EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer
  Learning for Automated Cancer Registry System
Chengjie Lu Qinghua Xu Tao Yue Shaukat Ali Thomas Schwitalla Jan F. Nygård
The Cancer Registry of Norway (CRN) collects information on cancer patients
by receiving cancer messages from different medical entities (e.g., medical
labs, and hospitals) in Norway. Such messages are validated by an automated
cancer registry system: GURI. Its correct operation is crucial since it lays
the foundation for cancer research and provides critical cancer-related
statistics to its stakeholders. Constructing a cyber-cyber digital twin (CCDT)
for GURI can facilitate various experiments and advanced analyses of the
operational state of GURI without requiring intensive interactions with the
real system. However, GURI constantly evolves due to novel medical diagnostics
and treatment, technological advances, etc. Accordingly, CCDT should evolve as
well to synchronize with GURI. A key challenge of achieving such
synchronization is that evolving CCDT needs abundant data labelled by the new
GURI. To tackle this challenge, we propose EvoCLINICAL, which considers the
CCDT developed for the previous version of GURI as the pretrained model and
fine-tunes it with the dataset labelled by querying a new GURI version.
EvoCLINICAL employs a genetic algorithm to select an optimal subset of cancer
messages from a candidate dataset and query GURI with it. We evaluate
EvoCLINICAL on three evolution processes. The precision, recall, and F1 score
are all greater than 91%, demonstrating the effectiveness of EvoCLINICAL.
Furthermore, we replace the active learning part of EvoCLINICAL with random
selection to study the contribution of transfer learning to the overall
performance of EvoCLINICAL. Results show that employing active learning in
EvoCLINICAL increases its performances consistently.