Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data

Parkinson's disease is a neurodegenerative disease that can affect a person's movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson's disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson's disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson's disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson's disease.Comment: AAAI Conference on Artificial Intelligence 201

A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals

Managing the Ethical Dimensions of Brain-Computer Interfaces in eHealth: An SDLC-based Approach

A growing range of brain-computer interface (BCI) technologies is being employed for purposes of therapy and human augmentation. While much thought has been given to the ethical implications of such technologies at the ‘macro’ level of social policy and ‘micro’ level of individual users, little attention has been given to the unique ethical issues that arise during the process of incorporating BCIs into eHealth ecosystems. In this text a conceptual framework is developed that enables the operators of eHealth ecosystems to manage the ethical components of such processes in a more comprehensive and systematic way than has previously been possible. The framework’s first axis defines five ethical dimensions that must be successfully addressed by eHealth ecosystems: 1) beneficence; 2) consent; 3) privacy; 4) equity; and 5) liability. The second axis describes five stages of the systems development life cycle (SDLC) process whereby new technology is incorporated into an eHealth ecosystem: 1) analysis and planning; 2) design, development, and acquisition; 3) integration and activation; 4) operation and maintenance; and 5) disposal. Known ethical issues relating to the deployment of BCIs are mapped onto this matrix in order to demonstrate how it can be employed by the managers of eHealth ecosystems as a tool for fulfilling ethical requirements established by regulatory standards or stakeholders’ expectations. Beyond its immediate application in the case of BCIs, we suggest that this framework may also be utilized beneficially when incorporating other innovative forms of information and communications technology (ICT) into eHealth ecosystems