no code implementations • • Veer Sangha, Bobak J. Mortazavi, Adrian D. Haimovich, Antônio H. Ribeiro, Cynthia A. Brandt, Daniel L. Jacoby, Wade L. Schulz, Harlan M. Krumholz, Antonio Luiz P. Ribeiro & Rohan Khera
The application of artificial intelligence (AI) for automated diagnosis of electrocardiograms (ECGs) can improve care in remote settings but is limited by the reliance on infrequently available signal-based data.
no code implementations • 7 Jul 2021 • Irene Li, Jessica Pan, Jeremy Goldwasser, Neha Verma, Wai Pan Wong, Muhammed Yavuz Nuzumlali, Benjamin Rosand, Yixin Li, Matthew Zhang, David Chang, R. Andrew Taylor, Harlan M. Krumholz, Dragomir Radev
Electronic health records (EHRs), digital collections of patient healthcare events and observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and research.
A practical problem with this approach is that the amount of data required to confidently train such a regression model can be prohibitive.
We highlight three primary needs in the design of new smart health technologies: 1) the need for sensing technology that can track longitudinal trends in signs and symptoms of the cardiovascular disorder despite potentially infrequent, noisy, or missing data measurements; 2) the need for new analytic techniques that model data captured in a longitudinal, continual fashion to aid in the development of new risk prediction techniques and in tracking disease progression; and 3) the need for machine learning techniques that are personalized and interpretable, allowing for advancements in shared clinical decision making.
1 code implementation • 14 Aug 2018 • Jacob McPadden, Thomas JS Durant, Dustin R Bunch, Andreas Coppi, Nathan Price, Kris Rodgerson, Charles J Torre Jr, William Byron, H Patrick Young, Allen L Hsiao, Harlan M. Krumholz, Wade L Schulz
This infrastructure also provides a robust analytics platform where healthcare and biomedical research data can be analyzed in near real-time for precision medicine and computational healthcare use cases.
Distributed, Parallel, and Cluster Computing
The TrialChain platform provides a data governance solution to audit the acquisition and analysis of biomedical research data.
Distributed, Parallel, and Cluster Computing Cryptography and Security
We address the problem of defining a network graph on a large collection of classes.
In this paper, we build an organization of high-dimensional datasets that cannot be cleanly embedded into a low-dimensional representation due to missing entries and a subset of the features being irrelevant to modeling functions of interest.