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

Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Classification of Illness01:17

Classification of Illness

7.8K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
7.8K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

170
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
170
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

4.4K
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...
4.4K
Errors occurring during blood pressure monitoring01:25

Errors occurring during blood pressure monitoring

839
Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
Several factors...
839
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.8K
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...
11.8K

You might also read

Related Articles

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

Sort by
Same author

Sublytic C5b-9 triggers glomerular mesangial cell apoptosis in rat Thy-1 nephritis via Gadd45 activation mediated by Egr-1 and p300-dependent ATF3 acetylation.

Journal of molecular cell biology·2016
Same author

Improving the hydrogen selectivity of graphene oxide membranes by reducing non-selective pores with intergrown ZIF-8 crystals.

Chemical communications (Cambridge, England)·2016
Same author

The Association Between Genetic Polymorphism rs703842 in CYP27B1 and Multiple Sclerosis: A Meta-Analysis.

Medicine·2016
Same author

High-Flexibility, High-Toughness Double-Cross-Linked Chitin Hydrogels by Sequential Chemical and Physical Cross-Linkings.

Advanced materials (Deerfield Beach, Fla.)·2016
Same author

Synthesis, structure, and magnetic and catalytic properties of metal frameworks with 2,2'-dinitro-4,4'-biphenyldicarboxylate and imidazole-containing tripodal ligands.

Dalton transactions (Cambridge, England : 2003)·2016
Same author

Reactive oxygen species and hormone signaling cascades in endophytic bacterium induced essential oil accumulation in Atractylodes lancea.

Planta·2016

Related Experiment Video

Updated: Aug 22, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K

Time interval uncertainty-aware and text-enhanced based disease prediction.

Dan Zhao1, Yuliang Shi2, Lin Cheng1

  • 1School of Software, Shandong University, China.

Journal of Biomedical Informatics
|November 10, 2022
PubMed
Summary

This study introduces a novel deep learning model for disease prediction using electronic health records (EHR). The model accounts for non-linear time intervals and incorporates text data, improving prediction accuracy for common diseases.

Keywords:
AttentionDisease predictionEHR dataText informationTime interval

More Related Videos

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

317
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

667

Related Experiment Videos

Last Updated: Aug 22, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

317
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

667

Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Computational Biology

Background:

  • Electronic health records (EHR) are valuable for disease prediction.
  • Existing deep learning models often use a simplified, homogeneous decay for time intervals, which may not accurately reflect disease progression.
  • The rich text data within EHRs, detailing patient history and symptoms, is frequently underutilized in predictive models.

Purpose of the Study:

  • To develop an advanced deep learning model for disease prediction that addresses limitations in handling time intervals and text data from EHRs.
  • To propose a model that accounts for the non-monotonic and uncertain effects of time intervals on disease prediction.
  • To integrate patient text information into EHR-based disease prediction models.

Main Methods:

  • A novel model, the Time Interval Uncertainty-Aware and Text-Enhanced Based Disease Prediction Model, was developed.
  • A cross-attention mechanism was employed to create a global patient representation integrating disease and text information.
  • A key-query attention mechanism was utilized to determine the importance of visit sequences with and without time intervals, modifying the Transformer architecture.

Main Results:

  • The proposed model demonstrated improved performance compared to state-of-the-art methods on MIMIC-III and MIMIC-IV datasets.
  • On the MIMIC-III dataset, the model achieved up to a three percent improvement in evaluation metrics over the optimal baseline.
  • The model effectively leverages both temporal dynamics and textual information for enhanced disease prediction.

Conclusions:

  • The developed model offers a more nuanced approach to disease prediction by considering the complex nature of time intervals and the significance of EHR text data.
  • This approach has the potential to improve the accuracy and reliability of predictive models in clinical settings.
  • Future research can further explore the integration of diverse data types and advanced deep learning architectures for personalized disease prediction.