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

Longitudinal Research02:20

Longitudinal Research

13.1K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
13.1K
Longitudinal Studies01:26

Longitudinal Studies

481
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
481
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.1K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.1K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.6K
3.6K
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

249
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
249
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

502
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
502

You might also read

Related Articles

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

Sort by
Same author

Digital phenotyping with large language models to detect depressive state changes in patients.

NPJ digital medicine·2026
Same author

Association and risk prediction of 19 complex diseases with polygenic scores and socioeconomic status.

Communications medicine·2026
Same author

Assessing the quality and performance of synthetic data augmentation to identify stigmatizing language in obstetric clinical notes.

Nursing outlook·2026
Same author

Documentation of Stigmatizing Language in Electronic Health Records and Birth Outcomes.

Health equity·2026
Same author

Genetic regulation across germline and somatic variation on the Y chromosome contributes to type 2 diabetes.

Nature medicine·2026
Same author

Heterogeneous associations of polygenic indices of 35 traits with mortality: a register-linked population-based follow-up study.

eLife·2026

Related Experiment Videos

Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model.

Hans Moen1, Vishnu Raj1, Andrius Vabalas2

  • 1Department of Computer Science, Aalto University, Espoo, Finland.

Annals of Epidemiology
|September 13, 2025
PubMed
Summary
This summary is machine-generated.

Deep learning model Evolve analyzes health trajectories for continuous monitoring and early disease detection. It shows promise for personalized healthcare by identifying predictive events and health shifts over time.

Keywords:
Deep learningDisease predictionEHRLongitudinal health trajectoriesTransformers

Related Experiment Videos

Area of Science:

  • Health informatics
  • Biomedical data science
  • Machine learning in healthcare

Background:

  • Health registers offer longitudinal data crucial for understanding individual health trajectories.
  • Analyzing complex health data requires advanced computational methods to extract meaningful insights.

Purpose of the Study:

  • To explore deep learning for modeling and analyzing individual health trajectories using nationwide longitudinal data.
  • To introduce and evaluate the Evolve model for continuous health trajectory analysis and disease prediction.

Main Methods:

  • Developed Evolve, a transformer-based deep learning model for time-series multi-label prediction.
  • Conditioned predictions on historical health data and forecast windows for disease onset prediction.
  • Analyzed health trajectories by tracking prediction probability changes and latent embedding neighborhood shifts.

Main Results:

  • Evolve demonstrated comparable disease onset prediction performance to baseline models.
  • The model successfully identified early predictive events and shifts in health trajectories via embedding space changes.
  • Visualizations revealed how individuals' health profiles evolve and converge with similar outcomes over time.

Conclusions:

  • The Evolve model shows potential for continuous health monitoring and early disease detection.
  • It facilitates retrospective analysis of health trajectories, aiding personalized healthcare interventions.
  • The model's code is publicly available for further research and application.