title: [Learning and prediction of a chaotic pattern related to the time-course of clinical laboratory data by artificial neural network].
abstract: We investigated whether an artificial neural network (ANN) could learn a chaotic pattern related to the time-course of some clinical laboratory data and predict its future pattern. Then, the patterns of serum ALT level over time in patients with chronic hepatitis were successively learned by ANN. Future patterns were also successively predicted. This procedure may be used to pick up an unexpected event in time-course of personal data, study the pathological background of disease, or application of prophylactic treatment.
authors.affiliation: Department of Clinico-Laboratory Diagnostics, Nara Medical University, Kashihara.
authors.forename: Y
authors.initials: Y
authors.lastname: Okamoto
authors.forename: H
authors.initials: H
authors.lastname: Nakano
authors.forename: H
authors.initials: H
authors.lastname: Matsuoka
authors.forename: T
authors.initials: T
authors.lastname: Tsujii
mesh_terms: D000410:Alanine Transaminase / Q000097:blood; D002908:Chronic Disease; D005544:Forecasting; D006505:Hepatitis / Q000097:blood*; D006801:Humans; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010338:Pathology, Clinical; D010363:Pattern Recognition, Automated; D013995:Time
chemical_list: D000410:Alanine Transaminase