TaGiTeD: Predictive Task Guided Tensor Decomposition for Representation Learning from Electronic Health Records
Open accessAbstract
With the better availability of healthcare data, such as Electronic Health Records (EHR), more and more data analytics methodologies are developed aiming at digging insights from them to improve the quality of care delivery. There are many challenges on analyzing EHR, such as high dimensionality and event sparsity. Moreover, different from other application domains, the EHR analysis algorithms need to be highly interpretable to make them clinically useful. This makes representation learning from EHRs of key importance. In this paper, we propose an algorithm called Predictive Task Guided Tensor Decomposition (TaGiTeD), to analyze EHRs. Specifically, TaGiTeD learns event interaction patterns that are highly predictive for certain tasks from EHRs with supervised tensor decomposition. Compared with unsupervised methods, TaGiTeD can learn effective EHR representations in a more focused way. This is crucial because most of the medical problems have very limited patient samples, which are not enough for unsupervised algorithms to learn meaningful representations form. We apply TaGiTeD on real world EHR data warehouse and demonstrate that TaGiTeD can learn representations that are both interpretable and predictive.
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From Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review (2021) · cites this paper as [48]
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1 retracted
A Graph Based Methodology for Temporal Signature Identification from HER.
Wang, F., Liu, C., Wang, Y., Hu, J., & Yu, G. (2015) · PubMed Central · Cited by 5 · see citing works
Self-citationRetractedRepresentation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works
Deep Neural Networks for Acoustic Modeling in Speech Recognition
Hinton, G. E., Deng, L., Yu, D., Dahl, G. E., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., et al. (2012) · Cited by 1,839 · see citing works
Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
Rifai, S., Vincent, P., Muller, X., Glorot, X., & Bengio, Y. (2011) · Cited by 1,189 · see citing works
(1993) · Cited by 1,173 · see citing works
Tensor completion and low-n-rank tensor recovery via convex optimization
Gandy, S., Recht, B., & Yamada, I. (2011) · Inverse Problems · doi:10.1088/0266-5611/27/2/025010 · Cited by 781 · see citing works
(2011) · Cited by 514 · see citing works
Boulanger-Lewandowski, N., Bengio, Y., & Vincent, P. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.6392 · Cited by 484 · see citing works
Multilinear operators for higher-order decompositions.
Kolda, T. G. (2006) · doi:10.2172/923081 · Cited by 375 · see citing works
On Tensors, Sparsity, and Nonnegative Factorizations
Chi, E. C., & Kolda, T. G. (2012) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/110859063 · Cited by 274 · see citing works
Ho, J. C., Ghosh, J., & Sun, J. (2014) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623658 · Cited by 214 · see citing works
Tensor decompositions for feature extraction and classification of high dimensional datasets
Phan, A. H., & Cichocki, A. S. (2010) · Nonlinear Theory and Its Applications IEICE · doi:10.1587/nolta.1.37 · Cited by 212 · see citing works
Welling, M., & Weber, M. (2001) · Pattern Recognition Letters · doi:10.1016/s0167-8655(01)00070-8 · Cited by 175 · see citing works
Smoothed analysis of tensor decompositions
Bhaskara, A., Charikar, M., Moitra, A., & Vijayaraghavan, A. (2014) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/2591796.2591881 · Cited by 117 · see citing works
Bengio, Y. (2008) · Scholarpedia · doi:10.4249/scholarpedia.3881 · Cited by 105 · see citing works
Numerical optimization for symmetric tensor decomposition
Kolda, T. G. (2015) · Mathematical Programming · doi:10.1007/s10107-015-0895-0 · Cited by 59 · see citing works
Spike-and-Slab Sparse Coding for Unsupervised Feature DiscoveryPreprint
Goodfellow, I., Courville, A. C., & Bengio, Y. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1201.3382 · Cited by 41 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Macskassy, S. A.; Perlich, C.; Leskovec, J.; Wang, W.; and Ghani, R., eds. 2014. The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14, New York, NY, USA - August 24 - 27, 2014 . ACM.
- Wang, Y.; Chen, R.; Ghosh, J.; Denny, J. C.; Kho, A.; Chen, Y.; Malin, B. A.; and Sun, J. 2015b. Rubik: Knowledge guided tensor factorization and completion for health data analytics. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 1265-1274.
