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Publications Nov 2018

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Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records

Description:

Emergency admissions are a major source of healthcare spending. This research paper aimed to derive, validate, and compare conventional and machine learning models for prediction of the first emergency admission. It found that that standard machine learning models not only outperformed the best statistical model but they did so with a substantially better performance and calibration.

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Authors:

Fatemeh Rahimian
Jenny Tran
Kazem Rahimi
Milad Nazarzadeh

Type: Journal Articles & Working Papers