AIMC Topic: Machine Learning

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Robust Diagnosis of Acute Bacterial and Viral Infections via Host Gene Expression Rank-Based Ensemble Machine Learning Algorithm: A Multi-Cohort Model Development and Validation Study.

Clinical chemistry
BACKGROUND: The accurate and prompt diagnosis of infections is essential for improving patient outcomes and preventing bacterial drug resistance. Host gene expression profiling as an approach to infection diagnosis holds great potential in assisting ...

Integrating Bioinformatics and Machine Learning to Identify Glucose Metabolism-Related Biomarkers with Diagnostic and Prognostic Value for Patients with Kidney Renal Clear Cell Carcinoma.

Archivos espanoles de urologia
BACKGROUND: Glucose metabolism plays a critical role in the development and progression of kidney renal clear cell carcinoma (KIRC). This study aimed to identify glucose metabolism-related biomarkers (GRBs) and therapeutic targets for KIRC diagnosis ...

Machine Learning in Automated Food Processing: A Mini Review.

Annual review of food science and technology
Industrial food processing is rapidly transforming into automation and digitalization. Automated food processing systems adapt to variations in raw materials and product quality requirements. Implementing automated processing systems can potentially ...

Systems Human Immunology and AI: Immune Setpoint and Immune Health.

Annual review of immunology
The immune system, critical for human health and implicated in many diseases, defends against pathogens, monitors physiological stress, and maintains tissue and organismal homeostasis. It exhibits substantial variability both within and across indivi...

A Machine Learning Approach for Investigating Variable Importance in Relationship and Sexual Satisfaction: The Role of Interpersonal Mindfulness and Psychological Safety.

Journal of marital and family therapy
Numerous studies have shown that mindfulness is positively associated with relationship and sexual satisfaction. However, most have examined the benefits of intrapersonal or trait mindfulness, rather than directly investigating interpersonal mindfuln...

Validation of a Microfluidic Device Prototype for Cancer Detection and Identification: Circulating Tumor Cells Classification Based on Cell Trajectory Analysis Leveraging Cell-Based Modeling and Machine Learning.

International journal for numerical methods in biomedical engineering
Microfluidic devices (MDs) present a novel method for detecting circulating tumor cells (CTCs), enhancing the process through targeted techniques and visual inspection. However, current approaches often yield heterogeneous CTC populations, necessitat...

Exploring the Impact of PA and Sedentary Behavior on Gout Risk in Hyperuricemia: Insights From Machine Learning and SHAP Analysis.

International journal of rheumatic diseases
BACKGROUND: Individuals with hyperuricemia (HUA) are widely recognized as being at increased risk for gout. This study aimed to investigate how physical activity (PA) duration and sedentary duration impact gout risk in individuals with HUA and to dev...

Optimising Nurse-Patient Assignments: The Impact of Machine Learning Model on Care Dynamics-Discursive Paper.

Nursing open
BACKGROUND: Machine learning (ML) models can enhance patient-nurse assignments in healthcare organisations by learning from real data and identifying key capabilities. Nurses must develop innovative ideas for adapting to the dynamic environment, mana...

Using Machine Learning to Predict Uptake to an Online Self-Guided Intervention for Stress During the COVID-19 Pandemic.

Stress and health : journal of the International Society for the Investigation of Stress
Online self-guided interventions appear efficacious for alleviating some mental health concerns. However, among persons who are offered online interventions, only a fraction access them (i.e., achieve uptake). Machine learning methods may be useful t...

Enhanced CT and MRI Focal Bone Tumor Classification with Machine Learning-based Stratification: A Multicenter Retrospective Study.

Radiology
Background Standardized bone tumor reporting is crucial for consistent, risk-aligned patient management. Current systems are based on expert consensus and/or lack multicenter validation. Purpose To evaluate a machine learning-based approach for diffe...