Replication of Real-World Evidence in Oncology Using Electronic Health Record Data Extracted by Machine Learning.

ABSTRACT: Meaningful real-world evidence (RWE) generation requires unstructured data found in electronic health records (EHRs) which are often missing from administrative claims; however, obtaining relevant data from unstructured EHR sources is resource-intensive. In response, researchers are using natural language processing (NLP) with machine learning (ML) techniques (i.e., ML extraction) to extract real-world data (RWD) at scale. This study assessed the quality and fitness-for-use of EHR-derived oncology data curated using NLP with ML as compared to the reference standard of expert abstraction. Using a sample of 186,313 patients with lung cancer from a nationwide EHR-derived de-identified database, we performed a series of replication analyses demonstrating some common analyses conducted i

SUBMITTER: Benedum CM 

PROVIDER: S-EPMC10046618 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature