AIMC Topic: Machine Learning

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A machine learning model for predicting 28-day mortality in ICU patients with community-acquired pneumonia and acute kidney injury.

Scientific reports
Acute kidney injury is a common and critical complication in patients with community-acquired pneumonia who are admitted to intensive care units, substantially increasing their risk of short-term mortality. To enhance early clinical decision-making, ...

Similarity as likelihood ratio: Coupling representations from machine learning (and other sources) with cognitive models.

Psychonomic bulletin & review
Similarity lies at the core of theories of memory and perception. To understand similarity relations among complex items like text and images, researchers often rely on machine learning to derive high-dimensional vector representations of those items...

Adoption of Machine Learning in US Hospital Electronic Health Record Systems: Retrospective Observational Study.

Journal of medical Internet research
BACKGROUND: While machine learning (ML) technologies have shifted from development to real-world deployment over the past decade, US health care providers and hospital administrators have increasingly embraced ML, particularly through its integration...

Development and validation of a plasma-urine metabolism diagnostic model for renal cell carcinoma using machine learning.

World journal of urology
BACKGROUND: Renal cell carcinoma (RCC), which accounts for 70-90% of kidney malignancies, remains difficult to diagnose early due to its asymptomatic onset and the lack of reliable biomarkers. This study aimed to develop a robust diagnostic model by ...

Machine learning-integrated electrochemical sensing of ciprofloxacin for digital point-of-care therapeutic drug monitoring.

Mikrochimica acta
Timely and precise therapeutic drug monitoring (TDM) is critical for managing pharmacokinetic variability and optimizing individualized therapy, particularly during public health crises such as the COVID-19 pandemic. Herein, we optimized integrated m...

Accuracy of AI-based binary classification for detecting malocclusion in the mixed dentition stage.

PloS one
BACKGROUND: Malocclusion is a common anomaly and is frequently observed in children and adults. Early detection and treatment of malocclusion is necessary to prevent and minimize complications. Therefore, developing a tool to check dentition at an ea...

Identifying influential determinants of women's empowerment in Bangladesh using machine learning algorithms.

PloS one
BACKGROUND AND OBJECTIVES: Women's empowerment is a vital issue in lower-middle-income developing countries like Bangladesh, where it plays a pivotal role in advancing development across the nation. Thus, this study aimed to identify the influential ...

Current state of machine learning implementation in pharmaceutical process modeling for oral solid dosage forms.

International journal of pharmaceutics
Driven by the Food and Drug Administration's Quality-by-Design initiative and the advancements of Industry 4.0, the pharmaceutical industry is transitioning from traditional batch manufacturing to advanced manufacturing. This transition requires resh...

AttMVGraph: Attention-Based Multimodal Fusion and Variational Graph Learning for SM-miRNA Association Prediction.

Journal of chemical information and modeling
MiRNA serves as a key noncoding RNA regulating gene expression and is frequently targeted as a therapeutic small molecule (SM). However, relying solely on the experimental identification of SM-miRNA interactions proves costly and inefficient. To addr...

ProfhEX: Empowering Early Drug Discovery with Machine Learning-Based Target Profiling and Liability Prediction.

Journal of chemical information and modeling
The drug discovery process is inherently lengthy, complex, and costly, with high attrition rates driven by safety concerns, limited efficacy, and regulatory barriers. AI-driven computational methods have become crucial in accelerating this process by...