Automating Vector Autoregression on Electronic Patient Diary Data

Ando Celino Emerencia, Lian Van Der Krieke, Elisabeth H. Bos, Peter De Jonge, Nicolai Petkov, Marco Aiello

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Finding the best vector autoregression model for any dataset, medical or otherwise, is a process that, to this day, is frequently performed manually in an iterative manner requiring a statistical expertize and time. Very few software solutions for automating this process exist, and they still require statistical expertize to operate. We propose a new application called Autovar, for the automation of finding vector autoregression models for time series data. The approach closely resembles the way in which experts work manually. Our proposal offers improvements over the manual approach by leveraging computing power, e.g., by considering multiple alternatives instead of choosing just one. In this paper, we describe the design and implementation of Autovar, we compare its performance against experts working manually, and we compare its features to those of the most used commercial solution available today. The main contribution of Autovar is to show that vector autoregression on a large scale is feasible. We show that an exhaustive approach for model selection can be relatively safe to use. This study forms an important step toward making adaptive, personalized treatment available and affordable for all branches of healthcare.

Original languageEnglish
Article number7038195
Pages (from-to)631-643
Number of pages13
JournalIEEE Journal of Biomedical and Health Informatics
Volume20
Issue number2
DOIs
Publication statusPublished - 1 Mar 2016
Externally publishedYes

Keywords

  • electronic patient diary data
  • statistical software
  • time series analysis
  • Vector autoregression

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