Large-scale multimodal mining for healthcare with mapreduce

Recent advances in healthcare and bioscience technologies and proliferation of portable medical devices are producing massive amounts of multimodal data. The need for parallel processing is apparent for mining these data sets, which can range anywhere from tens of gigabytes, to terabytes or even petabytes. AALIM (Advanced Analytics for Information Management) is a new multimodal mining-based clinical decision support system that brings together patient data captured in many modalities to provide a holistic presentation of a patient's exam data, diseases, and medications. In addition, it offers disease-specific similarity search based on various data modalities. Due to the complex nature of the processing in AALIM, the current deployed AALIM system can only handle a limited amount of patient data per day. In this paper, we attempt to address this challenge by porting the multimodal mining system on top of the MapReduce framework, specifically its popular open-source implementation...

• Type: Journal
• Year: 2010
• Where: IHI
• Authors: Fei Wang, Vuk Ercegovac, Tanveer Fathima Syeda-Mahmood, Akintayo Holder, Eugene J. Shekita, David Beymer, Lin Hao Xu
• Keywords: AALIM, Current Deployed Aalim, Healthcare, IHI 2010, Patient Data