Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer.

Journal: Artificial intelligence in medicine
Published Date: Mar 31, 2016

Abstract

OBJECTIVE: Machine learning techniques can be used to extract predictive models for diseases from electronic medical records (EMRs). However, the nature of EMRs makes it difficult to apply off-the-shelf machine learning techniques while still exploiting the rich content of the EMRs. In this paper, we explore the usage of a range of natural language processing (NLP) techniques to extract valuable predictors from uncoded consultation notes and study whether they can help to improve predictive performance.

Authors

  • Mark Hoogendoorn
    Vrije Universiteit Amsterdam, Department of Computer Science, De Boelelaan 1081, Amsterdam 1081 HV, The Netherlands.
  • Peter Szolovits
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Leon M G Moons
    Department of Gastroenterology and Hepatology, Utrecht University Medical Center, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands. Electronic address: l.m.g.moons@umcutrecht.nl.
  • Mattijs E Numans
    Department of Public Health and Primary Care, Leiden University Medical Center, Hippocratespad 21, 2333 ZD Leiden, The Netherlands. Electronic address: m.e.numans@lumc.nl.

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