Improving biomedical signal search results in big data case-based reasoning environments

Woodbridge, Jonathan; Mortazavi, Bobak J.; Bui, Alex A. T.; Sarrafzadeh, Majid

doi:10.1016/j.pmcj.2015.09.006

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…LSH maps similar datapoints (cases) into same 'buckets' (encoded with low-dimensional binary codes) with high probability, which preserves the similarity relationships between datapoints and can be regarded as a way of dimensionality reduction on high-dimensional data [14]. Several studies introduce LSH into CBR systems to approximate the nearest neighbor search process and scale traditional CBR systems to large-scale data [29,30,70]. The studies show CBR systems equipped with hashing techniques can greatly improve retrieval efficiency and achieve desirable performance with expected loss in accuracy.…”

Section: Gaps Of Efficient Case-based Reasoningmentioning

confidence: 99%

Supervised Deep Hashing for High-dimensional and Heterogeneous Case-based Reasoning

Zhang¹,

Liang²,

Shi³

et al. 2022

Preprint

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Case-based Reasoning (CBR) on high-dimensional and heterogeneous data is a trending yet challenging and computationally expensive task in the real world. A promising approach is to obtain low-dimensional hash codes representing cases and perform a similarity retrieval of cases in a Hamming space. However, previous methods based on data-independent hashing rely on random projections or manual construction, inapplicable to address specific data issues (e.g., high-dimensionality and heterogeneity) due to their insensitivity to data characteristics. To address these issues, this work introduces a novel deep hashing network to learn similarity-preserving compact hash codes for efficient case retrieval and proposes a deep-hashing-enabled CBR model HeCBR. Specifically, we introduce position embedding to represent heterogeneous features and utilize a multilinear interaction layer to obtain case embeddings, which effectively filtrates zero-valued features to tackle high-dimensionality and sparsity and captures inter-feature couplings. Then, we feed the case embeddings into fully-connected layers, and subsequently a hash layer generates hash codes with a quantization regularizer to control the quantization loss during relaxation. To cater for incremental learning of CBR, we further propose an adaptive learning strategy to update the hash function. Extensive experiments on public datasets show HeCBR greatly reduces storage and significantly accelerates the case retrieval. HeCBR achieves desirable performance compared with the state-of-the-art CBR methods and performs significantly better than hashing-based CBR methods in classification.

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Section: Gaps Of Efficient Case-based Reasoningmentioning

confidence: 99%

Supervised Deep Hashing for High-dimensional and Heterogeneous Case-based Reasoning

Zhang¹,

Liang²,

Shi³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Beside the obligation to deal with large scale data that comes with ever-growing CBs, current availability of the tools to interpret big data is also encouraging CBR researchers to work on systems that could benefit from hundreds of millions of cases (e.g. [8,9]). Working with CBs of this scale could not be imagined until recently.…”

Section: Related Workmentioning

confidence: 99%

Fast anytime retrieval with confidence in large-scale temporal case bases

Mülâyim

Arcos

2020

Knowledge-Based Systems

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“…The advancement in biomedical building will bring about new symptomatic. Pervasive care [16], rather than the present doctor's facility based drug, intends to convey wellbeing benefits past healing facilities and into individual's everyday lives. All the more significantly, it underpins individualization by giving intends to get individual wellbeing data that are difficult to get inside healing centers.…”

Section: Biomedical Informatics Usage Needs Of Big Data Technologymentioning

confidence: 99%

Emerging Biomedical Health Care System by Using Internet of Things

Kalyanasundaram

Babu

2016

JBBBE

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Internet of Things and Big Data are critical passion to applying medical field. But both field interaction necessary for Bio Medical fields to improve the Doctor efficiency and it helps to serve patients in better way. In this paper mention that what are the important of the Bio Medical field linking with most recent Technology. Scientific relations to delaying with unstructured data analysis. Digital Device integration requirements for patients. Digital way user friendly communication with Doctor to patient. It helpful for finding disease and counseling patient complications reduce. Finally we achieved a better virtual environment creating with Doctor to patients for improving service.

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Improving biomedical signal search results in big data case-based reasoning environments

Cited by 8 publications

References 28 publications

Supervised Deep Hashing for High-dimensional and Heterogeneous Case-based Reasoning

Supervised Deep Hashing for High-dimensional and Heterogeneous Case-based Reasoning

Fast anytime retrieval with confidence in large-scale temporal case bases

Emerging Biomedical Health Care System by Using Internet of Things

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