A large and diverse set of measurements are regularly collected during a
patient's hospital stay to monitor their health status. Tools for integrating
these measurements into severity scores, that accurately track changes in
illness severity, can improve clinicians ability to provide timely
interventions. Existing approaches for creating such scores either 1) rely on
experts to fully specify the severity score, or 2) train a predictive score,
using supervised learning, by regressing against a surrogate marker of severity
such as the presence of downstream adverse events. The first approach does not
extend to diseases where an accurate score cannot be elicited from experts. The
second approach often produces scores that suffer from bias due to
treatment-related censoring (Paxton, 2013). We propose a novel ranking based
framework for disease severity score learning (DSSL). DSSL exploits the
following key observation: while it is challenging for experts to quantify the
disease severity at any given time, it is often easy to compare the disease
severity at two different times. Extending existing ranking algorithms, DSSL
learns a function that maps a vector of patient's measurements to a scalar
severity score such that the resulting score is temporally smooth and
consistent with the expert's ranking of pairs of disease states. We apply DSSL
to the problem of learning a sepsis severity score using a large, real-world
dataset. The learned scores significantly outperform state-of-the-art clinical
scores in ranking patient states by severity and in early detection of future
adverse events. We also show that the learned disease severity trajectories are
consistent with clinical expectations of disease evolution. Further, using
simulated datasets, we show that DSSL exhibits better generalization
performance to changes in treatment patterns compared to the above approaches.

文章提出一种基于排序算法的疾病严重程度评分学习框架（DSSL），可将患者的测量数据转化为与专家评分一致的严重程度分数，将其应用于感染性休克疾病的严重程度评分，得到的分数在排序和早期治疗方面均明显优于现有的临床评分，并且对不同治疗方案的变化具有更好的泛化性能。