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

ABSTRACT:

Background

Emergency admissions are a major source of healthcare spending. We aimed to derive, validate, and compare conventional and machine learning models for prediction of the first emergency admission. Machine learning methods are capable of capturing complex interactions that are likely to be present when predicting less specific outcomes, such as this one.

Methods and findings

We used longitudinal data from linked electronic health records of 4.6 million patients aged 18-100 years from 389 practices across England between 1985 to 2015. The population was divided into a derivation cohort (80%, 3.75 million patients from 300 general practices) and a validation cohort (20%, 0.88 million patients from 89 general practices) from geographically distinct regions with differe

SUBMITTER: Rahimian F 

PROVIDER: S-EPMC6245681 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature