Event Mining Driven Context-Aware Personal Food Preference Modelling

Pandey, Vaibhav; Rostami, Ali; Nag, Nitish; Jain, Ramesh

doi:10.1007/978-3-030-68821-9_52

Cited by 7 publications

(2 citation statements)

References 35 publications

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“…According to infodemiology, capturing real-time information and public attitudes can help decision makers to be prepared based on the feedback loop on public data and disease spread [7] and can provide a better estimation of a deadly disease such as COVID-19 in each state to distribute health care-related utilities such as ventilators. In addition, this information can be used to model and analyze food-and lifestyle-related behaviors at the global level based on real-time events [39][40][41].…”

Section: Principal Resultsmentioning

confidence: 99%

The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis (Preprint)

Mehrabadi¹,

Dutt²,

Rahmani³

2020

Preprint

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BACKGROUND The COVID-19 coronavirus pandemic has affected virtually every region of the globe. At the time of conducting this study, the number of daily cases in the United States is more than any other country, and the trend is increasing in most of its states. Google trends provide public interest in various topics during different periods. Analyzing these trends using data mining methods might provide useful insights and observations regarding the COVID-19 outbreak. OBJECTIVE The objective of this study was to consider the predictive ability of different search terms (i.e., bars and restaurants) with regards to the increase of daily cases in the US. In particular, we were concerned with searches for dine-in restaurants and bars. Data were obtained from Google trends API and COVID tracking project. METHODS To test causation of one time series on another, we used Granger’s Causality Test. We considered the causation of two different search query trends, namely restaurant and bars, on daily positive cases in top-10 states/territories of the United States with the highest and lowest daily new positive cases. In addition, to measure the linear relation of different trends, we used Pearson correlation. RESULTS Our results showed for states/territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly happened after re-opening, significantly affect the daily new cases, on average. California, for example, had most searches for restaurants on June 7th, 2020, which affected the number of new cases within two weeks after the peak with the P-value of .004 for Granger’s causality test. CONCLUSIONS Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases for regions with higher numbers of daily new cases in the United States. We showed that such influential search trends could be used as additional information for prediction tasks in new cases of each region. This prediction can help healthcare leaders manage and control the impact of COVID-19 outbreaks on society and be prepared for the outcomes.

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Section: Principal Resultsmentioning

confidence: 99%

The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis (Preprint)

Mehrabadi¹,

Dutt²,

Rahmani³

2020

Preprint

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“…It, then, builds health models to estimate the variations in the health parameters and generate step-by-step recommendations. In another study, Pandey et al ( 42 ) present a food preference modelling approach to generate food recommendations based on aspects of nutritional information, taste preferences, and contextual and social information.…”

Section: State-of-the-art Navigation Systems For Physical Healthmentioning

confidence: 99%

Personal mental health navigator: Harnessing the power of data, personal models, and health cybernetics to promote psychological well-being

Rahmani

Lai

Jafarlou

et al. 2022

Front. Digit. Health

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Current digital mental healthcare solutions conventionally take on a reactive approach, requiring individuals to self-monitor and document existing symptoms. These solutions are unable to provide comprehensive, wrap-around, customized treatments that capture an individual’s holistic mental health model as it unfolds over time. Recognizing that each individual requires personally tailored mental health treatment, we introduce the notion of Personalized Mental Health Navigation (MHN): a cybernetic goal-based system that deploys a continuous loop of monitoring, estimation, and guidance to steer the individual towards mental flourishing. We present the core components of MHN that are premised on the importance of addressing an individual’s personal mental health state. Moreover, we provide an overview of the existing physical health navigation systems and highlight the requirements and challenges of deploying the navigational approach to the mental health domain.

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Application of Virtual Reality Technology in the Visual Optimization of Print Advertisement based on Image Information Mining System

Liu

2022

2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS)

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Event Mining Driven Context-Aware Personal Food Preference Modelling

Cited by 7 publications

References 35 publications

The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis (Preprint)

The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis (Preprint)

Personal mental health navigator: Harnessing the power of data, personal models, and health cybernetics to promote psychological well-being

Application of Virtual Reality Technology in the Visual Optimization of Print Advertisement based on Image Information Mining System

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