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.

翻译：本文提出HuLP——一种面向预后的"人在回路"模型，旨在提升临床预后模型在面临协变量和结局变量缺失等复杂情况时的可靠性与可解释性。HuLP提供了一种创新方法，使人类专家能够介入干预，赋予临床医生与模型预测进行交互并修正的能力，从而促进人类与AI模型协作，生成更准确的预后结果。此外，HuLP通过利用神经网络并设计定制化方法论有效处理数据缺失问题，应对了缺失数据挑战。传统方法往往难以捕捉患者群体内细微的变异特征，导致预后预测性能受损。HuLP基于影像特征对缺失协变量进行插补，更贴合临床医生工作流程，从而增强可靠性。我们在两个真实世界公开医学数据集上开展实验，验证了HuLP的优越性能。