Diagnosis prediction from electronic health records (EHR) using the binary diagnosis history vector representation

Ieva Vasiljeva, Ognjen Arandelovic

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

Large amounts of rich, heterogeneous information nowadays routinely collected by health care providers across the world possess remarkable potential for the extraction of novel medical data and the assessment of different practices in real-world conditions. Specifically in this work our goal is to use Electronic Health Records (EHRs) to predict progression patterns of future diagnoses of ailments for a particular patient, given the patient’s present diagnostic history. Following the highly promising results of a recently proposed approach which introduced the diagnosis history vector representation of a patient’s diagnostic record, we introduce a series of improvements to the model and conduct thorough experiments that demonstrate its scalability, accuracy, and practicability in the clinical context. We show that the model is able to capture well the interaction between a large number of ailments which correspond to the most frequent diagnoses, show how the original learning framework can be adapted to increase its prediction specificity, and describe a principled, probabilistic method for incorporating explicit, human clinical knowledge to overcome semantic limitations of the raw EHR data.
Original languageEnglish
Pages (from-to)767-768
Number of pages20
JournalJournal of Computational Biology
Volume24
Issue number8
Early online date7 Jul 2017
DOIs
Publication statusPublished - 1 Aug 2017

Keywords

  • Bayesian
  • Disease
  • Electronic medical records
  • EMRs
  • Epidemiology
  • Risk

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