An interpretable outcome prediction model based on electronic health records and hierarchical attention

International Journal of Intelligent SystemsVolume 37, Issue 6 p. 3460-3479 RESEARCH ARTICLE An interpretable outcome prediction model based on electronic health records and hierarchical attention Juan Du, Juan Du orcid.org/0000-0001-5057-5361 Department of Gastroenterology, First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, ChinaSearch for more papers by this authorDajian Zeng, Dajian Zeng orcid.org/0000-0001-7897-4268 College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, ChinaSearch for more papers by this authorZhao Li, Zhao Li orcid.org/0000-0002-2954-578X Department of Gastroenterology and Hepatobiliary, Ri Zhao Hospital of Traditional Chinese Medicine, Rizhao, ChinaSearch for more papers by this authorJingxuan Liu, Jingxuan Liu orcid.org/0000-0002-4805-0707 College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, ChinaSearch for more papers by this authorMingqi Lv, Corresponding Author Mingqi Lv mingqilv@zjut.edu.cn orcid.org/0000-0003-4810-7491 College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China Correspondence Mingqi Lv, College of Computer Science and Technology, Zhejiang University of Technology, 310027 Hangzhou, China. Email: mingqilv@zjut.edu.cnSearch for more papers by this authorLing Chen, Ling Chen orcid.org/0000-0003-1934-5992 College of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaSearch for more papers by this authorDan Zhang, Dan Zhang orcid.org/0000-0003-1295-4795 Key Laboratory of Reproductive Genetics (Ministry of Education), Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, ChinaSearch for more papers by this authorShouling Ji, Shouling Ji orcid.org/0000-0003-4268-372X College of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaSearch for more papers by this author Juan Du, Juan Du orcid.org/0000-0001-5057-5361 Department of Gastroenterology, First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, ChinaSearch for more papers by this authorDajian Zeng, Dajian Zeng orcid.org/0000-0001-7897-4268 College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, ChinaSearch for more papers by this authorZhao Li, Zhao Li orcid.org/0000-0002-2954-578X Department of Gastroenterology and Hepatobiliary, Ri Zhao Hospital of Traditional Chinese Medicine, Rizhao, ChinaSearch for more papers by this authorJingxuan Liu, Jingxuan Liu orcid.org/0000-0002-4805-0707 College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, ChinaSearch for more papers by this authorMingqi Lv, Corresponding Author Mingqi Lv mingqilv@zjut.edu.cn orcid.org/0000-0003-4810-7491 College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China Correspondence Mingqi Lv, College of Computer Science and Technology, Zhejiang University of Technology, 310027 Hangzhou, China. Email: mingqilv@zjut.edu.cnSearch for more papers by this authorLing Chen, Ling Chen orcid.org/0000-0003-1934-5992 College of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaSearch for more papers by this authorDan Zhang, Dan Zhang orcid.org/0000-0003-1295-4795 Key Laboratory of Reproductive Genetics (Ministry of Education), Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, ChinaSearch for more papers by this authorShouling Ji, Shouling Ji orcid.org/0000-0003-4268-372X College of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaSearch for more papers by this author First published: 11 October 2021 https://doi.org/10.1002/int.22697Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Outcome prediction aims to predict the future health condition of patients from Electronic Health Record (EHR) data. Because of the sequential characteristic of EHR data, recurrent neural network (RNN)-based outcome prediction methods have achieved state-of-the-art results. However, the major drawback of RNN-based outcome prediction methods is lack of interpretability, which would lead to trust issues. Aiming at this problem, this paper proposes interpretable outcome prediction model with hierarchical attention (IoHAN), an interpretable outcome prediction model by leveraging attention mechanism. The main novelty of IoHAN is that it can pinpoint the fine-grained influence on the final prediction result of each medical component by decomposing the attention weights hierarchically into hospital visits, medical variables, and interactions between medical variables. We evaluated IoHAN on MIMIC-III, a large real-world EHR data set. The experiment results demonstrate that IoHAN can achieve higher prediction accuracy than state-of-the-art outcome prediction models. In addition, the hierarchical decomposed attention weights can interpret the prediction results in a more natural and understandable way. CONFLICT OF INTERESTS The authors declare that there are no conflict of interests. Volume37, Issue6June 2022Pages 3460-3479 RelatedInformation