Multiscale analysis of long time-series medical databases.
Data mining in time-series medical databases has been receiving considerable attention since it provides a way of revealing useful information hidden in the database; for example relationships between the temporal course of examination results and onset time of diseases. This paper presents a new method for finding similar patterns in temporal sequences based on multiscale matching. Multiscale matching enables us the cross-scale comparison of sequences, namely, it enable us to compare temporal patterns by partially changing observation scales. We examined the usefulness of the method on the chronic hepatitis dataset and found some interesting patterns. On GPT sequences, we found patterns that may represent the effectiveness of interferon (IFN) treatment. On platelet count sequences, we found that, if IFN treatment was ineffective, platelet count kept decreasing following the progress of liver fibrosis, while it started increasing if the treatment was effective.
Hirano
Shoji
S
Department of Medical Informatics, Shimane Medical University, School of Medicine, Izumo, Shimane 693-8501, Japan.
Tsumoto
Shusaku
S
D000998:Antiviral Agents / Q000627:therapeutic use*; D019992:Databases as Topic; D019694:Hepatitis B, Chronic / Q000188:drug therapy*; D019698:Hepatitis C, Chronic / Q000188:drug therapy*; D006801:Humans; D016247:Information Storage and Retrieval; D007372:Interferons / Q000627:therapeutic use*; D008103:Liver Cirrhosis / Q000097:blood / Q000145:classification / Q000209:etiology; D010976:Platelet Count; D013995:Time
D000998:Antiviral Agents; D007372:Interferons