NotesViewer


NotesViewer

Date Last Modified

2026

Developers

Master, Sahal

Tags



Publisher

PEDSnet

About

NotesViewer is an interactive Shiny web application for exploring PostgreSQL tables. It provides a flexible UI for connecting to a database, selecting tables, applying server‑side filters, and visualising rows as cards, tables, or profiling summaries. The app is designed for quick ad‑hoc data inspection without writing SQL manually.

Access Package

# Clone the repository (if not already present) git clone https://github.com/yourorg/NotesViewer.git # Change to the project directory setwd("NotesViewer") # Install required packages install.packages(c( "shiny", "bslib", "DBI", "RPostgres", "DT", "shinyjs", "shinycssloaders", "jsonlite", "srcr" ))

Provenance

Developer Affiliation(s)

PEDSnet Data Coordinating Center
Applied Clinical Research Center, Children's Hospital of Philadelphia

Description

NotesViewer lets you connect to any PostgreSQL database (the connection is resolved via the srcr package) and explore tables in a responsive, searchable UI. It is especially handy for:

  • Inspecting raw rows from large tables.
  • Applying ad‑hoc filters before loading data to minimise network traffic.
  • Quickly locating records using plain‑text or PCRE regex searches.
  • Exporting filtered results to CSV for downstream analysis.
View on GitHub

Clinical Subject Headings

Related Code

Related Concept Sets

Related Studies

Related Phenotypes

Related Resources

Thumbnail Image
Development and Validation of an LLM-Based Pipeline for Transcranial Doppler Interpretation in Pediatric Sickle Cell Disease​
Created:2026-05
Slide deck presented by Sahal Master, BDS, MPH at the American Medical Informatics Association (AMIA) 2026 Clinical Informatics Conference. This presentation describes the work done at the PEDSnet learning health system to develop an LLM pipeline for the extraction of measurements from clinical notes data as part of a study with PEDSnet collaborator, Dr. Aleksandra Dain. Specifically, this case study evaluated an LLM pipeline for artery-level velocity extraction for transcranial doppler outcome classification.

Related Data Quality Check

Creative Commons license

a CC-BY Attribution 4.0 license.
Except where otherwised noted, this item's license is described as a CC-BY Attribution 4.0 license.

This license applies to the metadata and documentation of this digital package. The source code and software components may be governed by different licensing terms. Please refer to the GitHub repository link for specific software licensing information.

Cite this Code

Master, S. (2025, December). NotesViewer. [Code]. PEDSpace Knowledge Bank. https://hdl.handle.net/20.500.14642/1658