Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Health Information Technology and Healthcare Information System01:30

Health Information Technology and Healthcare Information System

Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cognitive Function and Cardiac Rehabilitation Attendance in Older Adults With Cardiovascular Disease Events: ATHEROSCLEROSIS RISK IN COMMUNITIES STUDY.

Journal of cardiopulmonary rehabilitation and prevention·2026
Same author

Use of Family Relationships in Commercial Claims Data to Characterize Clinical Events of Patients with <i>BRCA1/2</i> Cascade Testing.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same author

CAPABLE <i>Care + Connect</i>: A Qualitative Study to Adapt CAPABLE for Home-Based Primary Care and Foster Social Connection.

Journal of applied gerontology : the official journal of the Southern Gerontological Society·2026
Same author

Longitudinal TCR repertoires in ulcerative colitis patients show features distinguishing disease states.

Inflammatory bowel diseases·2026
Same author

A multi-metric evaluation of readability in psychiatric discharge summaries.

BioData mining·2026
Same author

A Framework to Quantify Disparities in Pharmacogenomic Treatment Concordance and Drug Response Outcomes.

Clinical and translational science·2026

Related Experiment Video

Updated: Jun 6, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

TASC: a time-aware sequence clustering framework with uncertainty quantification for electronic health record

April Yujie Yan1,2, Thomas K M Cudjoe3, Casey Overby Taylor4,5,6

  • 1Department of Biomedical Engineering, Johns Hopkins School of Medicine, 214 Hackerman Hall, 3101 Wyman Park Dr, Baltimore, MD, 21218, USA. yyan67@jhu.edu.

Biodata Mining
|June 5, 2026
PubMed
Summary

This study introduces a new framework, Time-Aware Sequence Clustering (TASC), to analyze complex patient health data over time. TASC effectively identifies distinct patient subgroups and their unique care journeys before surgery.

Keywords:
Association analysisElectronic health recordsLongitudinal clusteringMachine learningMusculoskeletal disordersPattern miningTotal knee replacement

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Related Experiment Videos

Last Updated: Jun 6, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

  • Health Informatics
  • Data Science
  • Biomedical Research

Background:

  • Longitudinal electronic health record (EHR) data present challenges in pattern discovery due to heterogeneity, sparsity, and irregularity.
  • Unsupervised learning methods struggle with quantifying uncertainty in temporal data patterns.

Purpose of the Study:

  • To develop and validate a Time-Aware Sequence Clustering (TASC) framework for identifying longitudinal patient trajectory patterns from EHR data.
  • To integrate temporal spacing, clinical semantic similarity, and probabilistic characterization for robust clustering.
  • To apply TASC to pre-surgical musculoskeletal care trajectories preceding total knee replacement (TKR).

Main Methods:

  • Applied TASC to EHR and survey data from 2,052 patients undergoing primary TKR.
  • Constructed temporally ordered sequences of musculoskeletal diagnoses and comorbidities.
  • Utilized a weighted edit-distance algorithm incorporating clinical similarity and time gaps, followed by K-Medoids clustering and stability analysis.
  • Quantified cluster membership uncertainty using probability-based subtype assignments and SoftMax transformation.

Main Results:

  • TASC identified stable and interpretable trajectory clusters from heterogeneous EHR data.
  • A five-cluster solution showed good stability (Adjusted Rand Index = 0.71) and distinct patient profiles.
  • Clusters differed in sociodemographics, diagnoses, time to surgery, and opioid use.
  • Fast progression to surgery was linked to younger patients with minimal comorbidity; complex chronic subtypes showed multimorbidity and long delays.

Conclusions:

  • TASC effectively captures heterogeneous longitudinal care-utilization patterns in musculoskeletal disease.
  • The framework provides uncertainty-aware clustering for longitudinal EHR data.
  • Findings support TASC's applicability to other clinical domains and event representations.