Healthcare Big Data Voice Pathology Assessment Framework

Abstract

The fast-growing healthcare big data plays an important role in healthcare service providing. Healthcare big data comprises data from different structured, semi-structured and unstructured sources. These data sources vary in terms of heterogeneity, volume, variety, velocity and value that traditional frameworks, algorithms, tools, and techniques are not fully capable of handling. Therefore, a framework is required that facilitates collection, extraction, storage, classification, processing, and modelling of this vast heterogeneous volume of data. The present paper proposes a healthcare big data framework using voice pathology assessment (VPA) as a case study. In the proposed VPA system, two robust features, MPEG-7 low-level audio and the interlaced derivative pattern (IDP), are used for processing the voice or speech signals. The machine learning algorithms in the form of a support vector machine (SVM), an extreme learning machine (ELM), and a Gaussian mixture model (GMM) are used as the classifier.In the experiments, the proposed VPA system shows its efficiency in terms of accuracy and time requirement. . His research interests include serious games, social media, IoT, cloud and multimedia for healthcare, smart health, and resource provisioning for big data processing on media clouds. He has authored and coauthored around 120 publications including refereed IEEE/ACM/Springer/Elsevier journals, conference papers, books, and book chapters. He has served as a member of the organizing and technical committees of several international conferences and workshops. He has served as co-chair, general chair, workshop chair, publication chair, and TPC for over 12 IEEE and ACM conferences and workshops. Currently, he serves as a co-chair of the 6th IEEE ICME workshop on Multimedia Services

Digi-HTA: Health technology assessment framework for digital healthcare services

“…The literature review showed that there is a lack of HTA frameworks for AI because it is a new era in HTA, and for that reason, new HTA criteria and processes are needed [59]. Assessment of the systems could be very complex, since their input data could be from multiple sources and an adaptive AI-based algorithm learns continuously and becomes more effective over time [59,60]. AI brings a new perspective to decision-making in support of healthcare professionals' own decisions, so its decisions must be trustworthy and transparent [61][62][63].…”

“…For this reason, we left them out, as our aim is to create a tool to provide fast assessments in a rapidly developing technology sector. The Digi-HTA domains and criteria are presented in Table 1.The detailed criteria in our framework are based on aspects of the mini-HTA questionnaire, ideas from NHS DAQ as well as information from literature, interviews, and workshops [9,29,31,[39][40][41][42][43]46,[55][56][57][58][59][60][61][62][63][64][67][68][69][70][71].…”

A 5G Cognitive System for Healthcare

“…In [19], the authors proposed an emotional cognitive system based on facial expression recognition, and discussed the inference mechanism of a cognitive system. Hossain et al, discussed the design of an emotional cognitive system based on voice [26,28] and facial expression [26]. Chen et al [23] utilized a pillow robot, a 5G network as well as a cloud platform to achieve real-time emotional interaction between remote users.…”

“…With regard to healthcare, there are three different domains of exploiting cognitive computing techniques, i.e., physiological healthcare [20,24,25], psychological healthcare [7,19,23,26,28] as well as medicine analysis [21,22]. In order to provide an advanced healthcare service.…”

“…Some studies on cognitive computing [11][12][13][14][15][16] on the basis of cloud computing, big data and machine learning have been done [5,17,18], some of which have also been applied to the domain of healthcare [7,[19][20][21][22][23][24][25][26][27][28]. Pozna et al [11] proposed a novel method of cognitive system strategy, zeroing in on the design of a new human knowledge model, considering a new data structure to organize and operate relative information.…”