SciELO

Predicting the Need for Blood Transfusions in Cardiac Surgery: A Comparison between Machine Learning Algorithms and Established Risk Scores in the Brazilian Population

Introduction: Blood transfusion is a common practice in cardiac surgery, despite its well-known negative effects. To mitigate blood transfusion-associated risks, identifying patients who are at higher ...
The Lancet

A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis

fNIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK ...
C&EN

Machine Learning Predictive Models, Interpretations, and Applications of Metal-Based Catalysts, UV, and Thermal Activation Persulfate-Based Advanced Oxidation Processes

School of Civil, Environmental, and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea ...
Europe PMC

Machine learning for Parkinson's disease: a comprehensive review of datasets, algorithms, and challenges.

Parkinson's disease (PD) is a devastating neurological ailment affecting both mobility and cognitive function, posing considerable problems to the health of the elderly across the world. The absence ...
GitHub

ML-STIM: Machine Learning for SubThalamic nucleus Intraoperative Mapping

Deep Brain Stimulation (DBS) of the SubThalamic Nucleus (STN) is an effective electroceutical therapy for treating motor symptoms in patients with Parkinson’s Disease (PD). Accurate placement of the ...
Frontiers

A comparative analysis of binary and multi-class classification machine learning algorithms to detect current frailty status using the English longitudinal study of ageing (ELSA)

Physical frailty is a pressing public health issue that significantly increases the risk of disability, hospitalization, and mortality. Early and accurate detection of frailty is essential for timely ...
www.cs.cmu.edu

John Wieting

I am a PhD student at Carnegie Mellon University supervised by Taylor Berg-Kirkpatrick and Graham Neubig. I also collaborate with Kevin Gimpel at the Toyota Technological Institute at the University ...
C&EN

Identification of Optimal Machine Learning Algorithms and Molecular Fingerprints for Explainable Toxicity Prediction Models Using ToxCast/Tox21 Bioassay Data

School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea ...