HuLP: Human-in-the-Loop for Prognosis
This addresses the problem of improving prognosis reliability for clinicians, though it appears incremental as it builds on existing human-in-the-loop and missing data handling methods.
The paper tackles the problem of unreliable prognostic models in clinical contexts due to missing covariates and outcomes by introducing HuLP, a human-in-the-loop model that allows clinician intervention to correct predictions and imputes missing data using neural networks, demonstrating superiority on two real-world medical datasets.
This paper introduces HuLP, a Human-in-the-Loop for Prognosis model designed to enhance the reliability and interpretability of prognostic models in clinical contexts, especially when faced with the complexities of missing covariates and outcomes. HuLP offers an innovative approach that enables human expert intervention, empowering clinicians to interact with and correct models' predictions, thus fostering collaboration between humans and AI models to produce more accurate prognosis. Additionally, HuLP addresses the challenges of missing data by utilizing neural networks and providing a tailored methodology that effectively handles missing data. Traditional methods often struggle to capture the nuanced variations within patient populations, leading to compromised prognostic predictions. HuLP imputes missing covariates based on imaging features, aligning more closely with clinician workflows and enhancing reliability. We conduct our experiments on two real-world, publicly available medical datasets to demonstrate the superiority and competitiveness of HuLP.