AIMC Topic: Machine Learning

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Current Status on the Convergence of Artificial Intelligence and Formulation Development in Industry: A Review.

AAPS PharmSciTech
Since Pfizer developed the mRNA vaccine for COVID-19 by leveraging artificial intelligence (AI) for designing the vaccine, integrating AI and allied domains in the drug development process has escalated at an unimaginable rate. Owing to the complex a...

Detection of brain network abnormalities by graph invariants in Alzheimer's disease using MRI images.

Scientific reports
Alzheimer's disease is a major cause of dementia in older adults. It involves gradual changes in brain function that result in cognitive decline, affecting memory, reasoning, and executive skills. The accurate detection of structural abnormalities in...

The global epidemiology, risk factors, and mortality prediction of nocardiosis: an easily missed opportunistic infection.

Scientific reports
This study was to comprehensively investigate the epidemiology of nocardiosis worldwide and develop an interpretable machine learning (ML) model to predict mortality in patients with nocardiosis. The PubMed and Web of Science databases were searched ...

Integrating machine-learning and nanotechnology to quantify pH-modulated oxaliplatin release.

Scientific reports
The purpose of this work was to formulate and characterize pH-sensitive, surfactant-based nanomicelles for the targeted delivery of Oxaliplatin to breast cancer cells. A secondary aim was to utilize machine learning (ML) models to interpolate and dis...

Comparison of Machine Learning Models for Colon Cancer Survival: Predictive Modeling Approach.

JMIR cancer
BACKGROUND: Colon cancer is a leading cause of cancer-related deaths worldwide, with survival influenced by risk factors, treatment type, and patient characteristics. Traditional statistical models, such as Kaplan-Meier curves, have been widely used ...

TLMACEA: design of a transfer learning model for correlative analysis of auscultation and clinical parameters via explainable AI-based recommender.

Biomedical physics & engineering express
Auscultations are commonly used to analyze lung conditions through signal processing and classification techniques. However, the efficiency of these models is often limited by factors like signal quality, sensor performance, and dataset size. Current...

Candidate correlates of protection in the HVTN505 HIV-1 vaccine efficacy trial identified by positive-unlabeled learning.

PLoS computational biology
With a goal of unveiling mechanisms by which vaccines can provide protection against HIV-1 acquisition, several studies have explored correlates of risk of HIV-1 acquisition in HVTN 505, which was a phase IIb trial conducted to assess the safety and ...

Machine learning meets maternal health: Uncovering spatial blind spots in antenatal care quality in Bangladesh.

PloS one
BACKGROUND: High-quality antenatal care (ANC) is defined as four or more antenatal visits with at least one to a medically trained provider, measurement of weight and blood pressure, testing of blood and urine, and receipt of information on potential...

Predicting arthritis risk with machine learning: Insights from the 2023 National Health Interview Survey data.

PloS one
Arthritis, a common chronic disease encompassing multiple subtypes of osteoarthritis and rheumatoid arthritis, was explored in this study as a risk-related factor based on data from the 2023 U.S. National Health Interview Survey (NHIS). The study inc...

Unveiling and interpreting the relationships among multi-pollutant emission factors in municipal solid waste incineration by machine learning.

Waste management (New York, N.Y.)
Effective control of key parameters is critical for regulating pollutant emissions in municipal solid waste incineration (MSWI), but existing research on these parameters remains limited and lacks comprehensiveness. This study used over 600,000 indus...