBJECTIVE: To develop a multi-instance learning (MIL) based artificial intelligence (AI)-assisted diagnosis models by using laryngoscopic images to differentiate benign and malignant vocal fold leukoplakia (VFL).

Pollen identification through convolutional neural networks: First application on a full fossil pollen sequence.

PloS one
The automation of pollen identification has seen vast improvements in the past years, with Convolutional Neural Networks coming out as the preferred tool to train models. Still, only a small portion of works published on the matter address the identi...

Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation.

Small methods
Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent...

Construction and evaluation of a metabolic correlation diagnostic model for diabetes based on machine learning algorithms.

Environmental toxicology
BACKGROUND: Diabetes mellitus (DM) is a prevalent chronic disease marked by significant metabolic dysfunctions. Understanding its molecular mechanisms is vital for early diagnosis and treatment strategies.

Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction.

Interdisciplinary sciences, computational life sciences
Autism spectrum disorder (ASD) is a complex, severe disorder related to brain development. It impairs patient language communication and social behaviors. In recent years, ASD researches have focused on a single-modal neuroimaging data, neglecting th...

Learning active subspaces and discovering important features with Gaussian radial basis functions neural networks.

Neural networks : the official journal of the International Neural Network Society
Providing a model that achieves a strong predictive performance and is simultaneously interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these two objectives. To address this ...

Long-term causal effects estimation via latent surrogates representation learning.

Neural networks : the official journal of the International Neural Network Society
Estimating long-term causal effects based on short-term surrogates is a significant but challenging problem in many real-world applications such as marketing and medicine. Most existing methods estimate causal effects in an idealistic and simplistic ...

DCDLN: A densely connected convolutional dynamic learning network for malaria disease diagnosis.

Neural networks : the official journal of the International Neural Network Society
Malaria is a significant health concern worldwide, particularly in Africa where its prevalence is still alarmingly high. Using artificial intelligence algorithms to diagnose cells with malaria provides great convenience for clinicians. In this paper,...

Data-driven shortened Insomnia Severity Index (ISI): a machine learning approach.

Sleep & breathing = Schlaf & Atmung
BACKGROUND: The Insomnia Severity Index (ISI) is a widely used questionnaire with seven items for identifying the risk of insomnia disorder. Although the ISI is still short, more shortened versions are emerging for repeated monitoring in routine clin...