Improving Healthcare Outcomes by Identifying Populations with Higher Risk of Lung Cancer from Primary Care Data

Machine learning models have increasingly played an important role in medicine and healthcare. They can be readily adapted for clinical prognostic tasks. A prominent task in lung cancer healthcare is to select people with higher lung cancer risk from some population. The task can be undertaken using clinical predictive models along with real-world Electronic Healthcare Records. In this paper, we provide a worked example for such task using Logistic Regression as the model and using CPRD Dataset as the EHRs which cover 4.5% UK population [9]. Further, the use of clinical predictive models in cancer care has gone beyond cancer screening programme. That is, such models can also be employed to perform a variety of cancer healthcare management tasks. In this paper, we provide six “lung cancer”-related use cases to illustrate task diversity. It is also demonstrated that each of 6 use cases has chosen their appropriate set of prognostic predictors to optimally perform their task. Last, their task performance is also critically evaluated. Domains such as medicine and healthcare require trustworthiness and accountability. To meet this challenge, Explainable Artificial Intelligence (XAI) techniques have been timely developed. In this paper, we introduced impurity-, permutation-, LIME-, and SHAP-based importance measures. These XAI techniques were applied to 6 use cases for variable importance analysis. Last, we used domain-specific knowledge to critically interpret their XAI results. We also briefly reviewed a model-specific XAI application. It relies on knowledge-based constraints.