Predicting diabetes-related hospitalizations based on electronic health records

By Theodora S. Brisimi, Tingting Xu, Taiyao Wang, Wuyang Dai, Ioannis Ch. Paschalidis. 2018

35 citations recorded by the source corpus.

Objective: To derive a predictive model to identify patients likely to be hospitalized during the following year due to complications attributed to Type II diabetes. Methods: A variety of supervised machine learning classification methods were tested and a new method that discovers hidden patient clusters in the positive class (hospitalized) was developed while, at the same time, sparse linear support vector machine classifiers were derived to separate positive samples from the negative ones (non-hospitalized). The convergence of the new method was established and theoretical guarantees were proved on how the classifiers it produces generalize to a test set not seen during training. Results: The methods were tested on a large set of patients from the Boston Medical Center – the largest safety net hospital in New England. It is found that our new joint clustering/classification method achieves an accuracy of 89% (measured in terms of area under the ROC Curve) and yields informative clusters which can help interpret the classification results, thus increasing the trust of physicians to the algorithmic output and providing some guidance towards preventive measures. While it is possible to increase accuracy to 92% with other methods, this comes with increased computational cost and lack of interpretability. The analysis shows that even a modest probability of preventive actions being effective (more than 19%) suffices to generate significant hospital care savings. Conclusions: Predictive models are proposed that can help avert hospitalizations, improve health outcomes and drastically reduce hospital expenditures. The scope for savings is significant as it has been estimated that in the USA alone, about $5.8 billion are spent each year on diabetes-related hospitalizations that could be prevented.

Subfields: Artificial Intelligence, Epidemiology, Health Information Management, Medical & Health AI

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Epidemiology

In Health Information Management

In Medical & Health AI

Citations in the corpus

Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020, background): “The rise of Electronic Health Record (EHR) systems has created an opportunity for building predictive ML models for diagnosis and prognosis (e.g. Ambrosino et al. (1995); Brisimi et al. (2019); Feng et al. (2019)).”