title: Predicting the severity of motor neuron disease progression using electronic health record data with a cloud computing Big Data approach.
abstract: 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.
authors.affiliation: High-Performance Computing Laboratory (HPCL), The George Washington University, Ashburn, VA, United States.
authors.forename: Kyung Dae
authors.initials: KD
authors.lastname: Ko
authors.affiliation: High-Performance Computing Laboratory (HPCL), The George Washington University, Ashburn, VA, United States.
authors.forename: Tarek
authors.initials: T
authors.lastname: El-Ghazawi
authors.affiliation: Center for Translational Science, Children's National Medical Center, Washington DC, United States.
authors.forename: Dongkyu
authors.initials: D
authors.lastname: Kim
authors.affiliation: Center for Genetic Medicine, Children's National Medical Center, Washington DC, United States.
authors.forename: Hiroki
authors.initials: H
authors.lastname: Morizono
keywords: ALS; Big data; Cloud computing; Hbase; Mahout; Medical decision support sytem; Randomforest