How can machine-learning methods assist in virtual screening for hyperuricemia? A healthcare machine-learning approach.

Journal: Journal of biomedical informatics
PMID: 27658886

Abstract

OBJECT: Our purpose was to develop a new machine-learning approach (a virtual health check-up) toward identification of those at high risk of hyperuricemia. Applying the system to general health check-ups is expected to reduce medical costs compared with administering an additional test.

Authors

  • Daisuke Ichikawa
    SUSMED, Inc., Tokyo, Japan.
  • Toki Saito
    Dep. of Clinical Information Engineering, Division of Social Medicine, Graduate School of Medicine, Univ. of Tokyo, Japan.
  • Waka Ujita
    Dep. of Clinical Information Engineering, Division of Social Medicine, Graduate School of Medicine, Univ. of Tokyo, Japan.
  • Hiroshi Oyama
    Department of Clinical Information Engineering, Division of Social Medicine, Graduate School of Medicine, the University of Tokyo, Bunkyo-ku, Tokyo, Japan; Department of Clinical Information Engineering, School of Public Health, Graduate School of Medicine, the University of Tokyo, Bunkyo-ku, Tokyo, Japan.

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