Automated Knowledge Modeling for Cancer Clinical Practice Guidelines
Pralaypati Ta Bhumika Gupta Arihant Jain Sneha Sree C Arunima Sarkar Keerthi Ram Mohanasankar Sivaprakasam
Clinical Practice Guidelines (CPGs) for cancer diseases evolve rapidly due to
new evidence generated by active research. Currently, CPGs are primarily
published in a document format that is ill-suited for managing this developing
knowledge. A knowledge model of the guidelines document suitable for
programmatic interaction is required. This work proposes an automated method
for extraction of knowledge from National Comprehensive Cancer Network (NCCN)
CPGs in Oncology and generating a structured model containing the retrieved
knowledge. The proposed method was tested using two versions of NCCN Non-Small
Cell Lung Cancer (NSCLC) CPG to demonstrate the effectiveness in faithful
extraction and modeling of knowledge. Three enrichment strategies using Cancer
staging information, Unified Medical Language System (UMLS) Metathesaurus &
National Cancer Institute thesaurus (NCIt) concepts, and Node classification
are also presented to enhance the model towards enabling programmatic traversal
and querying of cancer care guidelines. The Node classification was performed
using a Support Vector Machine (SVM) model, achieving a classification accuracy
of 0.81 with 10-fold cross-validation.