Automated Knowledge Modeling for Cancer Clinical Practice Guidelines
This work addresses the need for programmatic interaction with cancer care guidelines for healthcare professionals, though it is incremental as it builds on existing extraction and modeling techniques.
The authors tackled the problem of rapidly evolving cancer clinical practice guidelines (CPGs) by developing an automated method to extract knowledge from NCCN CPGs and generate a structured model, achieving a node classification accuracy of 0.81 with SVM and 10-fold cross-validation.
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.