AI Case Study
Researchers at the George Institute for Global Health improve emergency room admittance predictive models using machine learning
Researchers from Oxford test different machine learning models to predict risk of emergency room admission for patients against a standard non-AI statistical model. The machine learning models outperformed the standard methods of prediction.
Healthcare Providers And Services
"Building on earlier studies and emerging analytical opportunities, we aimed to assess whether application of 2 standard machine learning techniques could enhance the prediction of emergency hospital admissions in the general population compared with a high-performing Cox proportional hazards (CPH) model that also used large-scale EHRs.  To better understand when and how machine learning models might achieve a higher performance, we aimed to develop and compare a series of models. In the first step, we used the same set of variables and prediction window (24 months after baseline) as in the previous CPH model. In the next steps, we added more variables and included information about variable timing to the models. To further test the hypothesis that the predictive ability of machine learning models is stronger than that of conventional models when outcomes in the more distant future are to be predicted (because of their ability to better capture multiple known and unknown interactions), we further changed the time horizon for risk prediction to shorter and longer periods."
"Emergency hospital admissions are a major source of healthcare spending. In the UK, there were over 5.9 million recorded emergency hospital admissions in 2017, an increase of 2.6% compared to the preceding year. Given the avoidable nature of a large proportion of such admissions, there has been a growing research and policy interest in effective ways of averting them. To guide decision-making, several risk prediction models have been reported. However, on average, models tend to have a poor ability to discriminate risk."
"The use of machine learning and addition of temporal information led to substantially improved discrimination and calibration for predicting the risk of emergency admission. Model performance remained stable across a range of prediction time windows and when externally validated. These findings support the potential of incorporating machine learning models into electronic health records to inform care and service planning."
"We compared the CPH model to 2 machine learning models, namely gradient boosting classifier (GBC) and random forest (RF). Both GBC and RF models were used as ensemble models based on decision trees, but each represented a distinct family of ensemble learning methods—boosting and bagging, respectively. Boosting refers to any ensemble method that can combine several weak learners into a strong learner. The general idea of most boosting methods is to train predictors sequentially, each trying to correct its predecessor. By contrast, bagging uses the same training algorithm multiple times in parallel (e.g., a RF employs multiple decision trees), but trains them on different random subsets of the data. When sampling is performed with replacement, this method is called bagging. These 2 models were chosen because they are shown to outperform other machine learning models on a variety of datasets, are fairly robust and applicable to big datasets, and require little modification of parameters prior to modelling. These machine learning methods work on both categorical and numerical variables in any scale, obviating the need for conversion of features or normalisation of their values. We tuned the hyperparameters of GBC and RF after a broad search of parameter space."
The researchers 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 different risk levels.
The initial set of predictors for all models included 43 variables, including patient demographics, lifestyle factors, laboratory tests, currently prescribed medications, selected morbidities, and previous emergency admissions. We then added 13 more variables (marital status, prior general practice visits, and 11 additional morbidities), and also enriched all variables by incorporating temporal information whenever possible (e.g., time since first diagnosis)."