Multimodal tensor-based method for integrative and continuous patient monitoring during postoperative cardiac care.

TL;DR: In this article , the authors provide a general overview of advanced large-scale AI models, including language models, vision-language models, graph learning models, language-conditioned multiagent models, and multimodal embodied models.

TL;DR: In this paper , a review of the technical aspects of multimodal machine learning (ML) are discussed, including a definition of the technology and its technical underpinnings, especially data fusion, and a number of the most common problems hindering the development of ML and potential solutions that could be pursued in future studies are outlined.

TL;DR: In this article , a machine learning-based prediction model was proposed to predict postoperative hemodynamic deterioration among cardiac surgical patients, defined as a composite of low cardiac index, new inotrope/vasopressor infusion, epinephrine bolus of 1 mg or more, and sustained hypotension.

TL;DR: In this article , the authors present a review of the state-of-the-art dynamic prediction models of patient outcomes developed for use in the ICU, defined as those where predictions are regularly computed and updated over time in response to updated physiological signals.

TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

TL;DR: A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.

TL;DR: This paper is concerned with the construction of planes of closest fit to systems of points in space and the relationships between these planes and the planes themselves.

TL;DR: This survey provides an overview of higher-order tensor decompositions, their applications, and available software.