A blast from the past. Today, I am revisiting a 2019 paper about using deep learning to predict acute kidney injury (AKI) among hospitalized patients from structured EHR data (I summarized it when it was published). At the time, the model significantly outperformed any other results in the field, and I was curious how things have changed since then.
A bevy of models. Predicting AKI is an important use case, and a new review assesses the performance of 46 recent models. While they use data from different hospitals and are not directly comparable, the 2019 study remains a top performer. (practical point: reviews tend to be accessible and are useful to get a sense of the difficulty of a problem is and the expected performance.)
Ask “when”, not just “what”. The main innovation of the 2019 paper was designing a method that used data longitudinally, taking into account not just which diagnoses, procedures, and lab values are recorded, but also when. The importance of longitudinality has been demonstrated in many healthcare use cases in recent years, but most standard off-the-shelf models don’t handle longitudinal data well.
More good news. The authors expanded their method to a general protocol for longitudinal analysis of EHR data and made it available as open source. This is one of the themes of this newsletter: more and more cutting-edge deep learning models are now accessible to practicing data science teams.
What I’m curious about. The 2019 model uses a type of model called “recurrent neural network”, which are becoming less popular. A new type of model called “transformer” has taken over for many longitudinal tasks, but there aren’t many applications to healthcare yet – this is an area I’ll be following.