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.

翻译：本文提出HuLP（Human-in-the-Loop for Prognosis），一种面向临床预后预测的人机协同模型，旨在提升预后模型在面临协变量与结局变量缺失等复杂情况时的可靠性与可解释性。HuLP提供了一种创新的人机交互机制，允许临床专家介入并修正模型预测，从而促进人类与人工智能模型的协作，以生成更准确的预后结果。此外，HuLP通过运用神经网络技术，提出了一套针对性处理缺失数据的有效方法，以应对数据缺失带来的挑战。传统方法往往难以捕捉患者群体内部的细微差异，导致预后预测准确性下降。HuLP基于影像特征对缺失协变量进行填补，更贴合临床工作流程并提升了可靠性。我们在两个真实世界的公开医疗数据集上进行了实验，验证了HuLP的优越性与竞争力。