Approaching adverse event detection utilizing transformers on clinical time-series
This work addresses the risk of misdiagnosis or ineffective treatment in hospitalized patients, but it is incremental as it builds on existing transformer methods for time-series data.
The researchers tackled the problem of detecting adverse events in hospital patients by developing an anomaly detection system for clinical trajectories, using a self-supervised transformer framework on 16 months of vital sign data from Nordland Hospital Trust, with preliminary results showing promise but requiring more data for comprehensive evaluation.
Patients being admitted to a hospital will most often be associated with a certain clinical development during their stay. However, there is always a risk of patients being subject to the wrong diagnosis or to a certain treatment not pertaining to the desired effect, potentially leading to adverse events. Our research aims to develop an anomaly detection system for identifying deviations from expected clinical trajectories. To address this goal we analyzed 16 months of vital sign recordings obtained from the Nordland Hospital Trust (NHT). We employed an self-supervised framework based on the STraTS transformer architecture to represent the time series data in a latent space. These representations were then subjected to various clustering techniques to explore potential patient phenotypes based on their clinical progress. While our preliminary results from this ongoing research are promising, they underscore the importance of enhancing the dataset with additional demographic information from patients. This additional data will be crucial for a more comprehensive evaluation of the method's performance.