Abstract
The healthcare industry has a wealth of data that can be used by researchers and medical professionals to infer a patient’s condition and intention to receive treatment using machine learning models. However, this line of research generally suffers from some limitations: (1) struggling to leverage structural interactions among patients; (2) attending to learn patient representations from electronic medical records (EMRs) but rarely considering supplementary contexts; and (3) overlooking EMR data imbalance issue. To address these limitations, in this paper, we propose a hierarchical graph neural network for patient treatment preference prediction. Doctors’ information and their viewing activities are first integrated as external knowledge with EMRs to construct the hierarchical graph, where a dual message passing paradigm is then devised to perform intra- and inter-subgraph aggregation to enrich patient representations and advance label propagation. To mitigate patient data imbalance issue, a community detection method is further designed to better prediction. Our experimental results demonstrate the state-of-the-art performance on patient treatment preference prediction.
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References
Ando, S., Huang, C.Y.: Deep over-sampling framework for classifying imbalanced data. In: Ceci, M., Hollmén, J., Todorovski, L., Vens, C., Džeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10534, pp. 770–785. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71249-9_46
Buda, M., Maki, A., Mazurowski, M.A.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 106, 249–259 (2018)
Cai, D., Sun, C., Song, M., Zhang, B., Hong, S., Li, H.: Hypergraph contrastive learning for electronic health records. In: SDM, pp. 127–135. SIAM (2022)
Chen, J., Ma, T., Xiao, C.: Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247 (2018)
Chen, L., Li, X., Wu, D.: Enhancing robustness of graph convolutional networks via dropping graph connections. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds.) ECML PKDD 2020. LNCS (LNAI), vol. 12459, pp. 412–428. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67664-3_25
Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: SIGKDD, pp. 785–794 (2016)
Chipidza, F.E., Wallwork, R.S., Stern, T.A.: Impact of the doctor-patient relationship. The primary care companion for CNS disorders 17(5), 27354 (2015)
Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: SIGKDD (2017)
Choi, E., et al.: Learning the graphical structure of electronic health records with graph convolutional transformer. In: AAAI. vol. 34, pp. 606–613 (2020)
Chu, J., Dong, W., Wang, J., He, K., Huang, Z.: Treatment effect prediction with adversarial deep learning using electronic health records. In: BMC MIDM (2020)
Cui, L., Biswal, S., Glass, L.M., Lever, G., Sun, J., Xiao, C.: Conan: complementary pattern augmentation for rare disease detection. In: AAAI (2020)
Dossa, F., Chesney, T.R., Acuna, S.A., Baxter, N.N.: A watch-and-wait approach for locally advanced rectal cancer after a clinical complete response following neoadjuvant chemoradiation: a systematic review and meta-analysis. The lancet Gastroenterology & hepatology 2(7), 501–513 (2017)
Elkan, C.: The foundations of cost-sensitive learning. In: IJCAI. vol. 17, pp. 973–978. Lawrence Erlbaum Associates Ltd (2001)
Fernández, A., Garcia, S., Herrera, F., Chawla, N.V.: Smote for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J. Artifi. Intell. Res. 61, 863–905 (2018)
Ghorbani, M., Kazi, A., Baghshah, M.S., Rabiee, H.R., Navab, N.: Ra-gcn: graph convolutional network for disease prediction problems with imbalanced data. Med. Image Anal. 75, 102272 (2022)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Lee, W., Seo, K.: Downsampling for binary classification with a highly imbalanced dataset using active learning. Big Data Res. 28, 100314 (2022)
Li, Q., Li, X., Chen, L., Wu, D.: Distilling Knowledge on Text Graph for Social Media Attribute Inference. In: SIGIR, pp. 2024–2028 (2022)
Li, X., Chen, L., Wu, D.: Turning attacks into protection: Social media privacy protection using adversarial attacks. In: SDM, pp. 208–216. SIAM (2021)
Lin, W.C., Tsai, C.F., Hu, Y.H., Jhang, J.S.: Clustering-based undersampling in class-imbalanced data. Inf. Sci. 409, 17–26 (2017)
Ling, C.X., Sheng, V.S.: Cost-sensitive learning and the class imbalance problem. Encyclopedia Mach. Learn. 2011, 231–235 (2008)
Liu, R., Wei, L., Zhang, P.: A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data. Nature Mach. Intell. 3(1), 68–75 (2021)
Liu, Y., et al.: Learning to propagate labels: Transductive propagation network for few-shot learning. arXiv preprint arXiv:1805.10002 (2018)
Liu, Z., Li, X., Peng, H., He, L., Philip, S.Y.: Heterogeneous similarity graph neural network on electronic health records. In: IEEE Big Data (2020)
Ma, F., Wang, Y., Gao, J., Xiao, H., Zhou, J.: Rare disease prediction by generating quality-assured electronic health records. In: SDM, pp. 514–522. SIAM (2020)
Ma, F., You, Q., Xiao, H., Chitta, R., Zhou, J., Gao, J.: Kame: Knowledge-based attention model for diagnosis prediction in healthcare. In: CIKM (2018)
Papadopoulos, S., Kompatsiaris, Y., Vakali, A., Spyridonos, P.: Community detection in social media. Data Min. Knowl. Disc. 24(3), 515–554 (2012)
Reimers, N., Gurevych, I.: Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084 (2019)
Ross, M.K., Yoon, J., van der Schaar, A., van der Schaar, M.: Discovering pediatric asthma phenotypes on the basis of response to controller medication using machine learning. Ann. Am. Thorac. Soc. 15(1), 49–58 (2018)
Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proceedings of the national academy of sciences (2008)
Saqib, M., Sha, Y., Wang, M.D.: Early prediction of sepsis in emr records using traditional ml techniques and deep learning lstm networks. In: EMBC (2018)
Segura-Bedmar, I., Colón-Ruíz, C., Tejedor-Alonso, M.Á., Moro-Moro, M.: Predicting of anaphylaxis in big data emr by exploring machine learning approaches. J. Biomed. Inform. 87, 50–59 (2018)
Thekumparampil, K.K., Wang, C., Oh, S., Li, L.J.: Attention-based graph neural network for semi-supervised learning. arXiv preprint arXiv:1803.03735 (2018)
Yang, J., McAuley, J., Leskovec, J.: Community detection in networks with node attributes. In: ICDM, pp. 1151–1156. IEEE (2013)
Yang, J., Liu, Y., Qian, M., Guan, C., Yuan, X.: Information extraction from electronic medical records using multitask recurrent neural network with contextual word embedding. Appl. Sci. 9(18), 3658 (2019)
Yang, Y., Xu, Z.: Rethinking the value of labels for improving class-imbalanced learning. NeurIPS 33, 19290–19301 (2020)
Zhao, J., Gu, S., McDermaid, A.: Predicting outcomes of chronic kidney disease from emr data based on random forest regression. In: Mathematical biosciences (2019)
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Li, Q., Chen, L., Cai, Y., Wu, D. (2023). Hierarchical Graph Neural Network for Patient Treatment Preference Prediction with External Knowledge. In: Kashima, H., Ide, T., Peng, WC. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2023. Lecture Notes in Computer Science(), vol 13937. Springer, Cham. https://doi.org/10.1007/978-3-031-33380-4_16
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