Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Steps in Outbreak Investigation

Steps in Outbreak Investigation

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:

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Language Development

Language Development

Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...

You might also read

Related Articles

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

Sort by

Same author

GWAS analysis of a depression cohort defined by an EHR-phenotyping algorithm reveals the role of immune regulations in depression risk.

Frontiers in genetics·2026

Same author

Identifying anaphylaxis using weakly-supervised prediction models and natural language processing.

medRxiv : the preprint server for health sciences·2026

Same author

Early Feasibility of Registration of Micro-PET/CT Scans to Annotated 3D Specimen Models.

Head & neck·2026

Same author

The Big Mo: staying on the wave in an age of artificial intelligence.

Journal of the American Medical Informatics Association : JAMIA·2026

Same author

Multilingual Evaluation of a Large Language Model-Based Primary Care Chatbot.

medRxiv : the preprint server for health sciences·2026

Same author

Machine Learning Estimation of Gestational Age at Delivery Using Linked Mother-Infant Electronic Health Records Across Two Health Systems.

medRxiv : the preprint server for health sciences·2026

Same journal

Peripheral B-cell receptor repertoire predicts immune-related adverse events following immune checkpoint inhibitor therapy in advanced renal cell carcinoma.

Scientific reports·2026

Same journal

Effects of black soldier fly (Hermetia illucens L.) larvae zoocompost on the mineral element content of blue honeysuckle berries.

Scientific reports·2026

Same journal

Investigation on absorption refrigeration performance of R1243zf with imidazolium ionic liquid as the working pairs.

Scientific reports·2026

Same journal

DeepTriage-CN: integrating clinical text with vital signs for emergency department admission prediction in an aging population.

Scientific reports·2026

Same journal

Gold nanoparticles as dual-action antiviral agents: disruption of SARS-CoV-2 viral envelopes and RNA integrity.

Scientific reports·2026

Same journal

Comparison of capillary microsampling and venous blood for multi-pathogen serosurveillance.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 11, 2025

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

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

Published on: December 6, 2024

Scalable incident detection via natural language processing and probabilistic language models.

Colin G Walsh^1,2,3,4, Drew Wilimitis⁵, Qingxia Chen^5,6

¹Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA. Colin.walsh@vumc.org.

Scientific Reports

|October 8, 2024

Summary

This summary is machine-generated.

This study introduces a new method using natural language processing (NLP) on clinical notes to identify health events like suicide attempts and sleep behaviors. The approach shows promise for large-scale safety surveillance but requires careful monitoring for bias.

More Related Videos

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

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

Published on: May 15, 2020

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Related Experiment Videos

Last Updated: Jun 11, 2025

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

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

Published on: December 6, 2024

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

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

Published on: May 15, 2020

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Area of Science:

Medical Informatics
Natural Language Processing
Pharmacovigilance

Background:

Post-marketing safety surveillance is crucial for detecting clinical events.
Current methods using structured data have limitations in precision and completeness.
Natural Language Processing (NLP) offers potential for analyzing unstructured clinical text.

Purpose of the Study:

To develop and validate a novel incident phenotyping approach using unstructured clinical textual data.
To assess the generalizability of the approach across different phenotypes (suicide attempt, sleep-related behaviors).
To evaluate the performance of the phenotyping model, including potential racial disparities.

Main Methods:

Developed a novel phenotyping approach based on a validated method (PheRe) for analyzing entire healthcare records.
Utilized unstructured clinical textual data, agnostic to Electronic Health Record (EHR) and note type.
Validated the approach on large datasets for suicide attempt (89,428 records) and sleep-related behaviors (35,863 records) using silver and gold standards.

Main Results:

Achieved an Area Under the Precision-Recall Curve (AUPR) of ~0.77 for suicide attempt phenotyping.
Observed an AUPR of ~0.31 for sleep-related behaviors phenotyping.
Identified performance differences across phenotypes and by coded race, highlighting the need for algorithmovigilance and debiasing.

Conclusions:

The developed NLP-based phenotyping approach is a scalable method for identifying clinical events from unstructured text.
The model demonstrates generalizability but requires careful validation and bias assessment before implementation in healthcare AI.
Further research is needed to address performance disparities and ensure equitable application of these AI tools.