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

State Space Representation01:27

State Space Representation

547
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
547
Space Trusses01:25

Space Trusses

1.3K
A space truss is a three-dimensional counterpart of a planar truss. These structures consist of members connected at their ends, often utilizing ball-and-socket joints to create a stable and versatile framework. The space truss is widely used in various construction projects due to its adaptability and capacity to withstand complex loads.
At the core of a space truss lies the fundamental unit known as the tetrahedron. This structure is composed of six members that form a three-dimensional shape...
1.3K
Cluster Sampling Method01:20

Cluster Sampling Method

14.3K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
14.3K
Vesicular Tubular Clusters01:45

Vesicular Tubular Clusters

3.1K
After budding out from the ER membrane, some COPII vesicles lose their coat and fuse with one another to form larger vesicles and interconnected tubules called vesicular tubular clusters or VTCs. These clusters constitute a compartment at the ER-Golgi interface known as ERGIC (Endoplasmic Reticulum Golgi Intermediate Compartment). The ERGIC is a mobile membrane-bound cargo transport system that sorts proteins secreted from ER and delivers them to the Golgi.
With the help of motor proteins such...
3.1K
Transfer Function to State Space01:23

Transfer Function to State Space

778
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
778
State Space to Transfer Function01:21

State Space to Transfer Function

569
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
569

You might also read

Related Articles

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

Sort by
Same author

Computed tomography findings in 11,504 adult patients with traumatic brain injury: a large real-world cohort study with a S100B subgroup analysis.

Archives of orthopaedic and trauma surgery·2026
Same author

Clustering longitudinal multivariate trajectories using an ensemble of principal component trees.

BMC medical research methodology·2026
Same author

Transforming Annotated Clinical Narratives into Pruned Interoperable Knowledge Graphs with SNOMED CT.

Studies in health technology and informatics·2026
Same author

Standardized Annotation of Clinical Narratives with SNOMED CT and FHIR.

Studies in health technology and informatics·2026
Same author

Preliminary Investigation of Federated Learning for MACE Prediction from Electronic Medical Records: A Multicontinental Study.

Studies in health technology and informatics·2026
Same author

Ontology-Based Medication Named Entity Recognition Using Pretrained Transformer Models From a Thai Hospital: Model Fine-Tuning and Validation Study.

JMIR formative research·2026

Related Experiment Video

Updated: Jan 26, 2026

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function
06:17

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function

Published on: January 26, 2024

2.6K

EHR problem list clustering for improved topic-space navigation.

Markus Kreuzthaler1,2, Bastian Pfeifer3,4, Jose Antonio Vera Ramos3

  • 1Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria. markus.kreuzthaler@medunigraz.at.

BMC Medical Informatics and Decision Making
|April 5, 2019
PubMed
Summary
This summary is machine-generated.

This study developed a method to semantically group electronic health record problem list items, reducing data redundancy and improving physician access to patient information. The approach achieved an 80% compression rate, creating navigable disease topic spaces for efficient clinical review.

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.5K
In vitro Synthesis of Native, Fibrous Long Spacing and Segmental Long Spacing Collagen
07:54

In vitro Synthesis of Native, Fibrous Long Spacing and Segmental Long Spacing Collagen

Published on: September 20, 2012

14.2K

Related Experiment Videos

Last Updated: Jan 26, 2026

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function
06:17

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function

Published on: January 26, 2024

2.6K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.5K
In vitro Synthesis of Native, Fibrous Long Spacing and Segmental Long Spacing Collagen
07:54

In vitro Synthesis of Native, Fibrous Long Spacing and Segmental Long Spacing Collagen

Published on: September 20, 2012

14.2K

Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Data Science

Background:

  • Patient-related information in clinical systems grows, especially for chronic diseases.
  • Electronic health record problem lists are crucial but can become redundant due to length limits and coding requirements.
  • Physicians need efficient ways to overview patient histories amidst information overload.

Purpose of the Study:

  • To investigate a method for semantically grouping electronic health record problem list items.
  • To enable fast navigation through patient-related topic spaces for clinicians.
  • To address information overload in clinical settings.

Main Methods:

  • Applied a minimal language-dependent preprocessing strategy.
  • Mapped problem list entries as tf-idf weighted character 3-grams into a numerical vector space.
  • Utilized UPGMA clustering with cosine distances and LSA for dimensionality reduction to form semantic topic spaces.

Main Results:

  • Achieved an average compression rate of 80% for initial list items.
  • Formed consistent semantic topic spaces with an F-measure greater than 0.80.
  • Found that LSA-based feature space reduction did not significantly impact performance.

Conclusions:

  • Developed a data-driven solution to combat information overload in clinical workplaces.
  • Created navigable disease topic spaces by grouping related problem list items.
  • Facilitated easier access to patient topics, improving human-computer interaction for clinicians.