2 research outputs found
Patient Similarity Analysis with Longitudinal Health Data
Healthcare professionals have long envisioned using the enormous processing
powers of computers to discover new facts and medical knowledge locked inside
electronic health records. These vast medical archives contain time-resolved
information about medical visits, tests and procedures, as well as outcomes,
which together form individual patient journeys. By assessing the similarities
among these journeys, it is possible to uncover clusters of common disease
trajectories with shared health outcomes. The assignment of patient journeys to
specific clusters may in turn serve as the basis for personalized outcome
prediction and treatment selection. This procedure is a non-trivial
computational problem, as it requires the comparison of patient data with
multi-dimensional and multi-modal features that are captured at different times
and resolutions. In this review, we provide a comprehensive overview of the
tools and methods that are used in patient similarity analysis with
longitudinal data and discuss its potential for improving clinical decision
making
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