Interpretable time-aware and co-occurrence-aware network for medical prediction

Abstract Background Disease prediction based on electronic health records (EHRs) is essential for personalized healthcare. But it’s hard due to the special data structure and the interpretability requirement of methods. The structure of EHR is hierarchical: each patient has a sequence of admissions,...

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Autores principales:	Chenxi Sun, Hongna Dui, Hongyan Li
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Medical prediction Interpretable deep learning Electronic health records Disease correlation Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/05dcb3c64a9b4748815c61cc95dcf91c
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spelling	oai:doaj.org-article:05dcb3c64a9b4748815c61cc95dcf91c2021-11-08T10:59:22ZInterpretable time-aware and co-occurrence-aware network for medical prediction10.1186/s12911-021-01662-z1472-6947https://doaj.org/article/05dcb3c64a9b4748815c61cc95dcf91c2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12911-021-01662-zhttps://doaj.org/toc/1472-6947Abstract Background Disease prediction based on electronic health records (EHRs) is essential for personalized healthcare. But it’s hard due to the special data structure and the interpretability requirement of methods. The structure of EHR is hierarchical: each patient has a sequence of admissions, and each admission has some co-occurrence diagnoses. However, the existing methods only partially model these characteristics and lack the interpretation for non-specialists. Methods This work proposes a time-aware and co-occurrence-aware deep learning network (TCoN), which is not only suitable for EHR data structure but also interpretable: the co-occurrence-aware self-attention (CS-attention) mechanism and time-aware gated recurrent unit (T-GRU) can model multilevel relations; the interpretation path and the diagnosis graph can make the result interpretable. Results The method is tested on a real-world dataset for mortality prediction, readmission prediction, disease prediction, and next diagnoses prediction. Experimental results show that TCoN is better than baselines with 2.01% higher accuracy. Meanwhile, the method can give the interpretation of causal relationships and the diagnosis graph of each patient. Conclusions This work proposes a novel model—TCoN. It is an interpretable and effective deep learning method, that can model the hierarchical medical structure and predict medical events. The experiments show that it outperforms all state-of-the-art methods. Future work can apply the graph embedding technology based on more knowledge data such as doctor notes.Chenxi SunHongna DuiHongyan LiBMCarticleMedical predictionInterpretable deep learningElectronic health recordsDisease correlationComputer applications to medicine. Medical informaticsR858-859.7ENBMC Medical Informatics and Decision Making, Vol 21, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medical prediction Interpretable deep learning Electronic health records Disease correlation Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Medical prediction Interpretable deep learning Electronic health records Disease correlation Computer applications to medicine. Medical informatics R858-859.7 Chenxi Sun Hongna Dui Hongyan Li Interpretable time-aware and co-occurrence-aware network for medical prediction
description	Abstract Background Disease prediction based on electronic health records (EHRs) is essential for personalized healthcare. But it’s hard due to the special data structure and the interpretability requirement of methods. The structure of EHR is hierarchical: each patient has a sequence of admissions, and each admission has some co-occurrence diagnoses. However, the existing methods only partially model these characteristics and lack the interpretation for non-specialists. Methods This work proposes a time-aware and co-occurrence-aware deep learning network (TCoN), which is not only suitable for EHR data structure but also interpretable: the co-occurrence-aware self-attention (CS-attention) mechanism and time-aware gated recurrent unit (T-GRU) can model multilevel relations; the interpretation path and the diagnosis graph can make the result interpretable. Results The method is tested on a real-world dataset for mortality prediction, readmission prediction, disease prediction, and next diagnoses prediction. Experimental results show that TCoN is better than baselines with 2.01% higher accuracy. Meanwhile, the method can give the interpretation of causal relationships and the diagnosis graph of each patient. Conclusions This work proposes a novel model—TCoN. It is an interpretable and effective deep learning method, that can model the hierarchical medical structure and predict medical events. The experiments show that it outperforms all state-of-the-art methods. Future work can apply the graph embedding technology based on more knowledge data such as doctor notes.
format	article
author	Chenxi Sun Hongna Dui Hongyan Li
author_facet	Chenxi Sun Hongna Dui Hongyan Li
author_sort	Chenxi Sun
title	Interpretable time-aware and co-occurrence-aware network for medical prediction
title_short	Interpretable time-aware and co-occurrence-aware network for medical prediction
title_full	Interpretable time-aware and co-occurrence-aware network for medical prediction
title_fullStr	Interpretable time-aware and co-occurrence-aware network for medical prediction
title_full_unstemmed	Interpretable time-aware and co-occurrence-aware network for medical prediction
title_sort	interpretable time-aware and co-occurrence-aware network for medical prediction
publisher	BMC
publishDate	2021
url	https://doaj.org/article/05dcb3c64a9b4748815c61cc95dcf91c
work_keys_str_mv	AT chenxisun interpretabletimeawareandcooccurrenceawarenetworkformedicalprediction AT hongnadui interpretabletimeawareandcooccurrenceawarenetworkformedicalprediction AT hongyanli interpretabletimeawareandcooccurrenceawarenetworkformedicalprediction
_version_	1718442395662548992

Interpretable time-aware and co-occurrence-aware network for medical prediction

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