Bridging the Vocabulary Gap between Health Seekers and Healthcare Knowledge

Nie, Liqiang; Zhao, Yiliang; Akbari, Mohammad; Shen, Jialie; Chua, Tat-Seng

doi:10.1109/tkde.2014.2330813

Cited by 112 publications

(29 citation statements)

References 29 publications

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“…Typical examples are graph-based model [76], self-training [77], co-training [78], generative model [79], low-density separation [80], and heuristic model [81].…”

Section: Semi-supervised Learningmentioning

confidence: 99%

“…Three kinds of optimization algorithms, evolutionary [8][9][10][11][12][13][14][15][16][17], stochastic [18][19][20][21][22][23][24][25][26][27][28][29] and combinatorial optimization [30][31][32][33][34][35][36][37][38] will be addressed. For machine learning algorithms, the discussion is based on un-supervised learning [39][40][41][42][43][44][45][46][47][48][49], supervised learning and semi-supervised learning [71][72][73][74][75][76][77][78]…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

et al. 2017

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Abstract:To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer's disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed.

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“…Typical examples are graph-based model [76], self-training [77], co-training [78], generative model [79], low-density separation [80], and heuristic model [81].…”

Section: Semi-supervised Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

et al. 2017

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“…In addition, users with diverse backgrounds do not necessarily share the same vocabulary in CHSs [52]. Oftentimes, the same medical subjects may be colloquially expressed with distinct medical concepts.…”

Section: A Data Collection and Feature Extractionmentioning

confidence: 99%

“…To alleviate such problems, we employed the MetaMap tool [53] to detect medical attributes that are noun phrases in the health domain, and then normalized them to standardized terminologies in the SNOMED CT Metathesaurus. 10 The work in [52] detailed this procedure. The semantic types of these terminologies span from symptom, treatment, medication, body parts, to demographics.…”

Section: A Data Collection and Feature Extractionmentioning

confidence: 99%

Multiview Physician-Specific Attributes Fusion for Health Seeking

Nie¹,

Zhang

Yan

et al. 2017

IEEE Trans. Cybern.

Self Cite

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Abstract-Community-based health services have risen as important online resources for resolving users health concerns. Despite the value, the gap between what health seekers with specific health needs and what busy physicians with specific attitudes and expertise can offer is being widened. To bridge this gap, we present a question routing scheme that is able to connect health seekers to the right physicians. In this scheme, we first bridge the expertise matching gap via a probabilistic fusion of the physician-expertise distribution and the expertise-question distribution. The distributions are calculated by hypergraphbased learning and kernel density estimation. We then measure physicians attitudes toward answering general questions from the perspectives of activity, responsibility, reputation, and willingness. At last, we adaptively fuse the expertise modeling and attitude modeling by considering the personal needs of the health seekers. Extensive experiments have been conducted on a real-world dataset to validate our proposed scheme.Index Terms-Adaptive fusion, attitude modeling, communitybased health services (CHSs), expertise modeling, question routing.

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“…While the vocabulary gap between health seekers and providers has hindered the cross-system operability and the inter-user reusability. Nie et al [25] presented a novel scheme to code the medical records by jointly utilizing local mining and global learning approaches, which were tightly linked and mutually reinforced. Nie et al [26] proposed a scheme accurately and efficiently inferring diseases especially for community-based health services.…”

Section: Introductionmentioning

confidence: 99%

Monocular scene flow estimation via variational method

Xiao

Yang

et al. 2015

Multimed Tools Appl

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Scene flow provides the 3D motion field of point clouds, which correspond to image pixels. Current algorithms usually need complex stereo calibration before estimating flow, which has strong restrictions on the position of the camera. This paper proposes a monocular camera scene flow estimation algorithm. Firstly, an energy functional is constructed, where three important assumptions are turned into data terms derivation: a brightness constancy assumption, a gradient constancy assumption, and a short time object velocity constancy assumption. Two smooth operators are used as regularization terms. Then, an occluded map computation algorithm is used to ensure estimating scene flow only on un-occluded points. After that, the energy functional is solved with a coarse-to-fine variational equation on Gaussian pyramid, which can prevent the iteration from converging to a local minimum value. The experiment results show that the algorithm can use three sequential frames at least to get scene flow in world coordinate, without optical flow or disparity inputting.

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Bridging the Vocabulary Gap between Health Seekers and Healthcare Knowledge

Cited by 112 publications

References 29 publications

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Multiview Physician-Specific Attributes Fusion for Health Seeking

Monocular scene flow estimation via variational method

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