Your browser doesn't support javascript.
BiT-MAC: Mortality prediction by bidirectional time and multi-feature attention coupled network on multivariate irregular time series.
Wang, Qinfen; Chen, Geng; Jin, Xuting; Ren, Siyuan; Wang, Gang; Cao, Longbing; Xia, Yong.
  • Wang Q; National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, China.
  • Chen G; National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, China.
  • Jin X; Department of Critical Care Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710049, China.
  • Ren S; School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, China.
  • Wang G; Department of Critical Care Medicine, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710049, China.
  • Cao L; Engineering and IT, University of Technology Sydney, Sydney, 2007, Australia.
  • Xia Y; National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, China. Electronic address: yxia@nwpu.edu.cn.
Comput Biol Med ; 155: 106586, 2023 03.
Article in English | MEDLINE | ID: covidwho-2246202
ABSTRACT
Mortality prediction is crucial to evaluate the severity of illness and assist in improving the prognosis of patients. In clinical settings, one way is to analyze the multivariate time series (MTSs) of patients based on their medical data, such as heart rates and invasive mean arterial blood pressure. However, this suffers from sparse, irregularly sampled, and incomplete data issues. These issues can compromise the performance of follow-up MTS-based analytic applications. Plenty of existing methods try to deal with such irregular MTSs with missing values by capturing the temporal dependencies within a time series, yet in-depth research on modeling inter-MTS couplings remains rare and lacks model interpretability. To this end, we propose a bidirectional time and multi-feature attention coupled network (BiT-MAC) to capture the temporal dependencies (i.e., intra-time series coupling) and the hidden relationships among variables (i.e., inter-time series coupling) with a bidirectional recurrent neural network and multi-head attention, respectively. The resulting intra- and inter-time series coupling representations are then fused to estimate the missing values for a more robust MTS-based prediction. We evaluate BiT-MAC by applying it to the missing-data corrupted mortality prediction on two real-world clinical datasets, i.e., PhysioNet'2012 and COVID-19. Extensive experiments demonstrate the superiority of BiT-MAC over cutting-edge models, verifying the great value of the deep and hidden relations captured by MTSs. The interpretability of features is further demonstrated through a case study.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Journal: Comput Biol Med Year: 2023 Document Type: Article Affiliation country: J.compbiomed.2023.106586

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Journal: Comput Biol Med Year: 2023 Document Type: Article Affiliation country: J.compbiomed.2023.106586