AIMC Topic: Machine Learning

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Two-step beam geometry optimization for volumetric modulated arc therapy gantry angles in breast treatments.

Medical physics
BACKGROUND: In partial arc volumetric modulated arc therapy (VMAT) for treating breast cancer, setting up the limiting gantry positions of the treatment machine is a nontrivial yet repetitive and time-consuming task during planning. Templatized solut...

A sustainable system for predicting appliance energy consumption based on machine learning.

Journal of environmental management
Nowadays, energy conservation is a top priority for sustainable societies, which focus on environmental and economic sustainability to address fossil fuel scarcity, climate change, and increasing environmental pollution. Therefore, planning for energ...

Assessing the impacts of cascade reservoirs on Pearl River environmental status using machine learning and satellite-derived chlorophyll-a concentrations.

Journal of environmental management
Rivers play a crucial role in in global matter cycling and energy flow, contributing significantly to biogeochemical cycles and the development of human civilization. Reservoirs, as prevalent artificial water bodies, modify river flow and impact ener...

Machine Learning Models for Predicting Type 2 Diabetes Complications in Malaysia.

Asia-Pacific journal of public health
This study aimed to develop machine learning (ML) models to predict diabetic complications in patients with type 2 diabetes (T2D) in Malaysia. Data from the Malaysian National Diabetes Registry and Death Register were used to develop predictive model...

AI-assisted identification of disability patterns within identical EDSS grades.

Multiple sclerosis (Houndmills, Basingstoke, England)
BACKGROUND: The Neurostatus-Expanded Disability Status Scale (EDSS) is the most frequently used measure of disability in multiple sclerosis (MS) trials. However, EDSS scores ⩾4.5 are mainly based on ambulation and may fail to capture relevant disabil...

Integrating Machine Learning and Follow-Up Variables to Improve Early Detection of Hepatocellular Carcinoma in Tyrosinemia Type 1: A Multicenter Study.

International journal of molecular sciences
Hepatocellular carcinoma (HCC) is a major complication of tyrosinemia type 1 (HT-1), an inborn error of metabolism affecting tyrosine catabolism. The risk of HCC is higher in late diagnoses despite treatment. Alpha-fetoprotein (AFP) is widely used to...

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model.

Journal of visualized experiments : JoVE
Lymph node status is a critical prognostic predictor for patients; however, the prognosis of colorectal signet-ring cell carcinoma (SRCC) has garnered limited attention. This study investigates the prognostic predictive capacity of the log odds of po...

Machine learning prediction of HER2-low expression in breast cancers based on hematoxylin-eosin-stained slides.

Breast cancer research : BCR
BACKGROUND: Treatment with HER2-targeted therapies is recommended for HER2-positive breast cancer patients with HER2 gene amplification or protein overexpression. Interestingly, recent clinical trials of novel HER2-targeted therapies demonstrated pro...

MALDI-TOF mass spectrometry combined with machine learning algorithms to identify protein profiles related to malaria infection in human sera from Côte d'Ivoire.

Malaria journal
BACKGROUND: In sub-Saharan Africa, Plasmodium falciparum is the most prevalent species of malaria parasites. In endemic areas, malaria is mainly diagnosed using microscopy or rapid diagnostic tests (RDTs), which have limited sensitivity, and microsco...