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National Neonatal Research Database - Artificial Intelligence (NNRD-AI)

Population Size

1,270,000

People

Population Size statistic card

Years

2007

Years statistic card

Associated BioSamples

None/not available

Associated BioSamples statistic card

Geographic coverage

United Kingdom

England

...see more

Geographic coverage statistic card

Lead time

1-2 months

Lead time statistic card

Summary

The NNRD-AI is a version of the NNRD curated for machine learning and artificial intelligence applications.

Documentation

The National Neonatal Research Database is an award-winning resource, a dynamic relational database containing information extracted from the electronic patient records of babies admitted to NHS neonatal units in England, Wales and Scotland (Northern Ireland is currently addressing regulatory requirements for participation). The NNRD-AI is a version of the NNRD curated for machine learning and artificial intelligence applications.

A team led by Professor Neena Modi at the Chelsea and Westminster Hospital campus of Imperial College London established the NNRD in 2007 as a resource to support clinical teams, managers, professional organisations, policy makers, and researchers who wish to evaluate and improve neonatal care and services. Recently, supported by an award from the Medical Research Council, the neonatal team and collaborating data scientists at the Institute for Translational Medicine and Therapeutics, Data Science Group at Imperial College London, created NNRD-AI.

The NNRD-AI is a subset of the full NNRD with around 200 baby variables, 100 daily variables and 450 additional aggregate variables. The guiding principle underpinning the creation of the NNRD-AI is to make available data that requires minimal input from domain experts. Raw electronic patient record data are heavily influenced by the collection process. Additional processing is required to construct higher-order data representations suitable for modelling and application of machine learning/artificial intelligence techniques. In NNRD-AI, data are encoded as readily usable numeric and string variables. Imputation methods, derived from domain knowledge, are utilised to reduce missingness. Out of range values are removed and clinical consistency algorithms applied. A wide range of definitions of complex major neonatal morbidities (e.g. necrotising enterocolitis, bronchopulmonary dysplasia, retinopathy of prematurity), aggregations of daily data and clinically meaningful representations of anthropometric variables and treatments are also available.

Dataset type

Health and disease

Dataset sub-type

Not applicable

Dataset population size

1270000

Keywords

Observations

Observed Node

Disambiguating Description

Measured Value

Measured Property

Observation Date

Persons

Data on over 1.2 million babies, with approximately 25,000 new infant records added quarterly.

1270000

COUNT

07 Nov 2022

Provenance

Purpose of dataset collection

Care

Source of data extraction

EPR

Collection source setting

Secondary care - In-patients

Patient pathway description

Neonatal unit admission

Image contrast

Not stated

Biological sample availability

None/not available

Structural Metadata

Details

Publishing frequency

Quarterly

Version

5.0.0

Modified

08/10/2024

Citation Requirements

We acknowledge the use of the UK National Neonatal Research Database (https://www.imperial.ac.uk/neonatal-data-analysis-unit/neonatal-data-analysis-unit/), established and led by Professor Neena Modi and her research group at Imperial College London, the contribution of neonatal units that collectively form the UK Neonatal Collaborative (https://www.imperial.ac.uk/neonatal-data-analysis-unit/neonatal-data-analysis-unit/contributing-to-the-nnrd/), and their lead clinicians as well as the support of the Imperial BRC and MRC.

Coverage

Start date

01/01/2007

Time lag

2-6 months

Geographic coverage

United Kingdom, England, Wales, Isle of Man

Maximum age range

1

Follow-up

1 - 10 Years

Accessibility

Language

en

Controlled vocabulary

NHS NATIONAL CODES

Format

csv

Data Access Request

Dataset pipeline status

Not available

Time to dataset access

1-2 months

Jurisdiction

GB

Data use limitation

No restriction

Data use requirements

Ethics approval required,Project-specific restrictions,User-specific restriction

Data Controller

Professor Neena Modi, Imperial College London

Data Processor

Professor Neena Modi, Imperial College London

Dataset Types: Health and disease


Collection Sources: Secondary care - In-patients

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