Ko Kyung Dae, El-Ghazawi Tarek, Kim Dongkyu, Morizono Hiroki
High-Performance Computing Laboratory (HPCL), The George Washington University, Ashburn, VA, United States.
Center for Translational Science, Children's National Medical Center, Washington DC, United States.
IEEE Symp Comput Intell Bioinforma Comput Biol Proc. 2014 May;2014. doi: 10.1109/CIBCB.2014.6845506.
Motor neuron diseases (MNDs) are a class of progressive neurological diseases that damage the motor neurons. An accurate diagnosis is important for the treatment of patients with MNDs because there is no standard cure for the MNDs. However, the rates of false positive and false negative diagnoses are still very high in this class of diseases. In the case of Amyotrophic Lateral Sclerosis (ALS), current estimates indicate 10% of diagnoses are false-positives, while 44% appear to be false negatives. In this study, we developed a new methodology to profile specific medical information from patient medical records for predicting the progression of motor neuron diseases. We implemented a system using Hbase and the Random forest classifier of Apache Mahout to profile medical records provided by the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT) site, and we achieved 66% accuracy in the prediction of ALS progress.
运动神经元疾病（MNDs）是一类会损害运动神经元的进行性神经疾病。准确诊断对于MNDs患者的治疗至关重要，因为目前尚无针对MNDs的标准治愈方法。然而，在这类疾病中，假阳性和假阴性诊断率仍然很高。以肌萎缩侧索硬化症（ALS）为例，目前的估计表明10%的诊断为假阳性，而44%似乎为假阴性。在本研究中，我们开发了一种新方法，从患者病历中提取特定医疗信息，以预测运动神经元疾病的进展。我们使用Hbase和Apache Mahout的随机森林分类器实现了一个系统，用于分析汇总资源开放获取ALS临床试验数据库（PRO-ACT）站点提供的病历，并且在ALS进展预测中达到了66%的准确率。
