A double-layer interactive recurrent neural network method to predict hepatocellular carcinoma patient's survivability

Anqi Xu, Bo Wen, Paul Jen Hwa Hu, Ting Shuo Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Hepatocellular carcinoma (HCC) has high mortalities and warrants research efforts to examine its progression. Three components are central to the underlying cancer progression mechanisms: tumor, tumor microenvironment (TME), and their interactions. We develop a double-layer interactive recurrent neural network method that incorporates two interactive layers to account for the hidden tumor state and hidden TME state, and their interactions over time. Our method leverages deep learning to predict patient survivability with temporal clinical data, unlike existing data-driven techniques that primarily use static patient data. Our method can model both the hidden tumor state variable and the TME state variable to allow timely survivability predictions. A sample of 2,322 HCC patients from a major healthcare organization in Taiwan is used to evaluate the proposed method and several prevalent techniques. In line with previous research, we measure prediction performance with mean absolute error and concordance index.

Original languageEnglish (US)
Title of host publication26th Americas Conference on Information Systems, AMCIS 2020
PublisherAssociation for Information Systems
ISBN (Electronic)9781733632546
StatePublished - 2020
Externally publishedYes
Event26th Americas Conference on Information Systems, AMCIS 2020 - Salt Lake City, Virtual, United States
Duration: Aug 10 2020Aug 14 2020

Publication series

Name26th Americas Conference on Information Systems, AMCIS 2020

Conference

Conference26th Americas Conference on Information Systems, AMCIS 2020
Country/TerritoryUnited States
CitySalt Lake City, Virtual
Period8/10/208/14/20

Keywords

  • Data-driven healthcare
  • Double-layer interactive RNN
  • HCC
  • Tumor
  • Tumor microenvironment

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Computer Networks and Communications
  • Library and Information Sciences

Fingerprint

Dive into the research topics of 'A double-layer interactive recurrent neural network method to predict hepatocellular carcinoma patient's survivability'. Together they form a unique fingerprint.

Cite this