ArticleBMC medicine2020
Understanding personalized dynamics to inform precision medicine: a dynamic time warp analysis of 255 depressed inpatients.
Article in BMC medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Subtyping OCD based on individual symptom networks: subtype-specific neural dynamics and morphometric similarity linked to molecular profiles.Molecular psychiatry · 2026Article
- Emotional dynamics during glucocorticoid treatment in chronic cluster headache - Insights from dynamic time warp analysis.Comprehensive psychoneuroendocrinology · 2026Article
- What Changes First? Mapping the Temporal Ordering of Age-Related Functional Decline across Domains Using 30-Year Longitudinal Data.Gerontology · 2026Article
- Dynamic time warping network analysis of posttraumatic stress symptoms in Japanese first responders.European journal of psychotraumatology · 2025Article
- Dynamic Time Warp (DTW) as a scalable, data-efficient, and clinically relevant analysis of dynamic processes in patients with psychiatric disorders: a tutorial.Journal of eating disorders · 2025Article
- Observational
- The Potential of Personalized Post-Traumatic Stress Disorder Networks.Psychiatry and clinical psychopharmacology · 2025Article
- Understanding Personalized Dynamics in Eating Disorders: A Dynamic Time Warp Analysis.The International journal of eating disorders · 2025Article
- Group-Based Trajectory Modeling Identifies Distinct Patterns of Sympathetic Hyperactivity Following Traumatic Brain Injury.Neurocritical care · 2025Observational
- Dynamic time warp versus vector autoregression models for network analyses of psychological processes.Scientific reports · 2025Article
- Data analysis strategies for the Accelerating Medicines Partnership® Schizophrenia Program.Schizophrenia (Heidelberg, Germany) · 2025Article
- The Role of Viral Interference in Shaping RSV Epidemics Following the 2009 H1N1 Influenza Pandemic.Influenza and other respiratory viruses · 2025Article
- Clustering and drivers of symptoms observed at week six after antidepressant treatment in depressed outpatients.European psychiatry : the journal of the Association of European Psychiatrists · 2025Article
- Time dynamics of symptom progression in patients with acute pancreatitis: a Dynamic Time Warping analysis.Frontiers in medicine · 2025Article
- Network dynamics of self-compassion, anxiety, and depression during eating disorder therapy.European eating disorders review : the journal of the Eating Disorders Association · 2025Article
- Dynamic time warp of emotions in patients with cutaneous T-cell lymphoma treated with corticosteroids.JAAD international · 2024Article
- Unsupervised clustering of longitudinal clinical measurements in electronic health records.PLOS digital health · 2024Article
- Ecological momentary assessment (EMA) combined with unsupervised machine learning shows sensitivity to identify individuals in potential need for psychiatric assessment.European archives of psychiatry and clinical neuroscience · 2024Article
- Group based trajectory modeling identifies distinct patterns of sympathetic hyperactivity following traumatic brain injury.Research square · 2024Article
- Temporal dynamics of depressive symptoms and cognitive decline in the oldest old: dynamic time warp analysis of the Leiden 85-plus study.Age and ageing · 2024Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMajor depressive disorder (MDD) shows large heterogeneity of symptoms between patients, but within patients, particular symptom clusters may show similar trajectories. While symptom clusters and networks have mostly been studied using cross-sectional designs, temporal dynamics of symptoms within patients may yield information that facilitates personalized medicine. Here, we aim to cluster depressive symptom dynamics through dynamic time warping (DTW) analysis.
methodsThe 17-item Hamilton Rating Scale for Depression (HRSD-17) was administered every 2 weeks for a median of 11 weeks in 255 depressed inpatients. The DTW analysis modeled the temporal dynamics of each pair of individual HRSD-17 items within each patient (i.e., 69,360 calculated "DTW distances"). Subsequently, hierarchical clustering and network models were estimated based on similarities in symptom dynamics both within each patient and at the group level.
resultsThe sample had a mean age of 51 (SD 15.4), and 64.7% were female. Clusters and networks based on symptom dynamics markedly differed across patients. At the group level, five dynamic symptom clusters emerged, which differed from a previously published cross-sectional network. Patients who showed treatment response or remission had the shortest average DTW distance, indicating denser networks with more synchronous symptom trajectories.
conclusionsSymptom dynamics over time can be clustered and visualized using DTW. DTW represents a promising new approach for studying symptom dynamics with the potential to facilitate personalized psychiatric care.
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