A multi-centrality index for graph-based keyword extraction

Vega-Oliveros, Didier A.; Gomes, Pedro Spoljaric; Milios, Evangelos; Berton, Lilian

doi:10.1016/j.ipm.2019.102063

Cited by 77 publications

(51 citation statements)

References 15 publications

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“…Top topics, including health problems, AI technology, function, and population, were identified and described by frequency, percentage, and citation rate of keywords listed by the authors. The centrality of a keyword was a combination of statistic equations that measure the representativeness of selected words for the text content according to betweenness, closeness, degree, eigenvector, PageRank (Google LLC), eccentricity, coreness, clustering coefficient, and term frequency scores [ 16 ]. The centralities of keywords were computed using HistCite.…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence in Health Care: Bibliometric Analysis

Guo¹,

Hao²,

Zhao³

et al. 2020

J Med Internet Res

300

153

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Background As a critical driving power to promote health care, the health care–related artificial intelligence (AI) literature is growing rapidly. Objective The purpose of this analysis is to provide a dynamic and longitudinal bibliometric analysis of health care–related AI publications. Methods The Web of Science (Clarivate PLC) was searched to retrieve all existing and highly cited AI-related health care research papers published in English up to December 2019. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility, using the abstract and full text where needed. The growth rate of publications, characteristics of research activities, publication patterns, and research hotspot tendencies were computed using the HistCite software. Results The search identified 5235 hits, of which 1473 publications were included in the analyses. Publication output increased an average of 17.02% per year since 1995, but the growth rate of research papers significantly increased to 45.15% from 2014 to 2019. The major health problems studied in AI research are cancer, depression, Alzheimer disease, heart failure, and diabetes. Artificial neural networks, support vector machines, and convolutional neural networks have the highest impact on health care. Nucleosides, convolutional neural networks, and tumor markers have remained research hotspots through 2019. Conclusions This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care–related AI research and possible practice implications. Future AI research should be dedicated to filling in the gaps between AI health care research and clinical applications.

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Section: Methodsmentioning

confidence: 99%

Artificial Intelligence in Health Care: Bibliometric Analysis

Guo¹,

Hao²,

Zhao³

et al. 2020

J Med Internet Res

300

153

View full text Add to dashboard Cite

show abstract

“…Therefore, the links represent events in a chronological occurrence, which we call, for the sake of simplicity, as chronnet. In this way, we generalize the previously reported mechanism of connections 3 , 15 , 16 transforming the spatiotemporal data set into a network, as explored in other domains like text-mining 17 , 18 and social media 19 , 20 .…”

Section: Introductionmentioning

confidence: 79%

Spatiotemporal data analysis with chronological networks

et al. 2020

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The number of spatiotemporal data sets has increased rapidly in the last years, which demands robust and fast methods to extract information from this kind of data. Here, we propose a network-based model, called Chronnet, for spatiotemporal data analysis. The network construction process consists of dividing a geometric space into grid cells represented by nodes connected chronologically. Strong links in the network represent consecutive recurrent events between cells. The chronnet construction process is fast, making the model suitable to process large data sets. Using artificial and real data sets, we show how chronnets can capture data properties beyond simple statistics, like frequent patterns, spatial changes, outliers, and spatiotemporal clusters. Therefore, we conclude that chronnets represent a robust tool for the analysis of spatiotemporal data sets.

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“…Several studies have successfully analyzed their domain problem by adopting the NS framework. Transforming the dataset into a network has allowed finding many interesting patterns and results in different areas, like text-mining [36], health sciences [37], stock markets, among many others [38]. In the case of Earth sciences, the network representation has been largely used for analyzing global climate effects and teleconnections [20], [25], wild-fires events [39], anomalies in annual hurricanes events [40], seismic events [41], and continental moisture recycling process [42].…”

Section: Related Workmentioning

confidence: 99%

“…The proposal and novelty of this work are to employ classification for ENSO analysis using high-level topological features from the temporal networks. It is possible to construct networks from any dataset [8], [21], [36], [39], to generate elaborated features that feed a classification algorithm. More topological features allow more possibilities to be exploited than using only the time-series values to perform the predictions [8].…”

Section: Research Problemmentioning

confidence: 99%

Classifying El Niño-Southern Oscillation Combining Network Science and Machine Learning

et al. 2020

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Machine learning and complex network theory have emerged as crucial tools to extract meaningful information from big data, especially those related to complex systems. In this work, we aim to combine them to analyze El Niño Southern Oscillation (ENSO) phases. This non-linear phenomenon consists of anomalous (de)increase of temperature at the tropical Pacific Ocean, which has irregular occurrence and causes climatic variability worldwide. We construct temporal Climate Networks from the Surface Air Temperature time-series and calculate network metrics to characterize the warm and cold ENSO episodes. The metrics are used as topological features for classification. We employ ten classifiers and achieved 80% AUC ROC when predicting the intensity of Strong/ Weak El Niño and Strong/ Weak La Niña for the next season. The complex network represents the relationship among different regions of the planet and machine learning creates models to classify the different classes of ENSO. This work opens new paths of research by integrating network science and machine learning to analyze complex data like global climate systems.

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A multi-centrality index for graph-based keyword extraction

Cited by 77 publications

References 15 publications

Artificial Intelligence in Health Care: Bibliometric Analysis

Artificial Intelligence in Health Care: Bibliometric Analysis

Spatiotemporal data analysis with chronological networks

Classifying El Niño-Southern Oscillation Combining Network Science and Machine Learning

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