… XGBoost also performed well for critical event prediction, … were the strongest drivers of critical
event prediction, while higher … machine learning models for mortality and critical events for …

… of censoring by limiting exploration of only these time frames, particularly in the case of
model enrichment for acute critical events, we also predicted critical events at days 15 and 20. …

… , we design a machine learning approach to compute optimal precursors to extreme events
(“… Our first aim was to understand critical limitations of binary classification methods in the …

… of analysis is Machine Learning, described by Lantz … of machine learning is applied towards
the isolation of critical parameters (and potential causal factors) driving safety-related events …

We propose a psychological need detection framework which automatically identifies people
needs and measures their satisfaction level. The framework employs three need models, …

… re-using the standard clinical databases for event prediction or decision support, to including
… and preprocessing of critical care data. The three challenges in critical care are considered: …

… critical water infrastructure in the IIoT infrastructure. The study uses various machine learning
algorithms to classify the anomaly events … activity, various machine learning methods, such …

… A critical review of literature on hazardous events and supervised machine learning …
aided in detection or estimation by the machine learning algorithms. An introduction to deep …

… biomimetical (bio-inspired) machine learning algorithms to predict the vulnerability and …
loading event (such as fire, flood, earthquake, etc.). These algorithms comprise deep learning, …

… As a rule, machine learning algorithms cannot separate informative … critical for such type of
algorithms. One of the elements of preliminary data processing is the identification of events in …