Interpretable Machine Learning Models for Assisting Clinicians in the Analysis of Physiological Data

A total of 7 years later, the Atmospheric Chemistry and Climate Model Intercom- parison Project (ACCMIP) generated both historical (Naik et al., 2013) and future (Voulgarakis et

Abstract. Real data from manufacturing processes are essential to create useful insights for decision-making. However, acquiring real manufacturing data can be expensive

Background and Objectives: This paper presents the results of a Machine- Learning based Model Order Reduction (MOR) method applied to a complex 3D Finite Element (FE)

We present a radiomics based model, interpretable for each patient, trained on an American multicentric cohort that yielded a 92% predictive value for relapse at 18 months

We then present the main characteristics of our temporal model, introducing the concept of coherence zone, and how this one can be used to represent tense information in

In the case of E-STIT, the specification regarding vocabulary and theory of a knowledge base system contains the informa- tion about the number of agents and worlds, the morality

To obtain news for the study, a public data set located in a github repository was used https://github.com/GeorgeMcIntire/fake_real_news_dataset compiled in equal parts for

Comparing the different canonical and empirical propagation models (lines) against the measurements gathered on the 2218 wire- less links of the ARHO networks (cloudpoint)..