Chao Guoqing, Mao Chengsheng, Wang Fei, Zhao Yuan, Luo Yuan
Feinberg School of Medicine, Northwestern University, Chicago, U.S.
Weill Cornell Medicine, Cornell University, New York, U.S.
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2018 Dec;2018:1189-1194. doi: 10.1109/BIBM.2018.8621403. Epub 2019 Jan 24.
ICU mortality risk prediction is a tough yet important task. On one hand, due to the complex temporal data collected, it is difficult to identify the effective features and interpret them easily; on the other hand, good prediction can help clinicians take timely actions to prevent the mortality. These correspond to the interpretability and accuracy problems. Most existing methods lack of the interpretability, but recently Subgraph Augmented Nonnegative Matrix Factorization (SANMF) has been successfully applied to time series data to provide a path to interpret the features well. Therefore, we adopted this approach as the backbone to analyze the patient data. One limitation of the original SANMF method is its poor prediction ability due to its unsupervised nature. To deal with this problem, we proposed a supervised SANMF algorithm by integrating the logistic regression loss function into the NMF framework and solved it with an alternating optimization procedure. We used the simulation data to verify the effectiveness of this method, and then we applied it to ICU mortality risk prediction and demonstrated its superiority over other conventional supervised NMF methods.
重症监护病房（ICU）死亡率风险预测是一项艰巨但重要的任务。一方面，由于收集到的时间数据复杂，难以识别有效的特征并轻松解释它们；另一方面，良好的预测可以帮助临床医生及时采取行动预防死亡。这些分别对应可解释性和准确性问题。大多数现有方法缺乏可解释性，但最近子图增强非负矩阵分解（SANMF）已成功应用于时间序列数据，为很好地解释特征提供了一条途径。因此，我们采用这种方法作为主干来分析患者数据。原始SANMF方法的一个局限性是由于其无监督性质导致预测能力较差。为了解决这个问题，我们通过将逻辑回归损失函数集成到非负矩阵分解（NMF）框架中，提出了一种有监督的SANMF算法，并通过交替优化过程对其进行求解。我们使用模拟数据验证了该方法的有效性，然后将其应用于ICU死亡率风险预测，并证明了它相对于其他传统有监督NMF方法的优越性。
