Developing Automated Computer Algorithms to Track Periodontal Disease Change from Longitudinal Electronic Dental Records.

ABSTRACT:

Objective

To develop two automated computer algorithms to extract information from clinical notes, and to generate three cohorts of patients (disease improvement, disease progression, and no disease change) to track periodontal disease (PD) change over time using longitudinal electronic dental records (EDR).

Methods

We conducted a retrospective study of 28,908 patients who received a comprehensive oral evaluation between 1 January 2009, and 31 December 2014, at Indiana University School of Dentistry (IUSD) clinics. We utilized various Python libraries, such as Pandas, TensorFlow, and PyTorch, and a natural language tool kit to develop and test computer algorithms. We tested the performance through a manual review process by generating a confusion matrix. We calculated precis

SUBMITTER: Patel JS 

PROVIDER: S-EPMC10047444 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature