Health Multimedia

Nag, Nitish; Pandey, Vaibhav; Jain, Ramesh

doi:10.1145/3078971.3080545

Cited by 29 publications

(7 citation statements)

References 21 publications

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“…As age increases, we also see a reduction in crime and noise pollution, suggesting that older individuals live in safer and quieter neighborhoods. We also see circadian rhythm disruption maximally in the middle ages (20)(21)(22)(23)(24)(25)(26)(27)(28)(29), suggested a more erratic lifestyle for those in their twenties. Circulation and metabolism scores also trend (including VO2 Max / CRF) lower as age increases.…”

Section: Rq2: Cross-modal Heart Health Statementioning

confidence: 78%

“…Health state estimation and tracking has been an important field in medical literature and computer science. There has been a strong call by the medical science community to use continuous multimodal data for tracking individual health [29,30,36,43,48]. Most modern metrics that are used to understand patient health were derived from longitudinal studies of large cohorts to see what led to morbidity and mortality.…”

Section: Related Workmentioning

confidence: 99%

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Cross-Modal Health State Estimation

Nag

Pandey

Putzel

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

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Individuals create and consume more diverse data about themselves today than any time in history. Sources of this data include wearable devices, images, social media, geo-spatial information and more. A tremendous opportunity rests within cross-modal data analysis that leverages existing domain knowledge methods to understand and guide human health. Especially in chronic diseases, current medical practice uses a combination of sparse hospital based biological metrics (blood tests, expensive imaging, etc.) to understand the evolving health status of an individual. Future health systems must integrate data created at the individual level to better understand health status perpetually, especially in a cybernetic framework. In this work we fuse multiple user created and open source data streams along with established biomedical domain knowledge to give two types of quantitative state estimates of cardiovascular health. First, we use wearable devices to calculate cardiorespiratory fitness (CRF), a known quantitative leading predictor of heart disease which is not routinely collected in clinical settings. Second, we estimate inherent genetic traits, living environmental risks, circadian rhythm, and biological metrics from a diverse dataset. Our experimental results on 24 subjects demonstrate how multi-modal data can provide personalized health insight. Understanding the dynamic nature of health status will pave the way for better health based recommendation engines, better clinical decision making and positive lifestyle changes.

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Section: Rq2: Cross-modal Heart Health Statementioning

confidence: 78%

Section: Related Workmentioning

confidence: 99%

Cross-Modal Health State Estimation

Nag

Pandey

Putzel

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

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“…Navigation systems have been also introduced for diabetic patients. For instance, DiaNavi (i.e., Diabetes Navigator) ( 40 , 41 ) is a lifestyle guidance system proposed for patients with Type II diabetes with the aim of continuously monitoring blood sugar and mitigating drug dependency. The system captures nutritional parameters, calories out, blood glucose level, etc.…”

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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“…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]. This individual monitoring allows for personal health navigation to control the health state of the eye to the best potential for a certain individual [26], [29], [25]. Several limitations are present in our study.…”

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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Health Multimedia

Cited by 29 publications

References 21 publications

Cross-Modal Health State Estimation

Cross-Modal Health State Estimation

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

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

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