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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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Identifying temporal patterns in patient disease trajectories using dynamic time warping: A population-based study.

Alexia Giannoula¹, Alba Gutierrez-Sacristán¹, Álex Bravo¹

¹Research Program on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), DCEXS, Universitat Pompeu Fabra, Barcelona, Spain.

Scientific Reports

|March 11, 2018

Summary

This summary is machine-generated.

This study introduces a new time-analysis framework to understand complex disease patterns in large populations. It groups patient disease histories by temporal patterns, aiding in disease prediction.

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Area of Science:

Computational epidemiology
Health informatics
Biostatistics

Background:

Assessing comorbidities in population studies typically ignores or simplifies the temporal aspect of disease progression.
Understanding the sequence and timing of diseases is vital for identifying complex comorbidity patterns beyond simple associations.

Purpose of the Study:

To present a novel time-analysis framework for large-scale comorbidity studies.
To analyze temporal disease trajectories and group them based on shared patterns.
To lay the groundwork for a disease prediction system.

Main Methods:

Representing patient disease histories as time sequences of ordered diagnoses.
Identifying statistically significant pairwise disease associations and assessing their temporal directionality.
Applying an unsupervised clustering algorithm based on Dynamic Time Warping to common disease trajectories.

Main Results:

A novel framework for analyzing temporal comorbidity patterns was developed.
Disease trajectories were clustered based on shared temporal sequences.
The methodology allows for a more nuanced understanding of disease progression over time.

Conclusions:

Temporal analysis of disease histories provides deeper insights into comorbidity patterns.
The proposed framework can group complex disease trajectories effectively.
This approach has potential applications in developing predictive health systems.