Abstract
Clinical Phenotyping is a fundamental task in clinical services, which assessments whether a patient suffers a medical condition of interest. Existing works focus on learning better patients’ representations. Recently, multi-task learning has been proposed to transfer knowledge from different tasks and achieved promising performance. However, the existing multi-task models still suffer from the serious negative transfer and slow convergence problem when multiple phenotype tasks are trained together. Meanwhile, phenotype relatedness is ignored, limiting to boost the performance of the multi-task learning for the phenotype prediction. To address these issues, we propose a private-shared multitask framework with auxiliary task selection and adaptive shared-space correction for phenotype prediction (MTL_AC). To start with, we design an auxiliary task selection method to find the most compatible phenotype task against one task by using phenotype relatedness. And then, a novel adaptive shared-space correction mechanism is proposed to address the negative transfer and slow convergence problem when two tasks are jointly trained under the private-shared multitask framework. The experimental results show that the proposed method performs better on various phenotype prediction tasks.
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References
Ahuja, Y., Hong, C., Xia, Z., Cai, T.: Samgep: a novel method for prediction of phenotype event times using the electronic health record. medRxiv (2021)
Baytas, I.M., Xiao, C., Zhang, X., Wang, F., Jain, A.K., Zhou, J.: Patient subtyping via time-aware lstm networks. In: SIGKDD, pp. 65–74 (2017)
Birkhead, G.S., Klompas, M., Shah, N.R.: Uses of electronic health records for public health surveillance to advance public health. Annu. Rev. Public Health 36, 345–359 (2015)
Cao, Y., et al.: Kdtnet: medical image report generation via knowledge-driven transformer. In: DASFAA, p. 117–132 (2022)
Caruana, R.: Multitask learning. Mach. Learn. 28(1), 41–75 (1997)
Choi, E., Bahadori, M.T., Song, L., Stewart, W.F., Sun, J.: Gram: graph-based attention model for healthcare representation learning. In: SIGKDD, pp. 787–795 (2017)
Ding, D.Y., Simpson, C., Pfohl, S., Kale, D.C., Jung, K., Shah, N.H.: The effectiveness of multitask learning for phenotyping with electronic health records data. In: BIOCOMPUTING 2019: Proceedings of the Pacific Symposium, pp. 18–29 (2018)
Emrani, S., McGuirk, A., Xiao, W.: Prognosis and diagnosis of parkinson’s disease using multi-task learning. In: SIGKDD, pp. 1457–1466 (2017)
Harutyunyan, H., Khachatrian, H., Kale, D.C., Ver Steeg, G., Galstyan, A.: Multitask learning and benchmarking with clinical time series data. Sci. Data 6(1), 96 (2019)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Johnson, A.E., et al.: Mimic-iii, a freely accessible critical care database. Sci. data 3(1), 1–9 (2016)
Khedkar, S., Gandhi, P., Shinde, G., Subramanian, V.: Deep learning and explainable ai in healthcare using ehr. In: Deep Learning Techniques for Biomedical and Health Informatics, pp. 129–148 (2020)
Kung, P.N., Yin, S.S., Chen, Y.C., Yang, T.H., Chen, Y.N.: Efficient multi-task auxiliary learning: selecting auxiliary data by feature similarity. In: EMNLP, pp. 416–428 (2021)
Liu, N., Lu, P., Zhang, W., Wang, J.: Knowledge-aware deep dual networks for text-based mortality prediction. In: ICDE, pp. 1406–1417 (2019)
Liu, N., Zhang, W., Li, X., Yuan, H., Wang, J.: Coupled graph convolutional neural networks for text-oriented clinical diagnosis inference. In: DASFAA, pp. 369–385 (2020)
Liu, P., Qiu, X., Huang, X.: Adversarial multi-task learning for text classification. arXiv preprint arXiv:1704.05742 (2017)
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: SIGKDD, pp. 1903–1911 (2017)
Meng, Y., Speier, W.F., Ong, M.K., Arnold, C.: Bidirectional representation learning from transformers using multimodal electronic health record data to predict depression. IEEE J. Biomed. Health Inform. (2021)
Oellrich, A., et al.: The digital revolution in phenotyping. Brief. Bioinform. 17(5), 819–830 (2016)
Robinson, P.N.: Deep phenotyping for precision medicine. Hum. Mutat. 33(5), 777–780 (2012)
Sadek, R.M., et al.: Parkinson’s disease prediction using artificial neural network (2019)
Song, H., Rajan, D., Thiagarajan, J.J., Spanias, A.: Attend and diagnose: clinical time series analysis using attention models. In: AAAI (2018)
Wang, L., Zhang, W., He, X.: Continuous patient-centric sequence generation via sequentially coupled adversarial learning. In: DASFAA, pp. 36–52 (2019)
Wei, W.Q., Teixeira, P.L., Mo, H., Cronin, R.M., Warner, J.L., Denny, J.C.: Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance. J. Am. Med. Inform. Assoc. 23(e1), e20–e27 (2016)
Yang, Y., Zheng, X., Ji, C.: Disease prediction model based on bilstm and attention mechanism. In: BIBM, pp. 1141–1148 (2019)
Yu, F., Cui, L., Cao, Y., Liu, N., Huang, W., Xu, Y.: Similarity-aware collaborative learning for patient outcome prediction. In: DASFAA, pp. 407–422 (2022)
Yu, X., Li, G., Chai, C., Tang, N.: Reinforcement learning with tree-lstm for join order selection. In: ICDE (2020)
Yuan, H., Li, G.: Distributed in-memory trajectory similarity search and join on road network. In: ICDE, pp. 1262–1273 (2019)
Yuan, H., Li, G., Bao, Z., Feng, L.: Effective travel time estimation: when historical trajectories over road networks matter. In: SIGMOD, pp. 2135–2149 (2020)
Yuan, H., Li, G., Bao, Z., Feng, L.: An effective joint prediction model for travel demands and traffic flows. In: ICDE (2021)
Acknowledgement
This work is supported by the Fundamental Research Funds of Shandong University and partially supported by the NSFC (No. 91846205) the National Key R &D Program of China (No. 2021YFF0900800), the major Science and Technology Innovation of Shandong Province grant (No. 2021CXGC010108).
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Yang, X. et al. (2022). Clinical Phenotyping Prediction via Auxiliary Task Selection and Adaptive Shared-Space Correction. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13605. Springer, Cham. https://doi.org/10.1007/978-3-031-20500-2_36
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DOI: https://doi.org/10.1007/978-3-031-20500-2_36
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