CRII:SCH: Interactive Explainable Deep Survival Analysis

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Annually, the United States spends almost 20% of gross domestic product (GDP) in healthcare with growth continued to be boosted by a greying population aging into Medicare. Although the cost is huge, numerous patients fail to get timely and effective medication cure. Accurate diagnosis is critical in clinical decision making. However, “prevention is better than cure” as prevention and early intervention will prevent the aging people from suffering more diseases and/or more extensive treatments. Also, it is too late to build the prediction model when a lot of patients have been observed in the late stage of a progressive disease, which severely damages their health. Meanwhile, in order to be usable by healthcare providers, the prediction model needs to be interpretable and trustable. Also, efficient interaction between human stakeholders (e.g., developers, domain experts and/or end-users) and clear model interpretation not only improve the model performance but also enhance human trust. The proposed research project aims at developing algorithms and methods that support implementation of trustworthy and time-efficient data-driven decision making for prevention and early intervention.The main approach proposed in this project is interactive explainable deep survival analysis. Survival analysis aims at predicting the time to event of interest, which is extremely beneficial in healthcare for modeling disease progression, identifying prognostic factors, assessing risk of health. This project will build deep survival analysis models in healthy aging and precision medicine to support clinical decision making, especially in the early stage of a progressive disease before a lot of patients have been suffered from that disease. Deep survival analysis is a kind of “black box” model that stakeholders cannot tell how the model operates and how it comes to its decisions and hence limits its usage in practice. This project will develop methods to achieve both transparency and trustworthiness in deep survival analysis models with encoding of domain knowledge and expert feedback to achieve better prediction performance. More specifically, this project will propose a time-dependent counterfactual gradient integration to interpret what makes the model output differentiate from the counterfactual survival status at each time interval. This project will also incorporate feature attribution priors into the training process of deep survival analysis model to improve consistency of the explanation as well as the performance and trustworthiness of the model. Inspired by human-in-the-loop, this project will further investigate efficient schemes to mathematically formulate physicians' qualitative feedback, and interactively incorporate them in the learning process of the model with powerful perceptual user interface to efficiently encode diverse types of feedback from physicians.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.