Machine Learning for Detection of Correct Peripherally Inserted Central Catheter Tip Position from Radiology Reports in Infants.

ABSTRACT:

Background

In critically ill infants, the position of a peripherally inserted central catheter (PICC) must be confirmed frequently, as the tip may move from its original position and run the risk of hyperosmolar vascular damage or extravasation into surrounding spaces. Automated detection of PICC tip position holds great promise for alerting bedside clinicians to noncentral PICCs.

Objectives

This research seeks to use natural language processing (NLP) and supervised machine learning (ML) techniques to predict PICC tip position based primarily on text analysis of radiograph reports from infants with an upper extremity PICC.

Methods

Radiographs, containing a PICC line in infants under 6 months of age, were manually classified into 12 anatomical locations based on the ra

SUBMITTER: Shah M 

PROVIDER: S-EPMC8426077 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature