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Deepta Rajan
IBM
14Publications
4H-index
61Citations
Publications 14
Newest
Published on Dec 30, 2018
Deepta Rajan4
Estimated H-index: 4
(IBM),
David Beymer21
Estimated H-index: 21
(IBM),
Girish Narayan
Source Cite
Published on Jan 1, 2018in arXiv: Machine Learning
Jayaraman J. Thiagarajan10
Estimated H-index: 10
,
Deepta Rajan4
Estimated H-index: 4
,
Prasanna Sattigeri8
Estimated H-index: 8
The hypothesis that computational models can be reliable enough to be adopted in prognosis and patient care is revolutionizing healthcare. Deep learning, in particular, has been a game changer in building predictive models, thereby leading to community-wide data curation efforts. However, due to the inherent variabilities in population characteristics and biological systems, these models are often biased to the training datasets. This can be limiting when models are deployed in new environments,...
Published on Oct 17, 2016 in MICCAI (Medical Image Computing and Computer-Assisted Intervention)
Tanveer Fathima Syeda-Mahmood18
Estimated H-index: 18
(IBM),
Yanrong Guo1
Estimated H-index: 1
(IBM)
+ 5 AuthorsMohammadreza Negahdar4
Estimated H-index: 4
(IBM)
In this paper we present a new method of uncovering patients with aortic valve diseases in large electronic health record systems through learning with multimodal data. The method automatically extracts clinically-relevant valvular disease features from five multimodal sources of information including structured diagnosis, echocardiogram reports, and echocardiogram imaging studies. It combines these partial evidence features in a random forests learning framework to predict patients likely to ha...
4 Citations Source Cite
Published on Mar 24, 2016in Proceedings of SPIE
Tanveer Fathima Syeda-Mahmood18
Estimated H-index: 18
(IBM),
Eugene Walach8
Estimated H-index: 8
(IBM)
+ 20 AuthorsR. Negahdar1
Estimated H-index: 1
(IBM)
Radiologists and cardiologists today have to view large amounts of imaging data relatively quickly leading to eye fatigue. Further, they have only limited access to clinical information relying mostly on their visual interpretation of imaging studies for their diagnostic decisions. In this paper, we present Medical Sieve, an automated cognitive assistant for radiologists and cardiologists designed to help in their clinical decision-making. The sieve is a clinical informatics system that collects...
2 Citations Source Cite
Published on Oct 1, 2014
Mahesh K. Banavar12
Estimated H-index: 12
(ASU: Arizona State University),
Deepta Rajan4
Estimated H-index: 4
(ASU: Arizona State University)
+ 4 AuthorsAndreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
The objective of this project is to develop and design mobile content for introducing engineering technology to high school students. More specifically, we intend to work on a sequence of modules that will establish connections between high school mathematics and physics to modern technologies associated with smart phones, iPods and other high-tech products. The participants of the project will use the previously developed AJDSP (for Android devices) and iJDSP (for iPhones and iPads) apps to fac...
5 Citations Source Cite
Published on Oct 1, 2013
Deepta Rajan4
Estimated H-index: 4
(ASU: Arizona State University),
Andreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
+ 2 AuthorsPhotini Spanias3
Estimated H-index: 3
(ASU: Arizona State University)
The recent sensing capabilities of mobile devices along with their interactivity and popularity in the student community can be used to create a unique learning environment in engineering education. Android Java-DSP (AJDSP) is a mobile educational application that interfaces with sensors and enables simulation and visualization of signal processing concepts. In this paper, we present the work done towards building non-invasive physiological signal monitoring tools in AJDSP through hardware inter...
11 Citations Source Cite
Published on Aug 1, 2013
Deepta Rajan4
Estimated H-index: 4
(ASU: Arizona State University),
Girish Kalyanasundaram1
Estimated H-index: 1
(ASU: Arizona State University)
+ 2 AuthorsAndreas Spanias25
Estimated H-index: 25
(ASU: Arizona State University)
By exploiting the interactivity and processing power of mobile technologies, an immersive learning experience can be created. iJDSP and AJDSP are mobile graphical programming applications for simulation and visualization of signal processing concepts, developed to complement instruction to students from the STEM fields. In this paper, the enhanced sensing capabilities of modern mobile devices have been explored by developing DSP tools that allow interfaces to on-board and external sensors. An im...
3 Citations Source Cite
Published on Jun 23, 2013
Andreas Spanias25
Estimated H-index: 25
,
Jayaraman J. Thiagarajan10
Estimated H-index: 10
(ASU: Arizona State University)
+ 5 AuthorsGirish Kalyanasundaram1
Estimated H-index: 1
(ASU: Arizona State University)
Internet and multimedia technologies have had a profound impact in STEM education in the past decade. The increase in the use of mobile devices among students adds another novel dimension to course design and delivery. Furthermore, the traditional textbooks are being replaced and supplemented by inexpensive and mobile e-books, and hence there is an opportunity to integrate the multimedia and mobile technologies in an e-learning environment. As a part of the NSF TUES phase 3 project for developme...
12