AIMC Topic: Machine Learning

Clear Filters Showing 28641 to 28650 of 34417 articles

Automatic implicit motive codings are at least as accurate as humans' and 99% faster.

Journal of personality and social psychology
Implicit motives, nonconscious needs that influence individuals' behaviors and shape their emotions, have been part of personality research for nearly a century but differ from personality traits. The implicit motive assessment is very resource-inten...

Exploring multivariate machine learning frameworks to parallelize PM simultaneous estimations across the continental United States.

Environmental pollution (Barking, Essex : 1987)
Fine particulate matter (PM2.5) comprises diverse chemical components, including elemental carbon (EC), silicon (SI), sulfate (SO), and calcium (CA), each linked to varied health and environmental impacts. Accurately estimating these components' spat...

Analysis of protein determinants of genotype-specific properties of group a rotaviruses using machine learning.

Computers in biology and medicine
Group A rotaviruses (RVAs) are the leading cause of viral diarrhoea across various host species, including mammals and birds. The VP7 and VP4 proteins of these viruses play critical roles in determining genotype specificity, influencing viral infecti...

A stacked ensemble approach for symptom-based monkeypox diagnosis.

Computers in biology and medicine
The recent monkeypox outbreak has raised global health concerns. Caused by a virus, it is characterized by symptoms such as skin lesions. Early detection is critical for treatment and controlling its spread. This study uses advanced machine learning ...

Clinician Suicide Risk Assessment for Prediction of Suicide Attempt in a Large Health Care System.

JAMA psychiatry
IMPORTANCE: Clinical practice guidelines recommend suicide risk screening and assessment across behavioral health settings. The predictive accuracy of real-world clinician assessments for stratifying patients by risk of future suicidal behavior, howe...

Reinforcement learning for healthcare operations management: methodological framework, recent developments, and future research directions.

Health care management science
With the advancement in computing power and data science techniques, reinforcement learning (RL) has emerged as a powerful tool for decision-making problems in complex systems. In recent years, the research on RL for healthcare operations has grown r...

Detection of COVID-19, lung opacity, and viral pneumonia via X-ray using machine learning and deep learning.

Computers in biology and medicine
The COVID-19 pandemic has significantly strained healthcare systems, highlighting the need for early diagnosis to isolate positive cases and prevent the spread. This study combines machine learning, deep learning, and transfer learning techniques to ...

Development and External Validation of a Detection Model to Retrospectively Identify Patients With Acute Respiratory Distress Syndrome.

Critical care medicine
OBJECTIVE: The aim of this study was to develop and externally validate a machine-learning model that retrospectively identifies patients with acute respiratory distress syndrome (acute respiratory distress syndrome [ARDS]) using electronic health re...

Advancing low-cost air quality monitor calibration with machine learning methods.

Environmental pollution (Barking, Essex : 1987)
Low-cost monitors for measuring airborne contaminants have gained popularity due to their affordability, portability, and ease of use. However, they often exhibit significant biases compared to high-cost reference instruments. For optimal accuracy, t...

Machine learning-based prediction of hearing loss: Findings of the US NHANES from 2003 to 2018.

Hearing research
The prevalence of hearing loss (HL) has emerged as an escalating public health concern globally. The objective of this study was to leverage data from the National Health and Nutritional Examination Survey (NHANES) to develop an interpretable predict...