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

ABSTRACT: Time is a crucial parameter in the assessment of comorbidities in population-based studies, as it permits to identify more complex disease patterns apart from the pairwise disease associations. So far, it has been, either, completely ignored or only, taken into account by assessing the temporal directionality of identified comorbidity pairs. In this work, a novel time-analysis framework is presented for large-scale comorbidity studies. The disease-history vectors of patients of a regional Spanish health dataset are represented as time sequences of ordered disease diagnoses. Statistically significant pairwise disease associations are identified and their temporal directionality is assessed. Subsequently, an unsupervised clustering algorithm, based on Dynamic Time Warping, is applied on the common disease trajectories in order to group them according to the temporal patterns that they share. The proposed methodology for the temporal assessment of such trajectories could serve as the preliminary basis of a disease prediction system.

SUBMITTER: Giannoula A

PROVIDER: S-EPMC5844976 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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

Giannoula Alexia A Gutierrez-Sacristán Alba A Bravo Álex Á Sanz Ferran F Furlong Laura I LI

Scientific reports 20180309 1

Time is a crucial parameter in the assessment of comorbidities in population-based studies, as it permits to identify more complex disease patterns apart from the pairwise disease associations. So far, it has been, either, completely ignored or only, taken into account by assessing the temporal directionality of identified comorbidity pairs. In this work, a novel time-analysis framework is presented for large-scale comorbidity studies. The disease-history vectors of patients of a regional Spanis ...[more]

PMID: 29523868

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Project description:Post-stroke complications are the second most frequent cause of death and the third leading cause of disability worldwide. The motor function of post-stroke patients is often assessed by measuring the postural sway in the patients during quiet standing, based on sway measures, such as sway area and velocity, which are obtained from temporal variations of the center of pressure. However, such approaches to establish a relationship between the sway measures and patients' demographic factors have hardly been successful (e.g., days after onset). This study instead evaluates the postural sway features of post-stroke patients using the clustering method of machine learning. First, we collected the stroke patients' multi-variable motion-capture standing-posture data and processed them into t s long data slots. Then, we clustered the t-s data slots into K cluster groups using the dynamic-time-warping partition-around-medoid (DTW-PAM) method. The DTW measures the similarity between two temporal sequences that may vary in speed, whereas PAM identifies the centroids for the DTW clustering method. Finally, we used a post-hoc test and found that the sway amplitudes of markers in the shoulder, hip, knee, and center-of-mass are more important than their sway frequencies. We separately plotted the marker amplitudes and frequencies in the medial-lateral direction during a 5-s data slot and found that the post-stroke patients' postural sway frequency lay within the bandwidth of 0.5-1.5 Hz. Additionally, with an increase in the onset days, the cluster index of cerebral hemorrhage patients gradually transits in a four-cluster solution. However, the cerebral infarction patients did not exhibit such pronounced transitions over time. Moreover, we found that the postural-sway amplitude increased in clusters 1, 3, and 4. However, the amplitude of cluster 2 did not follow this pattern, owing to age effects related to the postural sway changes with age. A rehabilitation doctor can utilize these findings as guidelines to direct the post-stroke patient training.

Dataset Information

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

Publications

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

Similar Datasets

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