DeepHealth Phase 1 made progress towards developing entirely new ways to analyse patient data from electronic health records. This project will disseminate some of these results (concretely, present four peer reviewed papers at international conferences), as well as further develop some of these results/prototypes with the aim to investigate opportunities for building on these works for new funding proposals (e.g. H2020). Background: Vast amounts of heterogeneous and complex data from Electronic Health Records (EHRs) are ubiquitously being recorded at the patient level in healthcare (big data). This represents a largely untapped source of data-driven clinical information, having the potential to transform health and leap forward quality of care for the individual patient. However, this requires inference tools of much greater sophistication than traditional tools that often suffer from weaknesses such as oversimplified modeling and predictions based on population averages. As a future and emerging technology in artificial intelligence and cognitive systems, deep learning has revolutionized analysis of big data in applied domains such as speech and image analysis. Similar breakthrough performance is realistic in health, provided that main challenges related to deep learning, especially in health, are resolved. This included the high dimension-small sample size problem (d>>N), heterogeneous source integration, and missing data. DeepHealth's aim is to move the research front in deep learning and artificial intelligence for data analysis beyond the current state-of-the art for the best quality of care. This will be achieved by a long-term research endeavor within the context of ubiquitous data and services in healthcare for prediction and prevention of postoperative complications, an enormous problem in health. The project will leverage vast amounts of uniquely available EHR data and clinical imagery from the University Hospital of North Norway, related to gastrosurgery and especially colorectal cancer, for which surgery is the only curative treatment. DeepHealth will perform analysis before, under, and after surgery jointly on unstructured and structured data, times series data, and imagery, for predictions of postoperative complications. Close collaboration exists with surgeons and international deep learning and computational health expertise, wherein high mobility will be key.
Project leader: Robert Jenssen