Cited by
Sorted by citations
Si, Y., Du, J., Li, Z., Jiang, X., Miller, T., Wang, F., Jim Zheng, W., & Roberts, K. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103671 · Cited by 235 · see citing works
Self-citationXie, F., Yuan, H., Ning, Y., Ong, M. E. H., Feng, M., Hsu, W., Chakraborty, B., & Liu, N. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103980 · Cited by 159 · see citing works
Predicting Hospital Readmission: A Joint Ensemble-Learning Model
Yu, K., & Xie, X. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2938995 · Cited by 51 · see citing works
Xu, Y., Chu, X., Yang, K., Wang, Z., Zou, P., Ding, H., Zhao, J., Wang, Y., & Xie, B. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583543 · Cited by 29 · see citing works
Self-citationMachine Learning Algorithms for COPD Patients Readmission Prediction: A Data Analytics Approach
Mohamed, I., Fouda, M. M., & Hosny, K. M. (2022) · IEEE Access · doi:10.1109/access.2022.3148600 · Cited by 27 · see citing works
Yang, K., Xu, Y., Zou, P., Ding, H., Zhao, J., Wang, Y., & Xie, B. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25667 · Cited by 25 · see citing works
Self-citationVecoCare: Visit Sequences-Clinical Notes Joint Learning for Diagnosis Prediction in Healthcare Data
Xu, Y., Yang, K., Zhang, C., Zou, P., Wang, Z., Ding, H., Zhao, J., Wang, Y., & Xie, B. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/547 · Cited by 25 · see citing works
Self-citationMulti-Type Itemset Embedding for Learning Behavior Success
Wang, D., Jiang, M., Zeng, Q., Eberhart, Z., & Chawla, N. V. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219949 · Cited by 25 · see citing works
Yin, K., Qian, D., Cheung, W. K., Fung, B. C. M., & Poon, J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33011246 · Cited by 19 · see citing works
Learning Inter-Modal Correspondence and Phenotypes From Multi-Modal Electronic Health Records
Yin, K., Cheung, W. K., Fung, B. C. M., & Poon, J. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3038211 · Cited by 12 · see citing works
Multi-aspect Mining of Complex Sensor Sequences
Honda, T., Matsubara, Y., Neyama, R., Abe, M., & Sakurai, Y. (2019) · Proceedings · doi:10.1109/icdm.2019.00040 · Cited by 10 · see citing works
Generalized and transferable patient language representation for phenotyping with limited data
Si, Y., Bernstam, E. V., & Roberts, K. E. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103726 · Cited by 10 · see citing works
Unsupervised EHR ‐based phenotyping via matrix and tensor decompositions
Becker, F., Smilde, A. K., & Acar, E. (2023) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1494 · Cited by 9 · see citing works
Yin, K., Cheung, W. K., Liu, Y., Fung, B. C. M., & Poon, J. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/504 · Cited by 9 · see citing works
Adversarial shared-private model for cross-domain clinical text entailment recognition
Fei, H., Guo, Y., Li, B., Ji, D., & Ren, Y. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106962 · Cited by 6 · see citing works
Enhancing Personalized Healthcare via Capturing Disease Severity, Interaction, and Progression
Tan, Y., Zhou, Z., Yu, L., Liu, W., Chen, C., Ma, G., Hu, X., Hertzberg, V., & Yang, C. (2023) · Proceedings · doi:10.1109/icdm58522.2023.00173 · Cited by 7 · see citing works
All-at-once Decomposition of Coupled Billion-scale Tensors in Apache Spark
Gudibanda, A., Henretty, T., Baskaran, M. M., Ezick, J., & Lethin, R. (2018) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec.2018.8547544 · Cited by 7 · see citing works
MuLOT: Multi-level Optimization of the Canonical Polyadic Tensor Decomposition at Large-Scale
Gillet, A., Leclercq, É., & Cullot, N. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-82472-3_15 · Cited by 5 · see citing works
Tensor-Factorization-Based Phenotyping using Group Information
Choi, J., Kim, Y., Kim, H., Choi, I. Y., & Yu, H. (2017) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3107411.3107423 · Cited by 4 · see citing works
Choi, J., Kim, Y., Kim, H., Choi, I. Y., & Yu, H. (2018) · PLoS ONE · doi:10.1371/journal.pone.0197518 · Cited by 4 · see citing works
SWoTTeD: an extension of tensor decomposition to temporal phenotyping
Sebia, H., Guyet, T., & Audureau, É. (2024) · Machine Learning · doi:10.1007/s10994-024-06545-8 · Cited by 3 · see citing works
Explainable Health Risk Predictor with Transformer-based Medicare Claim EncoderPreprint
Lahlou, C., Crayton, A., Trier, C., & Willett, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.09428 · Cited by 5 · see citing works
Gillet, A., Leclercq, É., & Cullot, N. (2022) · Information Systems · doi:10.1016/j.is.2022.102142 · Cited by 2 · see citing works
On dynamic tensor decompositions
Zhou, S. (2019) · Minerva Access (University of Melbourne) · Cited by 2 · see citing works
Supervised Nonnegative Tucker Decomposition for Computational Phenotyping
Yang, K., Sun, W., Zhao, J., Wang, Y., & Xie, B. (2019) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00157
Self-citationImputing Growth Snapshot Similarity in Early Childhood Development: A Tensor Decomposition Approach
Schnur, J. J., Karl, R., Garcia-Martínez, A., Jiang, M., & Chawla, N. V. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313188
Gillet, A., Leclercq, É., & Sautot, L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-662-68014-8_3
Machine Learning Algorithms for COPD Patients Readmission Prediction
Goud, P. A., & Arathi, M. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.55770
Semi-Supervised PARAFAC2 Decomposition for Computational Phenotyping Using Electronic Health Records
Konyar, E., & Gahrooei, M. R. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3530271
Multimorbidity measurement strategies for predicting hospital visits
Neves, B. D., Moreira, J. M., Gonçalves, S., Cerejo, J., Mota, I., Silva, N. A., Leite, F., & Silva, M. J. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-13817-z
- Authors
- Yang, K., Li, X., Liu, H., Mei, J., Xie, G., Zhao, J., Xie, B., & Wang, F.
- Affiliations
- Peking University (CN)
- IBM Research (China) (CN)
- Cornell University (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2017-02-13
- Volume
- 31
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2604836086
- Cited by
- 30
- References
- 17
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 7 · 2022: 2 · 2021: 5 · 2020: 3 · 2019: 5 · 2018: 4 · 2017: 1
- FWCI
- 1.62
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- Funders
- National Science Foundation (1650723)
- National Natural Science Foundation of China (61472007)
- China National Funds for Distinguished Young Scientists
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Mathematics › Computational Mathematics › Tensor decomposition and applications
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- electronic health records, representation learning, tensor decomposition, model interpretability, high-dimensional data, medical data analysis
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