AliClu - Temporal sequence alignment for clustering longitudinal clinical data.

ABSTRACT: BACKGROUND:Patient stratification is a critical task in clinical decision making since it can allow physicians to choose treatments in a personalized way. Given the increasing availability of electronic medical records (EMRs) with longitudinal data, one crucial problem is how to efficiently cluster the patients based on the temporal information from medical appointments. In this work, we propose applying the Temporal Needleman-Wunsch (TNW) algorithm to align discrete sequences with the transition time information between symbols. These symbols may correspond to a patient's current therapy, their overall health status, or any other discrete state. The transition time information represents the duration of each of those states. The obtained TNW pairwise scores are then used to perform hierar

SUBMITTER: Rama K 

PROVIDER: S-EPMC6938005 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature