AIMC Topic: Machine Learning

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Machine learning evaluation of inequities and disparities associated with nurse sensitive indicator safety events.

Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
PURPOSE: To use machine learning to examine health equity and clinical outcomes in patients who experienced a nurse sensitive indicator (NSI) event, defined as a fall, a hospital-acquired pressure injury (HAPI) or a hospital-acquired infection (HAI).

Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence).

Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences
BACKGROUND: Treatment management for opioid poisoning is critical and, at the same time, requires specialized knowledge and skills. This study was designed to develop and evaluate machine learning algorithms for predicting the maintenance dose and du...

DualFluidNet: An attention-based dual-pipeline network for fluid simulation.

Neural networks : the official journal of the International Neural Network Society
Fluid motion can be considered as a point cloud transformation when using the SPH method. Compared to traditional numerical analysis methods, using machine learning techniques to learn physics simulations can achieve near-accurate results, while sign...

Hyperspectral imaging with machine learning for in vivo skin carcinoma margin assessment: a preliminary study.

Physical and engineering sciences in medicine
Surgical excision is the most effective treatment of skin carcinomas (basal cell carcinoma or squamous cell carcinoma). Preoperative assessment of tumoral margins plays a decisive role for a successful result. The aim of this work was to evaluate the...

A novel machine learning model for efficacy prediction of immunotherapy-chemotherapy in NSCLC based on CT radiomics.

Computers in biology and medicine
Lung cancer is categorized into two main types: non-small cell lung cancer (NSCLC) and small cell lung cancer. Of these, NSCLC accounts for approximately 85% of all cases and encompasses varieties such as squamous cell carcinoma and adenocarcinoma. F...

Ancestry analysis using a self-developed 56 AIM-InDel loci and machine learning methods.

Forensic science international
Insertion/deletion (InDel) polymorphisms can be used as one of the ancestry-informative markers in ancestry analysis. In this study, a self-developed panel consisting of 56 ancestry-informative InDels was used to investigate the genetic structures an...

Identifying influence factors and thresholds of the next day's pollen concentration in different seasons using interpretable machine learning.

The Science of the total environment
The prevalence of pollen allergies is a pressing global issue, with projections suggesting that half of the world's population will be affected by 2050 according to the estimation of the World Health Organization (WHO). Accurately forecasting pollen ...

Hepatic toxicity prediction of bisphenol analogs by machine learning strategy.

The Science of the total environment
Toxicological studies have demonstrated the hepatic toxicity of several bisphenol analogs (BPs), a prevalent type of endocrine disruptor. The development of Adverse Outcome Pathway (AOP) has substantially contributed to the rapid risk assessment for ...

Smartphone based wearable sweat glucose sensing device correlated with machine learning for real-time diabetes screening.

Analytica chimica acta
BACKGROUND: Diabetes is a significant health threat, with its prevalence and burden increasing worldwide indicating its challenge for global healthcare management. To decrease the disease severity, the diabetic patients are recommended to regularly c...

Predicting Schistosomiasis Intensity in Africa: A Machine Learning Approach to Evaluate the Progress of WHO Roadmap 2030.

The American journal of tropical medicine and hygiene
The World Health Organization (WHO) 2030 Roadmap aims to eliminate schistosomiasis as a public health issue, targeting reductions in the heavy intensity of infections. Previous studies, however, have predominantly used prevalence as the primary indic...