Abstract
Utilize electronic health records (EHR) to forecast the likelihood of a patient succumbing under the current clinical condition. This assists healthcare professionals in identifying clinical emergencies promptly, enabling timely intervention to alter the patient’s critical state. Existing healthcare prediction models are typically based on clinical features of EHR data to learn a patient’s clinical representation, but they frequently disregard structural information in features. To address this issue, we propose Adaptive Clinical latent Hierarchy construction and Information fusion Model (ACHIM), which adaptively constructs a clinical potential level without prior knowledge and aggregates the structural information from the learned into the original data to obtain a compact and informative representation of the human state. Our experimental results on real-world datasets demonstrate that our model can extract fine-grained representations of patient characteristics from sparse data and significantly improve the performance of death prediction tasks performed on EHR datasets.
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References
Baytas, I.M., Xiao, C., Zhang, X., Wang, F., Jain, A.K., Zhou, J.: Patient subtyping via time-aware LSTM networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 65–74 (2017)
Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 787–795 (2017)
Choi, E., Bahadori, M.T., Sun, J., Kulas, J., Schuetz, A., Stewart, W.: Retain: an interpretable predictive model for healthcare using reverse time attention mechanism. In: Advances in Neural Information Processing Systems 29 (2016)
Choi, E., Xiao, C., Stewart, W., Sun, J.: Mime: Multilevel medical embedding of electronic health records for predictive healthcare. In: Advances in Neural Information Processing Systems 31 (2018)
Choi, E., et al.: Learning the graphical structure of electronic health records with graph convolutional transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 606–613 (2020)
Gao, J., et al.: Camp: co-attention memory networks for diagnosis prediction in healthcare. In: 2019 IEEE International Conference on Data Mining (ICDM), pp. 1036–1041. IEEE (2019)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Lee, W., Park, S., Joo, W., Moon, I.C.: Diagnosis prediction via medical context attention networks using deep generative modeling. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 1104–1109. IEEE (2018)
Ma, F., Chitta, R., Zhou, J., You, Q., Sun, T., Gao, J.: Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1903–1911 (2017)
Ma, F., et al.: Unsupervised discovery of drug side-effects from heterogeneous data sources. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 967–976 (2017)
Ma, F., You, Q., Xiao, H., Chitta, R., Zhou, J., Gao, J.: Kame: knowledge-based attention model for diagnosis prediction in healthcare. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 743–752 (2018)
Ma, L., et al.: Concare: Personalized clinical feature embedding via capturing the healthcare context. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 833–840 (2020)
Ma, T., Xiao, C., Wang, F.: Health-atm: a deep architecture for multifaceted patient health record representation and risk prediction. In: Proceedings of the 2018 SIAM International Conference on Data Mining, pp. 261–269. SIAM (2018)
Miotto, R., Wang, F., Wang, S., Jiang, X., Dudley, J.T.: Deep learning for healthcare: review, opportunities and challenges. Brief. Bioinform. 19(6), 1236–1246 (2018)
Rajkomar, A., et al.: Scalable and accurate deep learning with electronic health records. NPJ Digital Med. 1(1), 1–10 (2018)
Suo, Q., et al.: Deep patient similarity learning for personalized healthcare. IEEE Trans. Nanobiosci. 17(3), 219–227 (2018)
Suo, Q., et al.: Personalized disease prediction using a CNN-based similarity learning method. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 811–816. IEEE (2017)
Suresh, H., Gong, J.J., Guttag, J.V.: Learning tasks for multitask learning: heterogenous patient populations in the ICU. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 802–810 (2018)
Tan, Q., et al.: Data-gru: Dual-attention time-aware gated recurrent unit for irregular multivariate time series. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 930–937 (2020)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)
Xu, Y., Biswal, S., Deshpande, S.R., Maher, K.O., Sun, J.: Raim: recurrent attentive and intensive model of multimodal patient monitoring data. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2565–2573 (2018)
Yuan, Y., et al.: A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning. In: 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), pp. 206–209. IEEE (2018)
Zhang, C., Gao, X., Ma, L., Wang, Y., Wang, J., Tang, W.: Grasp: generic framework for health status representation learning based on incorporating knowledge from similar patients. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, pp. 715–723 (2021)
Zhang, S., Xie, P., Wang, D., Xing, E.P.: Medical diagnosis from laboratory tests by combining generative and discriminative learning. arXiv preprint arXiv:1711.04329 (2017)
Acknowledgements
This work is supported by the National Key Research and Development Program of China (No. 2022YFB3904702), Key Research and Development Program of Jiangsu Province (No.BE2018084), Opening Project of Beijing Key Laboratory of Mobile Computing and Pervasive Device, and Industrial Internet Innovation and Development Project of 2021 (TC210A02M, TC210804D).
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Liu, G., Ye, J., Wang, B. (2024). ACHIM: Adaptive Clinical Latent Hierarchy Construction and Information Fusion Model for Healthcare Knowledge Representation. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14648. Springer, Singapore. https://doi.org/10.1007/978-981-97-2238-9_24
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DOI: https://doi.org/10.1007/978-981-97-2238-9_24
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