Nested Gaussian process modeling for high-dimensional data imputation in healthcare systems

Farhad Imani, Changqing Cheng, Ruimin Chen, Hui Yang

Research output: Contribution to conferencePaper

4 Scopus citations

Abstract

Surgical operation involves multifarious elements with a great level of complexity. To cope with this issue and enhance information visibility, modern healthcare systems are increasingly investing in advanced sensing and information technology which gives rise to data-rich environments in hospitals. However, it is not uncommon to encounter data uncertainty and incompleteness. This adversely affects the process of healthcare decisions (e.g., multiple attributes with missing values among describing a patient's health conditions). There is a dire need to go beyond current clinical practice and develop data-driven methods that will enable the extraction of pertinent knowledge about clinical status from heterogeneous healthcare recordings. This paper presents a novel nested Gaussian process for high-dimensional tensor data imputation. We evaluate and validate the proposed methodology with real tensor data with high-level of missing information from intensive care units. Experimental results show that the proposed method effectively handles the imputation of incomplete tensor data under uncertainty, further improves the effectiveness of biomarker extraction, patient monitoring, and decision making. The nested Gaussian process method shows great potentials for general application in many of other disciplines that need high-dimension data imputation and analytics.

Original languageEnglish (US)
Pages1312-1317
Number of pages6
StatePublished - Jan 1 2018
Event2018 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2018 - Orlando, United States
Duration: May 19 2018May 22 2018

Other

Other2018 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2018
CountryUnited States
CityOrlando
Period5/19/185/22/18

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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    Imani, F., Cheng, C., Chen, R., & Yang, H. (2018). Nested Gaussian process modeling for high-dimensional data imputation in healthcare systems. 1312-1317. Paper presented at 2018 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2018, Orlando, United States.