AIMC Topic: Machine Learning

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Automatic quantification of disgust reactions in mice using machine learning.

Scientific reports
Disgust, a primary negative emotion, plays a vital role in protecting organisms from intoxication and infection. In rodents, this emotion has been quantified by measuring the specific reactions elicited by exposure to unpleasant tastes. These reactio...

Forecasting Surgical Bed Utilization: Architectural Design of a Machine Learning Pipeline Incorporating Predicted Length of Stay and Surgical Volume.

Journal of medical systems
The objective of this study was to develop a machine learning model utilizing data from the electronic health record (EHR) to model length of stay and daily surgical volume, in order to subsequently predict daily surgical inpatient bed utilization. M...

Integrating machine learning and traditional methods for cadmium prediction and bioavailability assessment in Paeoniae Radix Alba: a case study from Bozhou, Anhui Province.

Environmental geochemistry and health
Soil heavy metal contamination, particularly cadmium (Cd), poses a significant risk to ecosystems and human health. This study investigates the distribution and bioavailability of Cd in soil and Paeoniae Radix Alba system from Qiaocheng District, Boz...

Decoding the role of the arginine dihydrolase pathway in shaping human gut community assembly and health-relevant metabolites.

Cell systems
The arginine dihydrolase pathway (arc operon) provides a metabolic niche by transforming arginine into metabolic byproducts. We investigate the role of the arc operon in probiotic Escherichia coli Nissle 1917 on human gut community assembly and healt...

Machine learning-based label-free macrophage phenotyping in immune-material interactions.

Journal of materials chemistry. B
The rapid advancement of implantable biomedical materials necessitates a comprehensive understanding of macrophage interactions to optimize implant immunocompatibility. Macrophages, key immune regulators, exhibit phenotypic plasticity by polarizing i...

Machine learning-assisted literature screening for a medication-use process-related systematic review.

American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists
PURPOSE: This article summarizes a novel methodology of applying machine learning (ML) algorithms trained with external training data to assist with article screening for 2 annual review series related to the medication-use process (MUP) generally an...

Posture analysis in predicting fall-related injuries during French Navy Special Forces selection course using machine learning: a proof-of-concept study.

BMJ military health
INTRODUCTION: Injuries induced by falls represent the main cause of failure in the French Navy Special Forces selection course. In the present study, we made the assumption that probing the posture might contribute to predicting the risk of fall-rela...

[Development and validation of risk assessment models for abnormal lung function in coal workers based on machine learning].

Zhonghua lao dong wei sheng zhi ye bing za zhi = Zhonghua laodong weisheng zhiyebing zazhi = Chinese journal of industrial hygiene and occupational diseases
To analyze the factors influencing the lung function of coal miners, identify the optimal combination of indicators for evaluating lung function, develop a risk assessment model using machine learning, and offer personalized risk assessment for work...

Driver Steering Intention Prediction for Human-Machine Shared Systems of Intelligent Vehicles Based on CNN-GRU Network.

Sensors (Basel, Switzerland)
In order to mitigate human-machine conflicts and optimize shared control strategy in advance, it is essential for the shared control system to understand and predict driver behavior. This paper proposes a method for predicting driver steering intenti...

Evaluating Prevalence of Preterm Postnatal Growth Faltering Using Fenton 2013 and INTERGROWTH-21st Growth Charts with Logistic and Machine Learning Models.

Nutrients
Postnatal growth faltering (PGF) significantly affects premature neonates, leading to compromised neurodevelopment and an increased risk of long-term health complications. This retrospective study at a level III NICU of a tertiary hospital analyzed...