Deep Learning Bests Traditional Models in Risk Stratification

Risk stratification in healthcare is defined as the process of assigning a risk status to all patients in a practice. The risk status is based on data collected through a multitude of sources such as medical history, health indicators, and the lifestyle of an adult or paediatric population. The objectives of stratifying risk include addressing population management challenges, individualizing treatment plans to lower risks, matching risk with levels of care, and aligning the practice with value-based care approaches.

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AI, ML and DL all have gained a lot of attention for quite some time now and are all a part of the Fourth Industrial Revolution. These technologies are greatly transforming industries such as retail, manufacturing, finance, travel, etc. Healthcare is surely one such industry where you will find plenty of use cases for these technologies. As a result, DL in healthcare has offered path-breaking applications. They are able to gather massive volumes of data such as medical reports, patients’ records, and insurance records, and apply neural networks to provide the best outcomes.

This team of researchers had set out to build a predictive risk model for pediatric patients using DL with clinical and financial data. The researchers collected data from 2014 and 2015 and developed a DL model that would predict a pediatric patient’s risk of hospitalization in 2016. The next step was to compare the model’s performance to that of traditional risk models. They also calculated costs for patients in the top 1 percent and 5 percent of hospitalization risk recognized by both models. The results were very clear and showed the DL model performed best compared to all the other models, with an area under the curve of 75.1 percent.

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Full Text： dataversity

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