HuLP: Human-in-the-Loop for Prognosis
CoRR(2024)
摘要
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 of HuLP.
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