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CHPC - Research Computing and Data Support for the University

In addition to deploying and operating high performance computational resources and providing advanced user support and training, CHPC serves as an expert team to broadly support the increasingly diverse research computing and data needs on campus. These needs include support for big data, big data movement, data analytics, security, virtual machines, Windows science application servers, protected environments for data mining and analysis of protected health information, and advanced networking.

If you are new to CHPC, the best place to start to get more information on CHPC resources and policies is our Getting Started page.

Upcoming Events:

CHPC Downtime: Tuesday March 5 starting at 7:30am

Posted February 8th, 2024


Two upcoming security related changes

Posted February 6th, 2024


Allocation Requests for Spring 2024 are Due March 1st, 2024

Posted February 1st, 2024


CHPC ANNOUNCEMENT: Change in top level home directory permission settings

Posted December 14th, 2023


CHPC Spring 2024 Presentation Schedule Now Available

CHPC PE DOWNTIME: Partial Protected Environment Downtime  -- Oct 24-25, 2023

Posted October 18th, 2023


CHPC INFORMATION: MATLAB and Ansys updates

Posted September 22, 2023


CHPC SECURITY REMINDER

Posted September 8th, 2023

CHPC is reaching out to remind our users of their responsibility to understand what the software being used is doing, especially software that you download, install, or compile yourself. Read More...

News History...


Two-stage model   AugmentedBi-LSTM-CRF model

Hybrid Models for Medication and Adverse Drug Events Extraction

By Keely Peterson1,4, Jianlin Shi2, Alec Chapman3, Hannah Eyre1,4,5, Heather Lent1,4, Kevin Graves2, Jianyin Shao2, Subhadeep Nag2, Olga Patterson1,2,4, John F. Hurdle2

1Division of Epidemiology, Department of Internal Medicine; 2Department of Biomedical Informatics, University of Utah 3Health Fidelity, San Mateo, CA; 4VA Salt Lake City Health Care System; 5School of Computing, University of Utah

This is the abstract for our solution of the National NLP Clinical Challenges (n2c2) track 2. The goal is to identify drugs and drug-related adverse events from the clinical notes. This track includes 9 types of entities and 8 types of relations to be identified. We used two different models to complete the NER tasks and one model for the relation task. We ranked the 8th in the NER task, the 2nd place in the relation task, and the 5th in the end-to-end task.

System Status

General Environment

last update: 2024-03-01 21:00:02
General Nodes
system cores % util.
kingspeak 848/972 87.24%
notchpeak 2620/3212 81.57%
lonepeak 2044/3140 65.1%
Owner/Restricted Nodes
system cores % util.
ash 1152/1152 100%
notchpeak 8927/18156 49.17%
kingspeak 1924/5468 35.19%
lonepeak 0/416 0%

Protected Environment

last update: 2024-03-01 21:00:02
General Nodes
system cores % util.
redwood 464/616 75.32%
Owner/Restricted Nodes
system cores % util.
redwood 510/6088 8.38%


Cluster Utilization

Last Updated: 2/20/24