An end-to-end hybrid algorithm for automated medication discrepancy detection.

ABSTRACT:

Background

In this study we implemented and developed state-of-the-art machine learning (ML) and natural language processing (NLP) technologies and built a computerized algorithm for medication reconciliation. Our specific aims are: (1) to develop a computerized algorithm for medication discrepancy detection between patients' discharge prescriptions (structured data) and medications documented in free-text clinical notes (unstructured data); and (2) to assess the performance of the algorithm on real-world medication reconciliation data.

Methods

We collected clinical notes and discharge prescription lists for all 271 patients enrolled in the Complex Care Medical Home Program at Cincinnati Children's Hospital Medical Center between 1/1/2010 and 12/31/2013. A double-annotated,

SUBMITTER: Li Q 

PROVIDER: S-EPMC4427951 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature