Development of Predictive Models in Patients with Epiphora Using Lacrimal Scintigraphy and Machine Learning.

ABSTRACT:

Purpose

We developed predictive models using different programming languages and different computing platforms for machine learning (ML) and deep learning (DL) that classify clinical diagnoses in patients with epiphora. We evaluated the diagnostic performance of these models.

Methods

Between January 2016 and September 2017, 250 patients with epiphora who underwent dacryocystography (DCG) and lacrimal scintigraphy (LS) were included in the study. We developed five different predictive models using ML tools, Python-based TensorFlow, R, and Microsoft Azure Machine Learning Studio (MAMLS). A total of 27 clinical characteristics and parameters including variables related to epiphora (VE) and variables related to dacryocystography (VDCG) were used as input data. Apart from this, w

SUBMITTER: Park YJ 

PROVIDER: S-EPMC6473022 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature