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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Predicting ophthalmic clinic non-attendance using machine learning: Development and validation of models using

Finley Breeze¹, Ruhella R Hossain^1,2, Michael Mayo³

¹Department of Ophthalmology, University of Auckland, Auckland, New Zealand.

Clinical & Experimental Ophthalmology

|October 27, 2023

Summary

Machine learning accurately predicts ophthalmic clinic non-attendance using basic data, offering a cost-effective alternative to current strategies. This can improve healthcare access and reduce inequities in New Zealand.

Keywords:

epidemiology equity machine learning

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

Area of Science:

Ophthalmology
Health Informatics
Machine Learning

Background:

Ophthalmic clinic non-attendance in New Zealand leads to poor health outcomes, inequities, and significant costs.
Current strategies to improve attendance are expensive and ineffective.

Purpose of the Study:

To develop and validate machine learning models for accurate prediction of ophthalmic clinic non-attendance.

Main Methods:

A retrospective observational study analyzed 3.1 million appointments from New Zealand public ophthalmology clinics (2009-2018).
XGBoost and logistic regression models were trained and optimized using repeated ten-fold cross-validation on demographic and clinic-related variables.
Models trained on regional data subsets were compared to a nationwide model.

Main Results:

XGBoost models trained on regional data achieved the highest predictive performance (mean AUROC 0.764).
XGBoost outperformed logistic regression (mean AUROC 0.756, p=0.002).
Region-specific models performed better than a single nationwide model (mean AUROC 0.754, p=0.04).

Conclusions:

Machine learning models can effectively predict ophthalmic clinic non-attendance using readily available data.
Further research into implementing these algorithms in scheduling and public health interventions is warranted.