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AliClu - Temporal sequence alignment for clustering longitudinal clinical data.

Kishan Rama1,2, Helena Canhão3, Alexandra M Carvalho1

  • 1Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, Avenida Rovisco Pais, 1 - Torre Norte Piso 10., Lisboa, 1049-001, Portugal.

BMC Medical Informatics and Decision Making
|January 1, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces AliClu, a new tool for patient stratification using temporal clinical data. AliClu employs the Temporal Needleman-Wunsch algorithm to cluster patients based on treatment history, enabling personalized medicine.

Keywords:
BootstrapClusteringTemporal sequence alignmentclustering indices

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Health informatics

Background:

  • Patient stratification is crucial for personalized medicine, requiring efficient analysis of longitudinal electronic medical records (EMRs).
  • Clustering patients based on temporal data from medical appointments is a key challenge.
  • Existing methods often struggle to incorporate the time-series nature of patient health trajectories.

Purpose of the Study:

  • To develop and validate a novel computational tool, AliClu, for clustering temporal clinical data.
  • To enable patient stratification by analyzing longitudinal EMRs, focusing on temporal therapy profiles.
  • To improve clinical decision-making through personalized treatment strategies.

Main Methods:

  • Application of the Temporal Needleman-Wunsch (TNW) algorithm for sequence alignment with transition time information.
  • Hierarchical clustering using TNW pairwise scores.
  • Resampling techniques for determining optimal cluster numbers and assessing stability.
  • Bootstrapping for clustering validity assessments.

Main Results:

  • The AliClu tool was successfully applied to rheumatoid arthritis EMRs from the Reuma.pt database.
  • Analysis of therapy switches, including biologic drug durations, enabled patient stratification.
  • Optimized clusters revealed temporal therapy profiles and common features among patient groups.

Conclusions:

  • AliClu offers a promising computational strategy for analyzing longitudinal patient data and uncovering clinical outcome patterns.
  • The tool facilitates automatic or semi-automatic patient stratification by allowing parameter tuning.
  • Validated clusters and identified patterns support personalized medicine and clinical decision-making.