Show simple item record

Semi‐supervised joint learning for longitudinal clinical events classification using neural network models

dc.contributor.authorTang, Weijing
dc.contributor.authorMa, Jiaqi
dc.contributor.authorWaljee, Akbar K.
dc.contributor.authorZhu, Ji
dc.date.accessioned2020-11-04T15:58:16Z
dc.date.availableWITHHELD_3_MONTHS
dc.date.available2020-11-04T15:58:16Z
dc.date.issued2020
dc.identifier.citationTang, Weijing; Ma, Jiaqi; Waljee, Akbar K.; Zhu, Ji (2020). "Semi‐supervised joint learning for longitudinal clinical events classification using neural network models." Stat 9(1): n/a-n/a.
dc.identifier.issn2049-1573
dc.identifier.issn2049-1573
dc.identifier.urihttps://hdl.handle.net/2027.42/163377
dc.publisherCurran Associates, Inc
dc.publisherWiley Periodicals, Inc.
dc.subject.otherrecurrent neural networks
dc.subject.otherlongitudinal features
dc.subject.otherjoint learning
dc.subject.othersemi‐supervised learning
dc.titleSemi‐supervised joint learning for longitudinal clinical events classification using neural network models
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelMathematics
dc.subject.hlbtoplevelScience
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/163377/2/sta4305.pdfen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/163377/1/sta4305_am.pdfen_US
dc.identifier.doi10.1002/sta4.305
dc.identifier.sourceStat
dc.identifier.citedreferenceNarayanaswamy, S., Paige, T. B., Vande Meent, J. W., Desmaison, A., Goodman, N., Kohli, P., & Torr, P. ( 2017 ). Learning disentangled representations with semi‐supervised deep generative models, In Advances in neural information processing systems (pp. 5925 – 5935 ). Long Beach, CA: Curran Associates, Inc.
dc.identifier.citedreferenceBallinger, B., Hsieh, J., Singh, A., Sohoni, N., Wang, J., Tison, G. H., … Pletcher, M. J. ( 2018 ). DeepHeart: Semi‐supervised sequence learning for cardiovascular risk prediction. In Proceedings of the Thirty‐Second AAAI Conference on Artificial Intelligence, New Orleans, LA, pp. 2079 ‐ 2086.
dc.identifier.citedreferenceChe, Z., Cheng, Y., Zhai, S., Sun, Z., & Liu, Y. ( 2017 ). Boosting deep learning risk prediction with generative adversarial networks for electronic health records. In 2017 IEEE International Conference on Data Mining (ICDM), New Orleans, LA, pp. 787 – 792. https://doi.org/10.1109/ICDM.2017.93
dc.identifier.citedreferenceCho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. ( 2014 ). Learning phrase representations using RNN encoder–decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 1724 – 1734. https://doi.org/10.3115/v1/D14-1179
dc.identifier.citedreferenceChoi, E., Schuetz, A., Stewart, W. F., & Sun, J. ( 2016 ). Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, 24 ( 2 ), 361 – 370. https://doi.org/10.1093/jamia/ocw112
dc.identifier.citedreferenceChung, J., Kastner, K., Dinh, L., Goel, K., Courville, A. C., & Bengio, Y. ( 2015 ). A recurrent latent variable model for sequential data, In Advances in Neural Information Processing Systems (pp. 2980 – 2988 ). Montreal, Canada: Curran Associates, Inc.
dc.identifier.citedreferenceDai, A. M., & Le, Q. V. ( 2015 ). Semi‐supervised sequence learning, In Advances in Neural Information Processing Systems (pp. 3079 – 3087 ). Montreal, Canada: Curran Associates, Inc.
dc.identifier.citedreferenceEsteban, C., Staeck, O., Baier, S., Yang, Y., & Tresp, V. ( 2016 ). Predicting clinical events by combining static and dynamic information using recurrent neural networks. In 2016 IEEE International Conference on Healthcare Informatics (ICHI), Chicago, IL, pp. 93 – 101. https://doi.org/10.1109/ICHI.2016.16
dc.identifier.citedreferenceGoldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., & Stanley, H. E. ( 2000 ). Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals. Circulation, 101 ( 23 ), e215 – e220. https://doi.org/10.1161/01.CIR.101.23.e215
dc.identifier.citedreferenceGoodfellow, I., Pouget‐Abadie, J., Mirza, M., Xu, B., Warde‐Farley, D., Ozair, S., & Bengio, Y. ( 2014 ). Generative adversarial nets, In Advances in Neural Information Processing Systems (pp. 2672 – 2680 ). Montreal, Canada: Curran Associates, Inc.
dc.identifier.citedreferenceGulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., & Webster, D. R. ( 2016 ). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Journal of the American Medical Association (JAMA), 316 ( 22 ), 2402 – 2410. https://doi.org/10.1001/jama.2016.17216
dc.identifier.citedreferenceHarutyunyan, H., Khachatrian, H., Kale, D. C., VerSteeg, G., & Galstyan, A. ( 2019 ). Multitask learning and benchmarking with clinical time series data. Scientific Data, 6 ( 1 ), 1 – 18. https://doi.org/10.1038/s41597-019-0103-9
dc.identifier.citedreferenceJohnson, A. E., Pollard, T. J., Shen, L., Li‐wei, H. L., Feng, M., Ghassemi, M., & Mark, R. G. ( 2016 ). MIMIC‐III, a freely accessible critical care database. Scientific Data, 3 ( 1 ), 1 – 9. https://doi.org/10.1038/sdata.2016.35
dc.identifier.citedreferenceKaji, D. A., Zech, J. R., Kim, J. S., Cho, S. K., Dangayach, N. S., Costa, A. B., & Oermann, E. K. ( 2019 ). An attention based deep learning model of clinical events in the intensive care unit. PLOS One, 14 ( 2 ), e0211057. https://doi.org/10.1371/journal.pone.0211057
dc.identifier.citedreferenceKingma, D. P., Mohamed, S., Rezende, D. J., & Welling, M. ( 2014 ). Semi‐supervised learning with deep generative models, In Advances in neural information processing systems (pp. 3581 – 3589 ). Montreal, Canada: Curran Associates, Inc.
dc.identifier.citedreferenceKingma, D. P., & Welling, M. ( 2014 ). Auto‐encoding variational bayes. In 2nd International Conference on Learning Representations (ICLR), Banff, Canada.
dc.identifier.citedreferenceMikolov, T., Chen, K., Corrado, G., & Dean, J. ( 2013 ). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
dc.identifier.citedreferenceOdena, A. ( 2016 ). Semi‐supervised learning with generative adversarial networks. arXiv preprint arXiv:1606.01583.
dc.identifier.citedreferencePurushotham, S., Meng, C., Che, Z., & Liu, Y. ( 2018 ). Benchmarking deep learning models on large healthcare datasets. Journal of Biomedical Informatics, 83, 112 – 134. https://doi.org/10.1016/j.jbi.2018.04.007
dc.identifier.citedreferenceSocher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. ( 2013 ). Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP), Seattle, WA, pp. 1631 – 1642.
dc.identifier.citedreferenceWu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., … Klingner, J. ( 2016 ). Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.
dc.identifier.citedreferenceYao, L., Mao, C., & Luo, Y. ( 2019 ). Clinical text classification with rule‐based features and knowledge‐guided convolutional neural networks. BMC Medical Informatics and Decision Making, 19 ( Suppl 3 ), 71. https://doi.org/10.1186/s12911-019-0781-4
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.