title: Accelerate Healthcare Data Analytics: An Agile Practice to Perform Collaborative and Reproducible Analyses.
abstract: Recent advances in cloud computing and machine learning made it more convenient for researchers to gain insights from massive healthcare data, while performing analyses on healthcare data in current practice still lacks efficiency for researchers. What's more, collaborating among different researchers and sharing analysis results are challenging issues. In this paper, we developed a practice to make analytics process collaborative and analysis results reproducible by exploiting and extending Jupyter Notebook. After applying this practice in our use cases, we can perform analyses and deliver results with less efforts in shorter time comparing to our previous practice.
authors: IBM Research - China, Beijing, China.
authors: Bibo
authors: B
authors: Hao
authors: IBM Research - China, Beijing, China.
authors: Wen
authors: W
authors: Sun
authors: IBM Research - China, Beijing, China.
authors: Yiqin
authors: Y
authors: Yu
authors: IBM Research - China, Beijing, China.
authors: Jing
authors: J
authors: Li
authors: IBM Research - China, Beijing, China.
authors: Gang
authors: G
authors: Hu
authors: IBM Research - China, Beijing, China.
authors: Guotong
authors: G
authors: Xie
mesh_terms: D000067917:Cloud Computing; D003695:Delivery of Health Care / Q000706:statistics & numerical data*; D006801:Humans; D000069550:Machine Learning; D015203:Reproducibility of Results