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Interpretable Deep Learning from Electronic Health Records under Learning Constraints


Description:

Patient and population specific data from heterogeneous Electronic Health Records (EHR) are becoming ubiquitous sources for data-driven decision and diagnosis support systems. Deep learning artificial intelligence technologies are emerging as the state-of-the-art for EHR analysis due to their ability to learn complex representations from raw clinical data to obtain strong predictive power combined with an inherent ability to accept multiple data types as input for heterogeneous data fusion. However, key problems and constraints for deep learning systems for health are their lack of interpretability, their inability to exploit vast amounts of unannotated patient data, and their hitherto inability to exploit contextual information to perform well in the low volume data regime, e.g. due to stratification. As a key solution, the DEEPehr project will develop interpretable deep learning predictive systems for a range of EHR input sources, focusing particularly on prediction and prevention of postoperative adverse events. Adverse events, such as infections, are potentially lethal, causing huge suffering for patients and huge costs for healthcare. DEEPehr will develop novel unsupervised and weakly supervised deep learning methodology to exploit the wealth of unannotated patient data for better quality of care, and will leverage the unique hierarchical nature of EHRs for utilizing contextual and prior information to extract new clinical knowledge from low data volumes. Project results and outcomes will impact DEEPehr's clinical stakeholders, and the potential to impact data-driven health and science beyond is great given the generic methodology development core of the project. DEEPehr is high risk because of the profound challenges and interdisciplinary nature of the endeavor, yet feasible due to the high quality of the team, the extensive mobility, and the top international collaborators, creating the synergy effects needed to reach the ambitious project objectives.


Project leader: Robert Jenssen

Started: 2020

Ends: 2024

Category: Universiteter

Sector: UoH-sektor

Budget: 12000000

Institution: Institutt for fysikk og teknologi

Address: Tromsø