Patient-Level Clinical Expertise Enhances Prostate Cancer Recurrence Predictions with Machine Learning

Abstract

With rising access to electronic health record data, application of artificial intelligence to create clinical risk prediction models has grown. A key component in designing these models is feature generation. Methods used to generate features differ in the degree of clinical expertise they deploy (from minimal to population-level to patient-level), and subsequently the extent to which they can extract reliable signals and be automated. In this work, we develop a new process that defines how to systematically implement patient-level clinician feature generation (CFG), which leverages clinical expertise to define concepts relevant to the outcome variable, identify each concept's associated features, and finally extract most features on a per-patient level by manual chart review. We subsequently apply this method to identifying and extracting patient-level features predictive of cancer recurrence from progress notes for a cohort of prostate cancer patients. We evaluate the performance of the CFG process against an automated feature generation (AFG) process via natural language processing techniques. The machine learning outcome prediction model leveraging the CFG process has a mean AUC-ROC of 0.80, in comparison to the AFG model that has a mean AUC-ROC of 0.74. This relationship remains qualitatively unchanged throughout extensive sensitivity analyses. Our analyses illustrate the value of in-depth specialist reasoning in generating features from progress notes and provide a proof of concept that there is a need for new research on efficient integration of in-depth clinical expertise into feature generation for clinical risk prediction.

Quantitative Assessment of COVID-19 Lung Disease Severity: A Segmentation-based Approach

“…Therefore, there is a need for methods to compensate for limitations of available datasets with biologically-inspired data augmentation e.g., Sousa et al [1] There has been criticism of using generated data for medical imaging applications, however. Chang [2] and Vallon et al [3] discussed the limitations of data augmentation strategies in medical image analysis and state that clinical expertise is needed to guide training and determine relevant features for model training.…”