title: Machine Learning for Knowledge Extraction from PHR Big Data.
abstract: Cloud computing, Internet of things (IOT) and NoSQL database technologies can support a new generation of cloud-based PHR services that contain heterogeneous (unstructured, semi-structured and structured) patient data (health, social and lifestyle) from various sources, including automatically transmitted data from Internet connected devices of patient living space (e.g. medical devices connected to patients at home care). The patient data stored in such PHR systems constitute big data whose analysis with the use of appropriate machine learning algorithms is expected to improve diagnosis and treatment accuracy, to cut healthcare costs and, hence, to improve the overall quality and efficiency of healthcare provided. This paper describes a health data analytics engine which uses machine learning algorithms for analyzing cloud based PHR big health data towards knowledge extraction to support better healthcare delivery as regards disease diagnosis and prognosis. This engine comprises of the data preparation, the model generation and the data analysis modules and runs on the cloud taking advantage from the map/reduce paradigm provided by Apache Hadoop.
authors.affiliation: Department of Digital Systems, University of Piraeus, Piraeus.
authors.forename: Michaela
authors.initials: M
authors.lastname: Poulymenopoulou
authors.affiliation: Department of Digital Systems, University of Piraeus, Piraeus.
authors.forename: Flora
authors.initials: F
authors.lastname: Malamateniou
authors.affiliation: Department of Digital Systems, University of Piraeus, Piraeus.
authors.forename: George
authors.initials: G
authors.lastname: Vassilacopoulos
mesh_terms: D000067917:Cloud Computing; D057225:Data Mining / Q000379:methods*; D066264:Datasets as Topic; D057286:Electronic Health Records / Q000458:organization & administration*; D055991:Health Records, Personal; D058436:Knowledge Management; D000069550:Machine Learning; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods