After admission to emergency department (ED), patients with critical illnesses are transferred to intensive care unit (ICU) due to unexpected clinical deterioration occurrence. Identifying such unplanned ICU transfers is urgently needed for medical physicians to achieve two-fold goals: improving critical care quality and preventing mortality. A priority task is to understand the crucial rationale behind diagnosis results of individual patients during stay in ED, which helps prepare for an early transfer to ICU. Most existing prediction studies were based on univariate analysis or multiple logistic regression to provide one-size-fit-all results. However, patient condition varying from case to case may not be accurately examined by such a simplistic judgment. In this study, we present a new decision tool using a mathematical optimization approach aiming to automatically discover rules associating diagnostic features with high-risk outcome (i.e., unplanned transfers) in different deterioration scenarios. We consider four mutually exclusive patient subgroups based on the principal reasons of ED visits: infections, cardiovascular/respiratory diseases, gastrointestinal diseases, and neurological/other diseases at a suburban teaching hospital. The analysis results demonstrate significant rules associated with unplanned transfer outcome for each subgroups and also show comparable prediction accuracy ($>$70%) compared to state-of-the-art machine learning methods while providing easy-to-interpret symptom-outcome information.

进入急诊室（ED）后，由于意外的临床恶化发生，危重病患者被转入重症监护病房（ICU）。为了实现两个目标，急需确定这种计划外的ICU转移：提高重症监护质量和预防死亡率。首要任务是了解在ED期间各个患者诊断结果背后的关键原理，这有助于为尽早转移到ICU做准备。现有的大多数预测研究都是基于单变量分析或多元逻辑回归来提供一种适合所有人的结果。然而，通过这种简单的判断可能无法准确地检查因情况而异的患者状况。在这个研究中，我们提出了一种使用数学优化方法的新决策工具，旨在自动发现在不同恶化情况下将诊断功能与高风险结果（即计划外转移）相关联的规则。我们根据急诊就诊的主要原因考虑四个互斥的患者亚组：感染，郊区教育医院的心血管/呼吸系统疾病，胃肠道疾病和神经系统/其他疾病。分析结果证明了与每个亚组的计划外转移结果相关的重要规则，并且还显示了可比较的预测准确性（我们根据急诊就诊的主要原因考虑四个互斥的患者亚组：感染，郊区教育医院的心血管/呼吸系统疾病，胃肠道疾病和神经系统/其他疾病。分析结果证明了与每个亚组计划外转移结果相关的重要规则，并且还显示了可比较的预测准确性（我们根据急诊就诊的主要原因考虑四个互斥的患者亚组：感染，郊区教育医院的心血管/呼吸系统疾病，胃肠道疾病和神经系统/其他疾病。分析结果证明了与每个亚组计划外转移结果相关的重要规则，并且还显示了可比较的预测准确性（$>$（70％）与最先进的机器学习方法进行比较，同时提供易于理解的症状结果信息。