… Machine learning has become a key driver of the digital health revolution. That comes with
a fair share of high hopes and hype. We conducted a scoping review on machine learning in …

… with standard machine learning (… machine learning for investigation of neurological and
psychiatric disorders has grown greatly in the last two decades 1,2 . Standard machine learning (…

… power led to coin the term machine learning, which is the robust … One of the branches of
machine learning is deep learning which … both machine learning and deep learning (see Fig. 1). …

… Machine learning approaches can be used to develop risk scores that are superior to ones
based on standard statistical methods. Careful attention to detail in curating data, selecting …

… We aimed to compare machine learning (ML) models predicting the hospitalization of ED
patients and conventional regression techniques at three points in time after ED registration. …

… our ongoing development of predictive machine learning algorithms. At this … HOPES
Platform and Its First Use in the HOPE-S Study, we describe the overall architecture of the HOPES …

… , with the integration of machine learning and nanotechnology … potential of machine learning
and nanotechnologybased … Machine learning techniques offer the ability to analyze large …

… the challenges of classifying hope and the limitations of the existing hope speech detection
corpora. … based on different learning approaches, such as traditional machine learning, deep …

… learning be similarly transformative for other scientific problems? After a brief review of some
initial applications of machine learning … some realistic machine learning benchmarks using …

… In the present study, we tested whether a supervised machine learning pipeline that combined
hyperparameter tuning and RFE in a repeated nested CV setup can lead to sparser but …