In this article, the proposed method develops a big data classification model with the aid of intelligent techniques. Here, the Parallel Pool Map reduce Framework is used for handling big data. The model involves three main phases, namely (1) feature...
BACKGROUND: Classification algorithms assign observations to groups based on patterns in data. The machine-learning community have developed myriad classification algorithms, which are used in diverse life science research domains. Algorithm choice c...
OBJECTIVES: As the performance of a conventional track and trigger system in a rapid response system has been unsatisfactory, we developed and implemented an artificial intelligence for predicting in-hospital cardiac arrest, denoted the deep learning...
Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft
Apr 1, 2020
BACKGROUND: Procedures with artificial intelligence (AI), such as deep neural networks, show promising results in automatic analysis of ophthalmological imaging data.
IMPORTANCE: Conventional segmentation of the retinal nerve fiber layer (RNFL) is prone to errors that may affect the accuracy of spectral-domain optical coherence tomography (SD-OCT) scans in detecting glaucomatous damage.
Journal of the American Medical Informatics Association : JAMIA
Apr 1, 2020
INTRODUCTION: Classifying whether concepts in an unstructured clinical text are negated is an important unsolved task. New domain adaptation and transfer learning methods can potentially address this issue.
Journal of the American Medical Informatics Association : JAMIA
Apr 1, 2020
OBJECTIVE: Depression is currently the second most significant contributor to non-fatal disease burdens globally. While it is treatable, depression remains undiagnosed in many cases. As mobile phones have now become an integral part of daily life, th...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.