title: Predicting disease progression from short biomarker series using expert advice algorithm.
abstract: Well-trained clinicians may be able to provide diagnosis and prognosis from very short biomarker series using information and experience gained from previous patients. Although mathematical methods can potentially help clinicians to predict the progression of diseases, there is no method so far that estimates the patient state from very short time-series of a biomarker for making diagnosis and/or prognosis by employing the information of previous patients. Here, we propose a mathematical framework for integrating other patients' datasets to infer and predict the state of the disease in the current patient based on their short history. We extend a machine-learning framework of "prediction with expert advice" to deal with unstable dynamics. We construct this mathematical framework by combining expert advice with a mathematical model of prostate cancer. Our model predicted well the individual biomarker series of patients with prostate cancer that are used as clinical samples.
authors.affiliation: Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan.
authors.forename: Kai
authors.initials: K
authors.lastname: Morino
authors.affiliation: 1] Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan [2] Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan.
authors.forename: Yoshito
authors.initials: Y
authors.lastname: Hirata
authors.affiliation: Toyota Technological Institute at Chicago, Chicago, Illinois 60637, USA.
authors.forename: Ryota
authors.initials: R
authors.lastname: Tomioka
authors.affiliation: Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan.
authors.forename: Hisashi
authors.initials: H
authors.lastname: Kashima
authors.affiliation: 1] Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan [2] CREST, JST, Honcho, Kawaguchi, Saitama 332-0012, Japan.
authors.forename: Kenji
authors.initials: K
authors.lastname: Yamanishi
authors.affiliation: Department of Urology, Jikei University School of Medicine, Tokyo 105-8461, Japan.
authors.forename: Norihiro
authors.initials: N
authors.lastname: Hayashi
authors.affiliation: Department of Urology, Jikei University School of Medicine, Tokyo 105-8461, Japan.
authors.forename: Shin
authors.initials: S
authors.lastname: Egawa
authors.affiliation: 1] Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan [2] Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan.
authors.forename: Kazuyuki
authors.initials: K
authors.lastname: Aihara
mesh_terms: D000465:Algorithms; D015415:Biomarkers; D018450:Disease Progression; D006801:Humans; D008962:Models, Theoretical
chemical_list: D015415:Biomarkers