AIMC Topic: Bayes Theorem
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An interpretable machine learning approach for detecting psoriatic arthritis in a UK primary care psoriasis cohort using electronic health records from the Clinical Practice Research Datalink.
Annals of the rheumatic diseases
Mar 1, 2025
OBJECTIVES: Develop an interpretable machine learning model to detect patients with newly diagnosed psoriatic arthritis (PsA) in a cohort of psoriasis patients and identify important clinical indicators of PsA in primary care.
Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models.
BMC pregnancy and childbirth
Feb 28, 2025
OBJECTIVE: To determine whether readily available patient, ultrasound and treatment outcome data can be used to develop, validate and externally test two machine learning (ML) models for predicting the success of expectant and medical management of m...
Urban and rural disparities in stroke prediction using machine learning among Chinese older adults.
Scientific reports
Feb 25, 2025
Stroke is a significant health concern in China. Differences in stroke risk between rural and urban areas have been highlighted in prior research. However, there is a scarcity of studies on urban-rural differences in predicting stroke. This study aim...
Development of the autonomous lab system to support biotechnology research.
Scientific reports
Feb 24, 2025
In this study, we developed the autonomous lab (ANL), which is a system based on robotics and artificial intelligence (AI) to conduct biotechnology experiments and formulate scientific hypotheses. This system was designed with modular devices and Bay...
Enhancing readmission prediction model in older stroke patients by integrating insight from readiness for hospital discharge: Prospective cohort study.
International journal of medical informatics
Feb 22, 2025
BACKGROUND: The 30-day hospital readmission rate is a key indicator of healthcare quality and system efficiency. This study aimed to develop machine-learning (ML) models to predict unplanned 30-day readmissions in older patients with ischemic stroke ...
Comparison of different dental age estimation methods with deep learning: Willems, Cameriere-European, London Atlas.
International journal of legal medicine
Feb 19, 2025
This study aimed to compare dental age estimates using Willems, Cameriere-Europe, London Atlas, and deep learning methods on panoramic radiographs of Turkish children. The dental ages of 1169 children (613 girls, 556 boys) who agreed to participate i...
Identifying plastic materials in post-consumer food containers and packaging waste using terahertz spectroscopy and machine learning.
Waste management (New York, N.Y.)
Feb 18, 2025
Accurate identification of plastic materials from post-consumer food container and packaging waste is crucial for enhancing the purity and added value of recycled materials, thereby promoting recycling and addressing the issue of plastic pollution. H...
Development and validation of interpretable machine learning models for triage patients admitted to the intensive care unit.
PloS one
Feb 18, 2025
OBJECTIVES: Developing and validating interpretable machine learning (ML) models for predicting whether triaged patients need to be admitted to the intensive care unit (ICU).
Development and clinical evaluation of an AI-assisted respiratory state classification system for chest X-rays: A BMI-Specific approach.
Computers in biology and medicine
Feb 16, 2025
PURPOSE: In this study, we aimed to develop and clinically evaluate an artificial intelligence (AI)-assisted support system for determining inhalation and exhalation states on chest X-ray images, focusing on the specific challenge of respiratory stat...
Advancing Emotionally Aware Child-Robot Interaction with Biophysical Data and Insight-Driven Affective Computing.
Sensors (Basel, Switzerland)
Feb 14, 2025
This paper investigates the integration of affective computing techniques using biophysical data to advance emotionally aware machines and enhance child-robot interaction (CRI). By leveraging interdisciplinary insights from neuroscience, psychology, ...
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