A Deep Learning-Based Decision Support Tool for Precision Risk Assessment of Breast Cancer.

TL;DR: An automated clinical trial matching system that uses natural language processing to extract patient and trial characteristics from unstructured sources and machine learning to match patients to clinical trials displayed a promising performance in screening patients with breast cancer for trial eligibility.

TL;DR: In this article, the diagnostic accuracy of digital mammography screening (DM) compared to screen-film mammography (SFM) in the whole Dutch screening programme, in the period of 2004-2010, during which a full transition from SFM to DM was made.

TL;DR: A 10-question assessment tool was applied to commercial and open-source algorithms used for diagnosis to extract evidence on the clinical utility of AI tools and reveals a broad spectrum of maturity and clinical use of AI products.

TL;DR: Natural language processing with machine learning improved the ability to predict postoperative complication and hospital readmission among women with ovarian cancer undergoing surgery.

TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

TL;DR: This paper explores the use of Support Vector Machines for learning text classifiers from examples and analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task.

TL;DR: This work introduces and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern.

TL;DR: The motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer modelssuch as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks are discussed.