AIMC Topic: Machine Learning

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Enhancing the diagnostic accuracy of colorectal cancer through the integration of serum tumor markers and hematological indicators with machine learning algorithms.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
BACKGROUND: Colorectal cancer has a high incidence and mortality rate due to a low rate of early diagnosis. Therefore, efficient diagnostic methods are urgently needed.

Development and validation of machine learning models to predict perioperative transfusion risk for hip fractures in the elderly.

Annals of medicine
BACKGROUND: Patients with hip fractures frequently need to receive perioperative transfusions of concentrated red blood cells due to preoperative anemia or surgical blood loss. However, the use of perioperative blood products increases the risk of ad...

Predicting State-Level Firearm Suicide Rates: A Machine Learning Approach Using Public Policy Data.

American journal of preventive medicine
INTRODUCTION: Over 40,000 people die by suicide annually in the U.S., and firearms are the most lethal suicide method. There is limited evidence on the effectiveness of many state-level policies on reducing firearm suicide. The objective of this stud...

Predicting daily recovery during long-term endurance training using machine learning analysis.

European journal of applied physiology
PURPOSE: The aim of this study was to determine if machine learning models could predict the perceived morning recovery status (AM PRS) and daily change in heart rate variability (HRV change) of endurance athletes based on training, dietary intake, s...

SAnDReS 2.0: Development of machine-learning models to explore the scoring function space.

Journal of computational chemistry
Classical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein-ligand structures and affinity data make it possible to develop machine-learning models focused on specific pro...

Machine learning-assisted matrix-assisted laser desorption/ionization time-of-flight mass spectrometry toward rapid classification of milk products.

Journal of dairy science
This study established a method for rapid classification of milk products by combining MALDI-TOF MS analysis with machine learning techniques. The analysis of 2 different types of milk products was used as an example. To select key variables as poten...

Automated machine learning-aided prediction and interpretation of gaseous by-products from the hydrothermal liquefaction of biomass.

The Science of the total environment
Hydrothermal liquefaction (HTL) is a thermochemical conversion technology that produces bio-oil from wet biomass without drying. However, by-product gases will inevitably be produced, and their formation is unclear. Therefore, an automated machine le...

A machine learning model to predict spontaneous vaginal delivery failure for term nulliparous women: An observational study.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
OBJECTIVE: This study aims to construct and evaluate a model to predict spontaneous vaginal delivery (SVD) failure in term nulliparous women based on machine learning algorithms.

From plan to delivery: Machine learning based positional accuracy prediction of multi-leaf collimator and estimation of delivery effect in volumetric modulated arc therapy.

Journal of applied clinical medical physics
PURPOSE: The positional accuracy of MLC is an important element in establishing the exact dosimetry in VMAT. We comprehensively analyzed factors that may affect MLC positional accuracy in VMAT, and constructed a model to predict MLC positional deviat...