Predicting Comorbid Conditions and Trajectories Using Social Health Records

Xiang Ji, Soon Ae Chun, James Geller

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Many patients suffer from comorbidity conditions; for example, obese patients often develop type-2 diabetes and hypertension. In the U.S., 80% of Medicare spending is for managing patients with these multiple coexisting conditions. Predicting potential comorbidity conditions for an individual patient can promote preventive care and reduce costs. Predicting possible comorbidity progression paths can provide important insights into population heath and aid with decisions in public health policies. Discovering the comorbidity relationships is complex and difficult, due to limited access to electronic health records by privacy laws. In this paper, we present a collaborative comorbidity prediction method to predict likely comorbid conditions for individual patients, and a trajectory prediction graph model to reveal progression paths of comorbid conditions. Our prediction approaches utilize patient generated health reports on online social media, called social health records (SHR). The experimental results based on one SHR source show that our method is able to predict future comorbid conditions for a patient with coverage values of 48% and 75% for a top-20 and a top-100 ranked list, respectively. For risk trajectory prediction, our approach is able to reveal each potential progression trajectory between any two conditions and infer the confidence of the future trajectory, given any observed condition. The predicted trajectories are validated with existing comorbidity relations from the medical literature.

Original languageEnglish (US)
Article number7465843
Pages (from-to)371-379
Number of pages9
JournalIEEE Transactions on Nanobioscience
Volume15
Issue number4
DOIs
StatePublished - Jun 2016

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Bioengineering
  • Medicine (miscellaneous)
  • Biomedical Engineering
  • Pharmaceutical Science
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Collaborative prediction
  • comorbidity prediction
  • disease progression
  • mining social media
  • trajectory prediction

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