Background: The predictive Intensive Care Unit (ICU) scoring system plays an
important role in ICU management because it predicts important outcomes,
especially mortality. Many scoring systems have been developed and used in the
ICU. These scoring systems are primarily based on the structured clinical data
in the electronic health record (EHR), which may suffer the loss of important
clinical information in the narratives and images. Methods: In this work, we
build a deep learning based survival prediction model with multi-modality data
to predict ICU mortality. Four sets of features are investigated: (1)
physiological measurements of Simplified Acute Physiology Score (SAPS) II, (2)
common thorax diseases pre-defined by radiologists, (3) BERT-based text
representations, and (4) chest X-ray image features. We use the Medical
Information Mart for Intensive Care IV (MIMIC-IV) dataset to evaluate the
proposed model. Results: Our model achieves the average C-index of 0.7829 (95%
confidence interval, 0.7620-0.8038), which substantially exceeds that of the
baseline with SAPS-II features (0.7470 (0.7263-0.7676)). Ablation studies
further demonstrate the contributions of pre-defined labels (2.00%), text
features (2.44%), and image features (2.82%).

本文采用深度学习技术，以多模态数据为特征预测 ICU 死亡率，在 MIMIC-IV 数据集上进行验证，结果显示平均 C 指数为 0.7829，明显优于基线 SAPS-II 特征 (0.7470) 并证明了已预定义标签 (2.00%)、文本特征 (2.44%) 和影像特征 (2.82%) 的贡献。

利用放射学报告和图像改善 ICU 死亡率预测的实证研究

An empirical study of using radiology reports and images to improve ICU  mortality prediction

Despite the massive investments in information security technologies and
research over the past decades, the information security industry is still
immature. In particular, the prioritization of remediation efforts within
vulnerability management programs predominantly relies on a mixture of
subjective expert opinion, severity scores, and incomplete data. Compounding
the need for prioritization is the increase in the number of vulnerabilities
the average enterprise has to remediate. This paper produces the first open,
data-driven framework for assessing vulnerability threat, that is, the
probability that a vulnerability will be exploited in the wild within the first
twelve months after public disclosure. This scoring system has been designed to
be simple enough to be implemented by practitioners without specialized tools
or software, yet provides accurate estimates of exploitation. Moreover, the
implementation is flexible enough that it can be updated as more, and better,
data becomes available. We call this system the Exploit Prediction Scoring
System, EPSS.

本文提出了第一个开放的数据驱动的漏洞威胁评估框架，即漏洞在公开披露后的 12 个月内被利用的概率评分系统，该系统被设计为足够简单，可以由非技术专家实践者实现，同时提供准确的利用估计，且可根据更多更好的数据进行更新。我们称之为 Exploit Prediction Scoring System (EPSS)。