Article

A hybrid approach to scalable real-world data curation by machine learning and human experts

2023-03-08

Abstract excerpt

<h4>Objective</h4> Machine learning has the potential to increase the scale of real-world data curated from electronic health records, but maintaining a high standard of data quality is important to avoid biasing downstream analyses. To increase scale without compromising quality, we propose a hybrid data curation methodology that employs both manual abstraction by clinical experts and automated extraction by mach...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 34bf2743-30e9-5d6b-aa1d-24f4db0b13ec
DOI: 10.1101/2023.03.06.23286770

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

A hybrid approach to scalable real-world data curation by machine learning and human expertsDOI 10.1101/2023.03.06.23286770Read

Fully Automated Abstraction of Longitudinal Breast Oncology Records with Off-The-Shelf Large Language ModelsSemantic proximity Read

Approach to Machine Learning for Extraction of Real-World Data Variables from Electronic Health RecordsSemantic proximity Read

Breaching the curation bottleneck with human-machine reading symbiosisSemantic proximity Read

Validation and clinical discovery demonstration of a real-world data extraction platformSemantic proximity Read

Generalizable and Automated Classification of TNM Stage from Pathology Reports with External ValidationSemantic proximity Read

Employing Consensus-Based Reasoning with Locally Deployed LLMs for Enabling Structured Data Extraction from Surgical Pathology ReportsSemantic proximity Read

Data Extraction from Oncology Imaging Reports by Large Language Models: A Comparative Accuracy StudySemantic proximity Read

Selective prediction for extracting unstructured clinical dataSemantic proximity Read

A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health dataSemantic proximity Read

Using Machine Learning to Discover Unexpected Patterns in Clinical Data: A Case Study in COVID-19 Sub-cohort DiscoverySemantic proximity Read

A semi-supervised approach for rapidly creating clinical biomarker phenotypes in the UK Biobank using different primary care EHR and clinical terminology systemsSemantic proximity Read

LLM-based data extraction for a large cancer registry, the Ontario Hereditary Cancer Research NetworkSemantic proximity Read