TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications
Summary: TRACER enables accurate, interpretable high-stakes analytics with TITV, separating time-invariant/time-variant feature importance. Self-attention and feature-wise transform yield patient- and feature-level explanations; validated on real hospital data. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Kaiping Zheng (National University of Singapore)
- 2. Shaofeng Cai (National University of Singapore)
- 3. Horng Ruey Chua (National University Health System)
- 4. Wei Wang (National University of Singapore)
- 5. Kee Yuan Ngiam (National University Health System)
- 6. Beng Chin Ooi (National University of Singapore)
BibTeX Citation
@inproceedings{zheng_sigmod20,
title = {{TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications}},
author = {Zheng, Kaiping and Cai, Shaofeng and Chua, Horng Ruey and Wang, Wei and Ngiam, Kee Yuan and Ooi, Beng Chin},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389720},
url = {https://dl.acm.org/doi/10.1145/3318464.3389720},
year = {2020}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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|---|---|---|---|---|
| 415 | SystemML: Declarative Machine Learning on Spark | 2016 | VLDB | 0.0001865959 |
| 654 | Materialization Optimizations for Feature Selection Workloads | 2014 | SIGMOD | 0.0001510357 |
| 2,966 | Choosing A Cloud DBMS: Architectures and Tradeoffs | 2019 | VLDB | 7.8039817e-05 |
| 3,955 | Smile: A System to Support Machine Learning on EEG Data at Scale | 2019 | VLDB | 6.9025583e-05 |
| 6,260 | GEMINI: An Integrative Healthcare Analytics System | 2014 | VLDB | 5.832951e-05 |
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