A personalized periodontitis risk based on nonimage electronic dental records by machine learning.

Objective: This study aimed to develop a machine-learning (ML) model to predict the risk for Periodontal Disease (PD) based on nonimage electronic dental records (EDRs).

Methods: By using EDRs collected in the BigMouth repository, dental patients from the US were included. Patients were labeled as cases or controls, based on PD diagnosis, treatment and pocketing.

The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review.

Background: Electronic health records (EHRs) contain patients' health information over time, including possible early indicators of disease. However, the increasing amount of data hinders clinicians from using them. There is accumulating evidence suggesting that machine learning (ML) and deep learning (DL) can assist clinicians in analyzing these large-scale EHRs, as algorithms thrive on high volumes of data.