Continuous Health Interface Event Retrieval

Pandey, Vaibhav; Nag, Nitish; Jain, Ramesh

doi:10.1145/3372278.3390705

Cited by 6 publications

(5 citation statements)

References 37 publications

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“…C defines the set of confounding variables and temporal conditions that might affect this relationship. For Biological-PFM, the input events are lifestyle events that have a causal impact on some observable biological outcome [40]. While, in the Preferential-PFM, the input events capture the contextual situations that affect the user's culinary preferences.…”

Section: Event Mining Results In Rules Of the Form Eventmentioning

confidence: 99%

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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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Section: Event Mining Results In Rules Of the Form Eventmentioning

confidence: 99%

“…Furthermore, Contextual understanding of the user needs must be layered for best computing real-time needs [32]. Other biological and life events may also impact the food events indirectly and needs to be added to the model [40].…”

Section: Personal Food Modelmentioning

confidence: 99%

Personal Food Model

Rostami

Pandey

Nag

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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“…We incorporated these general aspects into the categories of our modeling attributes and modified the physical, logical, and relative components to match those of daily activities. We developed an event mining system to identify temporal associations among events, which allowed us to build personalized models [73]. For instance, to understand an individual's social behavior, we must examine their locations (eg, the amount of time an individual spends at various places, such as a friend's home or parents' home).…”

Section: Personicle Appmentioning

confidence: 99%

Assessing the Mental Health of Emerging Adults Through a Mental Health App: Protocol for a Prospective Pilot Study

Yunusova¹,

Lai²,

Rivera³

et al. 2021

JMIR Res Protoc

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Background Individuals can experience different manifestations of the same psychological disorder. This underscores the need for a personalized model approach in the study of psychopathology. Emerging adulthood is a developmental phase wherein individuals are especially vulnerable to psychopathology. Given their exposure to repeated stressors and disruptions in routine, the emerging adult population is worthy of investigation. Objective In our prospective study, we aim to conduct multimodal assessments to determine the feasibility of an individualized approach for understanding the contextual factors of changes in daily affect, sleep, physiology, and activity. In other words, we aim to use event mining to predict changes in mental health. Methods We expect to have a final sample size of 20 participants. Recruited participants will be monitored for a period of time (ie, between 3 and 12 months). Participants will download the Personicle app on their smartphone to track their activities (eg, home events and cycling). They will also be given wearable sensor devices (ie, devices that monitor sleep, physiology, and physical activity), which are to be worn continuously. Participants will be asked to report on their daily moods and provide open-ended text responses on a weekly basis. Participants will be given a battery of questionnaires every 3 months. Results Our study has been approved by an institutional review board. The study is currently in the data collection phase. Due to the COVID-19 pandemic, the study was adjusted to allow for remote data collection and COVID-19–related stress assessments. Conclusions Our study will help advance research on individualized approaches to understanding health and well-being through multimodal systems. Our study will also demonstrate the benefit of using individualized approaches to study interrelations among stress, social relationships, technology, and mental health. International Registered Report Identifier (IRRID) DERR1-10.2196/25775

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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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Continuous Health Interface Event Retrieval

Cited by 6 publications

References 37 publications

Personal Food Model

Personal Food Model

Assessing the Mental Health of Emerging Adults Through a Mental Health App: Protocol for a Prospective Pilot Study

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

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