Ubiquitous Event Mining to Enhance Personal Health

Pandey, Vaibhav; Nag, Nitish; Jain, Ramesh

doi:10.1145/3267305.3267684

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…Many of these applications collect data without user intervention, which is one of the reasons for the widespread adoption of such systems and allows us to build future applications on top. Aggregation of events and data of different modalities and from different sources is necessary to fully utilize different ubiquitous systems tracking and measuring different aspects of our lives [38]. We have to combine the incoming data in one log for the individual, which can then be utilized to identify their habits and how those impact the user's life.…”

Section: Events Aggregation Frameworkmentioning

confidence: 99%

Continuous Health Interface Event Retrieval

Pandey

Nag

Jain

2020

Proceedings of the 2020 International Conference on Multimedia Retrieval

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Knowing the state of our health at every moment in time is critical for advances in health science. Using data obtained outside an episodic clinical setting is the first step towards building a continuous health estimation system. In this paper, we explore a system that allows users to combine events and data streams from different sources and retrieve complex biological events, such as cardiovascular volume overload, using measured lifestyle events. These complex events, which have been explored in biomedical literature and which we call interface events, have a direct causal impact on the relevant biological systems; they are the interface through which the lifestyle events influence our health. We retrieve the interface events from existing events and data streams by encoding domain knowledge using the event operator language. The interface events can then be utilized to provide a continuous estimate of the biological variables relevant to the user's health state. The event-based framework also makes it easier to estimate which event is causally responsible for a particular change in the individual's health state.

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Section: Events Aggregation Frameworkmentioning

confidence: 99%

Continuous Health Interface Event Retrieval

Pandey

Nag

Jain

2020

Proceedings of the 2020 International Conference on Multimedia Retrieval

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“…Event mining allows us to find patterns and relationships between different events in our daily lives. We can find relationships between different events in a person's lifelog data and derive an explainable personal model [39].…”

Section: Event Miningmentioning

confidence: 99%

Personal Food Model

Rostami

Pandey

Nag

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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Food is central to life. Food provides us with energy and foundational building blocks for our body and is also a major source of joy and new experiences. A significant part of the overall economy is related to food. Food science, distribution, processing, and consumption have been addressed by different communities using silos of computational approaches [29]. In this paper, we adopt a person-centric multimedia and multimodal perspective on food computing and show how multimedia and food computing are synergistic and complementary. Enjoying food is a truly multimedia experience involving sight, taste, smell, and even sound, that can be captured using a multimedia food logger. The biological response to food can be captured using multimodal data streams using available wearable devices. Central to this approach is the Personal Food Model. Personal Food Model is the digitized representation of the food-related characteristics of an individual. It is designed to be used in food recommendation systems to provide eating-related recommendations that improve the user's quality of life. To model the food-related characteristics of each person, it is essential to capture their food-related enjoyment using a Preferential Personal Food Model and their biological response to food using their Biological Personal Food Model. Inspired by the power of 3-dimensional color models for visual processing, we introduce a 6-dimensional taste-space for capturing culinary characteristics as well as personal preferences. We use event mining approaches to Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for thirdparty components of this work must be honored. For all other uses, contact the owner/author(s).

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“…Beyond the study of disease at the macro level with population digital epidemiology, future work should investigate diseases, such as dry eye disease, at the micro level for individual patients. With lifestyle pattern tracking in todays world, patients can collect personal data including daily habits, climate, geospatial data, and screen time [41], [27], [37], [38]. In the future, this type of data can one day be incorporated to monitor and advise on an individuals personal eye health state [30], [25], [28].…”

Section: Variablesmentioning

confidence: 99%

A Novel Epidemiological Approach to Geographically Mapping Population Dry Eye Disease in the United States through Google Trends

Azzam¹,

Nag²,

Tran³

et al. 2020

Preprint

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Dry eye disease (DED) affects approximately half of the United States population. DED is characterized by dryness on the corena surface due to a variety of causes. This study fills the spatiotemporal gaps in DED epidemiology by using Google Trends as a novel epidemiological tool for geographically mapping DED in relation to environmental risk factors. We utilized Google Trends to extract DED-related queries estimating user intent from 2004-2019 in the United States. We incorporated national climate data to generate heat maps comparing geographic, temporal, and environmental relationships of DED. Multi-variable regression models were constructed to generate quadratic forecasts predicting DED and control searches. Our results illustrated the upward trend, seasonal pattern, environmental influence, and spatial relationship of DED search volume across US geography. Localized patches of DED interest were visualized along the coastline. There was no significant difference in DED queries across US census regions. Regression model 1 predicted DED searches over time (R 2 =0.97) with significant predictors being control queries (p=0.0024), time (p=0.001), and seasonality (Winter p=0.0028; Spring p¡0.001; Summer p=0.018). Regression model 2 predicted DED queries per state (R 2 =0.49) with significant predictors being temperature (p=0.0003) and coastal zone (p=0.025). Importantly, temperature, coastal status, and seasonality were stronger risk factors of DED searches than humidity, sunshine, pollution, or region as clinical literature may suggest. Our work paves the way for future exploration of geographic information systems for locating DED and other diseases via online search query metrics.

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Ubiquitous Event Mining to Enhance Personal Health

Cited by 6 publications

References 4 publications

Continuous Health Interface Event Retrieval

Continuous Health Interface Event Retrieval

Personal Food Model

A Novel Epidemiological Approach to Geographically Mapping Population Dry Eye Disease in the United States through Google Trends

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