Leveraging Treatment Patterns to Predict Survival of Patients with Advanced Non-Small-Cell Lung Cancer
Kyle Haas, Malika Mahoui, Simone Gupta, Stuart Duncan Morton · 2018
Despite all efforts made in the last few decades in disease prevention and treatment, lung cancer remains the most common cancer after skin cancer. Lung cancer is also the leading cause in cancer death. One of the criteria used to assess both the severity of the disease and the efficacy of treatments in cancer is survival rate. Survival rate is impacted by several parameters including disease stage and patients profile. The aim of this research is to build a predictive model to assess survivability of advanced stage non-small cell lung cancer (NSCLC) patients. The main feature of the proposed methodology is the leveraging of patients treatment sequences in addition to clinical, demographic and genomic patient information. A new algorithm that generates frequent patterns derived from patient treatment sequences is proposed. The algorithm aims at capturing the complex relationship that exists between treatments sequences and patient outcomes in order to produce better prediction models. The results of the experiments show that random forest models achieved the best prediction performance. The features that have the most influence in the prediction performance are features derived from treatment sequences; indicating their potential to encapsulate the information expressed in the other features such as the genomic features.