Background: As genome sequencing becomes better integrated into scientific research, government policy, and personalized medicine, the primary challenge for researchers is shifting from generating raw data to analyzing these vast datasets. Although much work has been done to reduce compute times using various configurations of traditional CPU computing infrastructures, Graphics Processing Units (GPUs) offer opportunities to accelerate genomic workflows by orders of magnitude. Here we benchmark one GPU-accelerated software suite called NVIDIA Parabricks on Amazon Web Services (AWS), Google Cloud Platform (GCP), and an NVIDIA DGX cluster.
View Article and Find Full Text PDFWith the enactment of the Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009, hospitals and physician practices across the country converted from a system of paper recordkeeping to fully integrated electronic health records (EHR)., With financial incentives in hand, there was a rush to market to acquire and implement these systems. Fast-forward 10 years, and it is apparent that the EHR space has significantly evolved in technology, processes, and policies.
View Article and Find Full Text PDFBackground: Artificial intelligence (AI), including machine learning (ML) and deep learning, has the potential to revolutionize biomedical research. Defined as the ability to "mimic" human intelligence by machines executing trained algorithms, AI methods are deployed for biomarker discovery.
Objective: We detail the advancements and challenges in the use of AI for biomarker discovery in ovarian and pancreatic cancer.
As more digital resources are produced by the research community, it is becoming increasingly important to harmonize and organize them for synergistic utilization. The findable, accessible, interoperable, and reusable (FAIR) guiding principles have prompted many stakeholders to consider strategies for tackling this challenge. The FAIRshake toolkit was developed to enable the establishment of community-driven FAIR metrics and rubrics paired with manual and automated FAIR assessments.
View Article and Find Full Text PDFIntroduction: The effects of device and patient characteristics on health and economic outcomes in patients with cardiac implantable electronic devices (CIEDs) are unclear. Modeling can estimate costs and outcomes for patients with CIEDs under a variety of scenarios, varying battery longevity, comorbidities, and care settings. The objective of this analysis was to compare changes in patient outcomes and payer costs attributable to increases in battery life of implantable cardiac defibrillators (ICDs) and cardiac resynchronization therapy defibrillators (CRT-D).
View Article and Find Full Text PDFObjectives: Health care-associated infections (HAIs) pose a significant health care and cost burden. This study estimates annual HAI hospital costs in the US avoided through use of health care antiseptics (health care personnel hand washes and rubs; surgical hand scrubs and rubs; patient preoperative and preinjection skin preparations).
Methods: A spreadsheet model was developed with base case inputs derived from the published literature, supplemented with assumptions when data were insufficient.
Methods Mol Biol
November 2006
Requirements for a flexible image analysis package for high content screening (HCS) are discussed. An overview of tools and techniques for image analysis and machine learning is given. Machine learning for classification and segmentation, the two fundamental elements of image analysis, is discussed.
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