Abstract
Disease diagnosis plays an important role in the application of clinical decision system. When applying artificial intelligence models in disease diagnosis, one should be aware of the spurious correlations learned from the data. Such correlations could be introduced by the biased distribution of the data and would be reinforced during the optimization process. The spurious correlations will cause the model ultra-sensitive to some trivial variations in the input, and even introduce some significant errors when applied in non-independent and identically distributed (non-i.i.d) data with the training data. In this paper, we addressed this problem by applying the global balancing method to adjust the distribution balance in the learned representation space of the text via the assignment of global sample weights. By learning the global sample weights that minimized the balance loss, the text representation space had been iteratively optimized by attenuating the strength of the spurious correlations introduced by the training data. Our algorithm, TCGBR (Text Classification with the Global Balance Regularizer), showed the reduced sensitivity to the trivial variations of the input text and increased diagnosis accuracy when tested on the non-i.i.d data. Furthermore, we built the causal graph to investigate the causal relationships in established diagnostic models and found the diagnostic logic of TCGBR was more in line with the clinical common sense. The presented architecture can be used by practitioners to improve the stability of diagnostic models and the technique of global balancing can be naturally generalized to other representation-based deep learning models.
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References
Choy, G., et al.: Current applications and future impact of machine learning in radiology. Radiology 288(2), 318 (2018)
Coca-Perraillon, M.: Local and global optimal propensity score matching. In: SAS Global Forum. vol. 185, pp. 1–9 (2007)
De Fauw, J., et al.: Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24(9), 1342–1350 (2018)
DuGoff, E.H., Schuler, M., Stuart, E.A.: Generalizing observational study results: applying propensity score methods to complex surveys. Health Serv. Res. 49(1), 284–303 (2014)
Fenton, N.E., Neil, M., Constantinou, A.C.: The book of why: the new science of cause and effect, judea pearl, dana mackenzie. basic books (2018). Artif. Intell. 284, 103286 (2020)
Graber, M.L., Trowbridge, R., Myers, J.S., Umscheid, C.A., Strull, W., Kanter, M.H.: The next organizational challenge: finding and addressing diagnostic error. Joint Comm. J. Qual. Patient Saf. 40(3), 102–110 (2014)
Guan, C., Wang, X., Zhang, Q., Chen, R., He, D., Xie, X.: Towards a deep and unified understanding of deep neural models in NLP. In: International Conference on Machine Learning, pp. 2454–2463. PMLR (2019)
Jiang, X., et al.: Characteristics of online health care services from china’s largest online medical platform: cross-sectional survey study. J. Med. Internet Res. 23(4), e25817 (2021)
Kim, Y.: Convolutional neural networks for sentence classification. In: Moschitti, A., Pang, B., Daelemans, W. (eds.) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, 25–29 October 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1746–1751. ACL (2014)
Kuang, K., et al.: Treatment effect estimation via differentiated confounder balancing and regression. ACM Trans. Knowl. Discov. Data (TKDD) 14(1), 1–25 (2019)
Kuang, K., Xiong, R., Cui, P., Athey, S., Li, B.: Stable prediction with model misspecification and agnostic distribution shift. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 4485–4492 (2020)
Liang, H., et al.: Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat. Med. 25(3), 433–438 (2019)
Liberman, A.L., Newman-Toker, D.E.: Symptom-disease pair analysis of diagnostic error (spade): a conceptual framework and methodological approach for unearthing misdiagnosis-related harms using big data. BMJ Qual. Saf. 27(7), 557–566 (2018)
Pryzant, R., Shen, K., Jurafsky, D., Wagner, S.: Deconfounded lexicon induction for interpretable social science. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 1615–1625 (2018)
Richens, J.G., Lee, C.M., Johri, S.: Improving the accuracy of medical diagnosis with causal machine learning. Nat. Commun. 11(1), 1–9 (2020)
Rosenbaum, P.R., Rubin, D.B.: The central role of the propensity score in observational studies for causal effects. Biometrika 70(1), 41–55 (1983)
Rubin, D.B.: Causal inference using potential outcomes: design, modeling, decisions. J. Am. Stat. Assoc. 100(469), 322–331 (2005)
Shen, Z., Cui, P., Kuang, K., Li, B., Chen, P.: Causally regularized learning with agnostic data selection bias. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 411–419 (2018)
Singh, H., Meyer, A.N., Thomas, E.J.: The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving us adult populations. BMJ Qual. Saf. 23(9), 727–731 (2014)
Singh, H., Schiff, G.D., Graber, M.L., Onakpoya, I., Thompson, M.J.: The global burden of diagnostic errors in primary care. BMJ Qual. Saf. 26(6), 484–494 (2017)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search, 2nd edn. MIT Press, Adaptive computation and machine learning (2000)
Spirtes, P., Meek, C., Richardson, T.S.: Causal inference in the presence of latent variables and selection bias. In: Besnard, P., Hanks, S. (eds.) UAI 1995: Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence, Montreal, Quebec, Canada, 18–20 August 1995, pp. 499–506. Morgan Kaufmann (1995)
Wong, K.K., Fortino, G., Abbott, D.: Deep learning-based cardiovascular image diagnosis: a promising challenge. Futur. Gener. Comput. Syst. 110, 802–811 (2020)
Yao, L., Li, S., Li, Y., Xue, H., Gao, J., Zhang, A.: On the estimation of treatment effect with text covariates. In: International Joint Conference on Artificial Intelligence (2019)
Yu, B.: Three principles of data science: predictability, computability, and stability (PCS). In: Abe, N., et al. (eds.) IEEE International Conference on Big Data (IEEE BigData 2018), Seattle, WA, USA, 10–13 December 2018, p. 4. IEEE (2018)
Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. In: Advances in Neural Information Processing Systems, vol. 17 (2004)
Zhang, Z., et al.: Pathologist-level interpretable whole-slide cancer diagnosis with deep learning. Nat. Mach. Intell. 1(5), 236–245 (2019)
Zhu, R., Tu, X., Huang, J.: Using deep learning based natural language processing techniques for clinical decision-making with EHRs. In: Dash, S., Acharya, B.R., Mittal, M., Abraham, A., Kelemen, A. (eds.) Deep Learning Techniques for Biomedical and Health Informatics. SBD, vol. 68, pp. 257–295. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-33966-1_13
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The work is supported by Shenzhen City’s Science and Technology Plan Project (JSGG20210802153806021).
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Xu, Z. et al. (2023). Global Balanced Text Classification for Stable Disease Diagnosis. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14178. Springer, Cham. https://doi.org/10.1007/978-3-031-46671-7_15
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DOI: https://doi.org/10.1007/978-3-031-46671-7_15
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