Wu Chuang, Rosenfeld Roni, Clermont Gilles
School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
Departments of Critical Care Medicine, Industrial Engineering, and Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
PLoS One. 2014 Apr 3;9(4):e89053. doi: 10.1371/journal.pone.0089053. eCollection 2014.
Prediction of patient-centered outcomes in hospitals is useful for performance benchmarking, resource allocation, and guidance regarding active treatment and withdrawal of care. Yet, their use by clinicians is limited by the complexity of available tools and amount of data required. We propose to use Disjunctive Normal Forms as a novel approach to predict hospital and 90-day mortality from instance-based patient data, comprising demographic, genetic, and physiologic information in a large cohort of patients admitted with severe community acquired pneumonia. We develop two algorithms to efficiently learn Disjunctive Normal Forms, which yield easy-to-interpret rules that explicitly map data to the outcome of interest. Disjunctive Normal Forms achieve higher prediction performance quality compared to a set of state-of-the-art machine learning models, and unveils insights unavailable with standard methods. Disjunctive Normal Forms constitute an intuitive set of prediction rules that could be easily implemented to predict outcomes and guide criteria-based clinical decision making and clinical trial execution, and thus of greater practical usefulness than currently available prediction tools. The Java implementation of the tool JavaDNF will be publicly available.
预测医院中以患者为中心的结果对于绩效基准评估、资源分配以及有关积极治疗和停止治疗的指导很有用。然而，临床医生对其的使用受到可用工具的复杂性和所需数据量的限制。我们建议使用析取范式作为一种新方法，从基于实例的患者数据中预测医院死亡率和90天死亡率，这些数据包括一大群因严重社区获得性肺炎入院患者的人口统计学、遗传学和生理学信息。我们开发了两种算法来有效地学习析取范式，从而产生易于解释的规则，这些规则将数据明确映射到感兴趣的结果。与一组先进的机器学习模型相比，析取范式实现了更高的预测性能质量，并揭示了标准方法无法获得的见解。析取范式构成了一组直观的预测规则，可以很容易地用于预测结果，并指导基于标准的临床决策和临床试验执行，因此比目前可用的预测工具具有更大的实际用途。该工具JavaDNF的Java实现将公开提供。
