AIMC Topic: Machine Learning

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Research related to the diagnosis of prostate cancer based on machine learning medical images: A review.

International journal of medical informatics
BACKGROUND: Prostate cancer is currently the second most prevalent cancer among men. Accurate diagnosis of prostate cancer can provide effective treatment for patients and greatly reduce mortality. The current medical imaging tools for screening pros...

Emerging Directions for Carbon Capture Technologies: A Synergy of High-Throughput Theoretical Calculations and Machine Learning.

Environmental science & technology
As the world grapples with the challenges of energy transition and industrial decarbonization, the development of carbon capture technologies presents a promising solution. The Scalable Modeling, Artificial Intelligence (AI), and Rapid Theoretical ca...

ML-DSTnet: A Novel Hybrid Model for Breast Cancer Diagnosis Improvement Based on Image Processing Using Machine Learning and Dempster-Shafer Theory.

Computational intelligence and neuroscience
Medical intelligence detection systems have changed with the help of artificial intelligence and have also faced challenges. Breast cancer diagnosis and classification are part of this medical intelligence system. Early detection can lead to an incre...

A hybrid machine learning feature selection model-HMLFSM to enhance gene classification applied to multiple colon cancers dataset.

PloS one
Colon cancer is a significant global health problem, and early detection is critical for improving survival rates. Traditional detection methods, such as colonoscopies, can be invasive and uncomfortable for patients. Machine Learning (ML) algorithms ...

Application of Machine Learning Models to Biomedical and Information System Signals From Critically Ill Adults.

Chest
BACKGROUND: Machine learning (ML)-derived notifications for impending episodes of hemodynamic instability and respiratory failure events are interesting because they can alert physicians in time to intervene before these complications occur.

Identification of Potential Neddylation-related Key Genes in Ischemic Stroke based on Machine Learning Methods.

Molecular neurobiology
Ischemic stroke (IS) is a complex neurological disease that can lead to severe disability or even death. Understanding the molecular mechanisms involved in the occurrence and progression of IS is of great significance for developing effective treatme...

Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.

Artificial intelligence in medicine
OBJECTIVE: Natural language processing (NLP) combined with machine learning (ML) techniques are increasingly used to process unstructured/free-text patient-reported outcome (PRO) data available in electronic health records (EHRs). This systematic rev...

Potential for Machine Learning to Address Data Gaps in Human Toxicity and Ecotoxicity Characterization.

Environmental science & technology
Machine Learning (ML) is increasingly applied to fill data gaps in assessments to quantify impacts associated with chemical emissions and chemicals in products. However, the systematic application of ML-based approaches to fill chemical data gaps is ...

LCK-SafeScreen-Model: An Advanced Ensemble Machine Learning Approach for Estimating the Binding Affinity between Compounds and LCK Target.

Molecules (Basel, Switzerland)
The lymphocyte-specific protein tyrosine kinase (LCK) is a critical target in leukemia treatment. However, potential off-target interactions involving LCK can lead to unintended consequences. This underscores the importance of accurately predicting t...

Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression.

JAMA network open
IMPORTANCE: Predictive models using machine learning techniques have potential to improve early detection and management of Alzheimer disease (AD). However, these models potentially have biases and may perpetuate or exacerbate existing disparities.