Demo Data from SIGIR 2026 / reviewers in the wild / expert
Gregg Tracton
dblp:63/2668
· DBLP profile ↗
4ranked-venue papers
0as first author
0since 2021 · last 2016
0000-0001-7670-6550ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 3Graphics, computer vision, multimedia, augmented reality and games · 2Artificial intelligence and machine learning · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% | |
| Computer graphics and multimedia
1 paper |
Geometric modeling and processing · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Medical and health informatics › medical imaging
medical image analysis |
0.0 | 1 | 2003 | Deformable M-Reps for 3D Medical Image Segmentation · Int. J. Comput. Vis. 2003 |
Medical and health informatics › medical imaging › medical image analysis
medical image segmentation |
0.0 | 1 | 2003 | Deformable M-Reps for 3D Medical Image Segmentation · Int. J. Comput. Vis. 2003 |
Geometric modeling and processing › shape representation
medial representation |
0.0 | 1 | 2003 | Deformable M-Reps for 3D Medical Image Segmentation · Int. J. Comput. Vis. 2003 |
Geometric modeling and processing
shape modeling |
0.0 | 1 | 2003 | Deformable M-Reps for 3D Medical Image Segmentation · Int. J. Comput. Vis. 2003 |
Methods — techniques the papers use, named apart from their topics
m-reps · 0.1deformable models · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Toward a better understanding of task demands, workload, and performance during physician-computer interactionsabstractOBJECTIVE: To assess the relationship between (1) task demands and workload, (2) task demands and performance, and (3) workload and performance, all during physician-computer interactions in a simulated environment. METHODS: Two experiments were performed in 2 different electronic medical record (EMR) environments: WebCIS (n = 12) and Epic (n = 17). Each participant was instructed to complete a set of prespecified tasks on 3 routine clinical EMR-based scenarios: urinary tract infection (UTI), pneumonia (PN), and heart failure (HF). Task demands were quantified using behavioral responses (click and time analysis). At the end of each scenario, subjective workload was measured using the NASA-Task-Load Index (NASA-TLX). Physiological workload was measured using pupillary dilation and electroencephalography (EEG) data collected throughout the scenarios. Performance was quantified based on the maximum severity of omission errors. RESULTS: Data analysis indicated that the PN and HF scenarios were significantly more demanding than the UTI scenario for participants using WebCIS (P < .01), and that the PN scenario was significantly more demanding than the UTI and HF scenarios for participants using Epic (P < .01). In both experiments, the regression analysis indicated a significant relationship only between task demands and performance (P < .01). DISCUSSION: Results suggest that task demands as experienced by participants are related to participants' performance. Future work may support the notion that task demands could be used as a quality metric that is likely representative of performance, and perhaps patient outcomes. CONCLUSION: The present study is a reasonable next step in a systematic assessment of how task demands and workload are related to performance in EMR-evolving environments. Lukasz Mazur, Prithima Mosaly, Carlton Moore, Elizabeth Comitz, Fei Yu 0013, Aaron D. Falchook, Michael J. Eblan, Lesley M. Hoyle, Gregg Tracton, Bhishamjit S. Chera, Lawrence B. Marks |
J. Am. Medical Informatics Assoc. | 9 |
| 2004 | Prostate Shape Modeling Based on Principal Geodesic Analysis Bootstrapping
Erik Dam, P. Thomas Fletcher, Stephen M. Pizer, Gregg Tracton, Julian G. Rosenman |
MICCAI (2) | 4 |
| 2003 | Deformable M-Reps for 3D Medical Image Segmentation
Stephen M. Pizer, P. Thomas Fletcher, Sarang C. Joshi, Andrew Thall, James Z. Chen, Yonatan Fridman, Daniel S. Fritsch, A. Graham Gash, John M. Glotzer, Michael R. Jiroutek, Conglin Lu, Keith E. Muller, Gregg Tracton, Paul A. Yushkevich, Edward L. Chaney |
Int. J. Comput. Vis. | 13 |
| 2001 | Segmentation of Single-Figure Objects by Deformable M-reps
Stephen M. Pizer, Sarang C. Joshi, P. Thomas Fletcher, Martin Styner, Gregg Tracton, James Z. Chen |
MICCAI | 5 |