Researchers are combining AI with data to improve patient care and outcomes

Researchers are combining AI with data to improve patient care and outcomes

Chenyang Lu, Fullgraf Professor of Computer Science and Engineering at Washington University in the St. Louis McKelvey School of Engineering, combines artificial intelligence with data to improve patient care and outcomes.

But he doesn't just care for patients, he also develops technology to monitor the health and well-being of doctors.

The Lu lab presented two papers at this year's ACM SIGKDD conference on Knowledge Discovery and Data Mining, both of which outline novel methods his team has developed -; with staff at Washington University School of Medicine -; to improve health outcomes by bringing deep learning into clinical care.

For caregivers, Lu addressed burnout and how to predict it before it even occurs. Activity logs of how doctors interact with electronic health records provided researchers with vast amounts of data. They fed this data into a machine learning framework developed by Lu and his team -; Hierarchical Burnout Prediction Based on Activity Logs (HiPAL) -; and it was able to extrapolate meaningful patterns of workload and predict burnout from that data in a non-intrusive and automated way.

When it comes to patient care, doctors in the operating room collect significant amounts of data about their patients, both during preoperative care and during surgery -; Data that Lu and his collaborators thought they could put to good use with Lu's deep learning approach: Clinical Variational Autoencoder (cVAE).

Using novel algorithms developed by the Lu lab, they were able to predict who would have longer surgery and who would be more likely to develop delirium after surgery. The model was able to convert hundreds of clinical variables into just 10, which the model used to make accurate and interpretable predictions about outcomes that were superior to current methods.

Find out more about the team's findings on the engineering website.

Lu and his interdisciplinary collaborators will continue to validate both models in the hope that both will bring the power of AI to hospital environments.

Source:

Washington University in St. Louis

Reference:

Liu, H., et al. (2022) HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records. KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. doi.org/10.1145/3534678.3539056.

.