Time to CARE: A collaborative engine for practical disease prediction

Darcy A. Davis, Nitesh V. Chawla, Nicholas A. Christakis, Albert László Barabási

Research output: Contribution to journalArticlepeer-review

Abstract (may include machine translation)

The monumental cost of health care, especially for chronic disease treatment, is quickly becoming unmanageable. This crisis has motivated the drive towards preventative medicine, where the primary concern is recognizing disease risk and taking action at the earliest signs. However, universal testing is neither time nor cost efficient. We propose CARE, a Collaborative Assessment and Recommendation Engine, which relies only on patient's medical history using ICD-9-CM codes in order to predict future disease risks. CARE uses collaborative filtering methods to predict each patient's greatest disease risks based on their own medical history and that of similar patients. We also describe an Iterative version, ICARE, which incorporates ensemble concepts for improved performance. Also, we apply time-sensitive modifications which make the CARE framework practical for realistic long-term use. These novel systems require no specialized information and provide predictions for medical conditions of all kinds in a single run. We present experimental results on a large Medicare dataset, demonstrating that CARE and ICARE perform well at capturing future disease risks.

Original languageEnglish
Pages (from-to)388-415
Number of pages28
JournalData Mining and Knowledge Discovery
Volume20
Issue number3
DOIs
StatePublished - May 2010
Externally publishedYes

Keywords

  • Collaborative filtering
  • Disease prediction
  • Electronic healthcare record
  • Prospective medicine

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