Natural language processing of clinical notes enables early inborn error of immunity risk ascertainment.

ABSTRACT:

Background

There are now approximately 450 discrete inborn errors of immunity (IEI) described; however, diagnostic rates remain suboptimal. Use of structured health record data has proven useful for patient detection but may be augmented by natural language processing (NLP). Here we present a machine learning model that can distinguish patients from controls significantly in advance of ultimate diagnosis date.

Objective

We sought to create an NLP machine learning algorithm that could identify IEI patients early during the disease course and shorten the diagnostic odyssey.

Methods

Our approach involved extracting a large corpus of IEI patient clinical-note text from a major referral center's electronic health record (EHR) system and a matched control corpus for compari

SUBMITTER: Roberts K 

PROVIDER: S-EPMC10910118 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature