Abstract
Nonalcoholic Steatohepatitis (NASH), an advanced stage of Nonalcoholic Fatty Liver Disease (NAFLD) causes liver inflammation and can lead to cirrhosis. In this paper, we present a deep learning approach to identify patients at risk of developing NASH, given that they are suffering from NAFLD. For this, we created two sub cohorts within NASH (NASH suspected (NASH-S) and NASH biopsy-confirmed (NASH-B)) based on the availability of liver biopsy tests. We utilized medical codes from patient electronic medical records and augmented it with patient demographics to build a long short-term memory based NASH vs. NAFLD classifier. The model was trained and tested using five-fold cross-validation and compared with baseline models including XGBoost, random forest and logistic regression. An out-of-sample area under the precision-recall curve (AUPRC) of 0.61 was achieved in classifying NASH patients from NAFLD. When the same model was used to classify out-of-sample NASH-B cohort from NAFLD patients, a highest AUPRC of 0.53 was achieved which was better than other baseline methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ching, T., Himmelstein, D.S., Beaulieu-Jones, B.K., Kalinin, A.A., Do, B.T., Way, G.P., Ferrero, E., Agapow, P.M., Zietz, M., Hoffman, M.M., et al.: Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15(141), 20170387 (2018)
Choi, E., Bahadori, M.T., Schuetz, A., Stewart, W.F., Sun, J.: Doctor AI: predicting clinical events via recurrent neural networks. In: Machine Learning for Healthcare Conference, pp. 301–318 (2016)
Dyson, J.K., Anstee, Q.M., McPherson, S.: Non-alcoholic fatty liver disease: a practical approach to diagnosis and staging. Frontline Gastroenterol. 5(3), 211–218 (2014)
Farrell, G.C., Larter, C.Z.: Nonalcoholic fatty liver disease: from steatosis to cirrhosis. Hepatology 43(S1), S99–S112 (2006)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Michelotti, G.A., Machado, M.V., Diehl, A.M.: NAFLD, NASH and liver cancer. Nat. Rev. Gastroenterol. Hepatol. 10(11), 656 (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Neuschwander-Tetri, B.A., Brunt, E.M., Wehmeier, K.R., Oliver, D., Bacon, B.R.: Improved nonalcoholic steatohepatitis after 48 weeks of treatment with the ppar-\(\gamma \) ligand rosiglitazone. Hepatology 38(4), 1008–1017 (2003)
Ratziu, V., Goodman, Z., Sanyal, A.: Current efforts and trends in the treatment of NASH. J. Hepatol. 62(1), S65–S75 (2015)
Rotmensch, M., Halpern, Y., Tlimat, A., Horng, S., Sontag, D.: Learning a health knowledge graph from electronic medical records. Sci. Rep. 7(1), 5994 (2017)
Saria, S.: The digital patient: machine learning techniques for analyzing electronic health record data. Ph.D. thesis, Stanford University (2011)
Sumida, Y., Yoneda, M.: Current and future pharmacological therapies for NAFLD/NASH. J. Gastroenterol. 53(3), 362–376 (2018)
Wree, A., Broderick, L., Canbay, A., Hoffman, H.M., Feldstein, A.E.: From NAFLD to NASH to cirrhosis-new insights into disease mechanisms. Nat. Rev. Gastroenterol. Hepatol. 10(11), 627 (2013)
Younossi, Z., Anstee, Q.M., Marietti, M., Hardy, T., Henry, L., Eslam, M., George, J., Bugianesi, E.: Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention. Nat. Rev. Gastroenterol. Hepatol. 15(1), 11 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Suresha, P.B., Wang, Y., Xiao, C., Glass, L., Yuan, Y., Clifford, G.D. (2021). A Deep Learning Approach for Classifying Nonalcoholic Steatohepatitis Patients from Nonalcoholic Fatty Liver Disease Patients Using Electronic Medical Records. In: Shaban-Nejad, A., Michalowski, M., Buckeridge, D.L. (eds) Explainable AI in Healthcare and Medicine. Studies in Computational Intelligence, vol 914. Springer, Cham. https://doi.org/10.1007/978-3-030-53352-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-53352-6_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-53351-9
Online ISBN: 978-3-030-53352-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)