AIMC Topic: Machine Learning

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StackIL10: A stacking ensemble model for the improved prediction of IL-10 inducing peptides.

PloS one
Interleukin-10, a highly effective cytokine recognized for its anti-inflammatory properties, plays a critical role in the immune system. In addition to its well-documented capacity to mitigate inflammation, IL-10 can unexpectedly demonstrate pro-infl...

Comparing machine learning approaches for estimating soil saturated hydraulic conductivity.

PloS one
Characterization of near (field) saturated hydraulic conductivity (Kfs) of the soil environment is among the crucial components of hydrological modeling frameworks. Since the associated laboratory/field experiments are time-consuming and labor-intens...

Advancing ensemble learning techniques for residential building electricity consumption forecasting: Insight from explainable artificial intelligence.

PloS one
Accurate electricity consumption forecasting in residential buildings has a direct impact on energy efficiency and cost management, making it a critical component of sustainable energy practices. Decision tree-based ensemble learning techniques are p...

A comprehensive overview of acupuncture therapy over the past 20 years: Machine learning-based bibliometric analysis.

Complementary therapies in medicine
BACKGROUND: Acupuncture, a therapy created by the ancient Chinese, has been gaining increasing popularity and acceptance worldwid. The surge in the number of publications on acupuncture therapy has posed significant challenges for researchers in effe...

Why we should talk about institutional (dis)trustworthiness and medical machine learning.

Medicine, health care, and philosophy
The principle of trust has been placed at the centre as an attitude for engaging with clinical machine learning systems. However, the notions of trust and distrust remain fiercely debated in the philosophical and ethical literature. In this article, ...

Microscopic water dispersion and hydrogen-bonding structures in margarine spreads with Raman hyperspectral imaging and machine learning.

Food chemistry
Margarine, a water-in-oil (W/O) emulsion, offers advantages such as lower costs in comparison to similar products, but large amounts of saturated fats pose health risks. Reduction of saturated fat content is difficult and often leads to "oil-off," i....

Applications and potential of machine, learning augmented chest X-ray interpretation in cardiology.

Minerva cardiology and angiology
The chest X-ray (CXR) has a wide range of clinical indications in the field of cardiology, from the assessment of acute pathology to disease surveillance and screening. Despite many technological advancements, CXR interpretation error rates have rema...

A comprehensive study on tuberculosis prediction models: Integrating machine learning into epidemiological analysis.

Journal of theoretical biology
Tuberculosis (TB), the second leading infectious killer globally, claimed the lives of 1.3 million individuals in 2022, after COVID-19, surpassing the toll of HIV and AIDS. With an estimated 10.6 million new TB cases worldwide in 2022, the gravity of...

A machine learning model for predicting worsening renal function using one-year time series data in patients with type 2 diabetes.

Journal of diabetes investigation
BACKGROUND AND AIMS: To prevent end-stage renal disease caused by diabetic kidney disease, we created a predictive model for high-risk patients using machine learning.

The Value of Machine Learning Models in Predicting Factors Associated with the Need for Permanent Shunting in Patients with Intracerebral Hemorrhage Requiring Emergency Cerebrospinal Fluid Diversion.

World neurosurgery
OBJECTIVE: To assess the efficacy of machine learning models in identifying factors associated with the need for permanent ventricular shunt placement in patients experiencing intracerebral hemorrhage (ICH) who require emergency cerebrospinal fluid (...