Decision-tree induction from time-series data based on standard-example split test

This paper proposes a novel decision tree for a data set with time-series attributes. Our time-series tree has a value (i.e. a time sequence) of a time-series attribute in its internal node, and splits examples based on dissimilarity between a pair of time sequences. Our method selects, for a split test, a time sequence which exists in data by exhaustive search based on class and shape information. Experimental results confirm that our induction method constructs comprehensive and accurate decision trees. Moreover, a medical application shows that our time-series tree is promising for knowledge discovery

Integration of Learning Methods, Medical Literature and Expert Inspection in Medical Data Mining

From lessons learned in medical data mining projects we show that integration of advanced computation techniques and human inspection is indispensable in medical data mining. We proposed an integrated approach that merges data mining and text mining methods plus visualization support for expert evaluation. We also appropriately developed temporal abstraction and text mining methods to exploit the collected data. Furthermore, our visual discovery system D2MS allowed to actively and effectively working with physicians. Significant findings in hepatitis study were obtained by the integrated approach

Estimation and Evaluation of Future Demand and Supply of Healthcare Services Based on a Patient Access Area Model

Accessibility to healthcare service providers, the quantity, and the quality of them are important for national health. In this study, we focused on geographic accessibility to estimate and evaluate future demand and supply of healthcare services. We constructed a simulation model called the patient access area model (PAAM), which simulates patients’ access time to healthcare service institutions using a geographic information system (GIS). Using this model, to evaluate the balance of future healthcare services demand and supply in small areas, we estimated the number of inpatients every five years in each area and compared it with the number of hospital beds within a one-hour drive from each area. In an experiment with the Tokyo metropolitan area as a target area, when we assumed hospital bed availability to be 80%, it was predicted that over 78,000 inpatients would not receive inpatient care in 2030. However, this number would decrease if we lowered the rate of inpatient care by 10% and the average length of the hospital stay. Using this model, recommendations can be made regarding what action should be undertaken and by when to prevent a dramatic increase in healthcare demand. This method can help plan the geographical resource allocation in healthcare services for healthcare policy

A Semi-Supervised Ensemble Learning Method for Finding Discriminative Motifs and its Application

Finding discriminative motifs has recently received much attention in biomedicine as such motifs allow us to characterize in distinguishing two different classes of sequences. It is common in biomedical applications that the quantity of labeled sequences is very limited while a large number of unlabeled sequences is usually available. The current methods of discriminative motif finding are powerful and effective with large labeled datasets, but they do not function well on small labeled datasets. In this paper, we present a semi-supervised ensemble method for finding discriminative motifs which is based on the SLUPC algorithm, a separate-and-conquer searching method to discover motifs of type `discriminative one occurrence per sequence'. The proposed method, named E-SLUPC (Ensemble SLUPC), uses SLUPC to search discriminative motifs from an extended labeled dataset that contains labeled data and unlabeled data with predicted labels. Strong discriminative and frequent motifs characterizing two outcome classes of hepatitis C virus treatment (sustained viral response and non-sustained viral response) were detected and analyzed. Furthermore, the experimental evaluation shows that our method can function considerably well in the common context of medical research when the labeled data is usually difficult to obtain