A Navigational Approach to Health: Actionable Guidance for Improved Quality of Life

Nag, Nitish; Jain, Ramesh

doi:10.1109/mc.2018.2883280

Cited by 29 publications

(33 citation statements)

References 10 publications

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“…However, we believe that with the advances in natural language processing and knowledge graphs, we may be able to automate this process by directly converting the insights from medical literature to events definition. The approach detailed in this paper facilitates continuous health state estimation and, eventually, continuous health navigation, as described in [33].…”

Section: Resultsmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

“…Over time, these changes accumulate and may present themselves as chronic diseases. In order to provide health navigation for individuals [33], we need to recognize all such events that are relevant for determining a user's health state. We have adopted a knowledge-driven approach to recognize and retrieve those events.…”

Section: Knowledge Driven Event Retrievalmentioning

confidence: 99%

Continuous Health Interface Event Retrieval

Pandey

Nag

Jain

2020

Proceedings of the 2020 International Conference on Multimedia Retrieval

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“…Unfortunately these metrics fail to capture the quality of recommendations in relationship to real world implementation for enjoyment or health. E ectively extending the food recommendation to incorporate the individual health state criteria and culinary avour and user preferences will be the next evolution of more personalized food recommendation [4,6,7,10,12].…”

Section: Related Workmentioning

confidence: 99%

“…Food computing collects data from multiple sources and involves tasks such as perception, recognition, retrieval, recommendation, prediction and monitoring of food intake. One of the key outcomes of food computing is understanding the relationship between dietary choices and health state [10,12]. A healthy diet promotes overall well-being and lowers the risk of chronic diseases.…”

Section: Introductionmentioning

confidence: 99%

“…A healthy diet promotes overall well-being and lowers the risk of chronic diseases. To aid in building a healthy diet, algorithms can potentially compute health scores for food items based on the users health status [10,12], the item nutritional features, along with context and other environmental factors [11]. However, healthy food su ers from the adoption problem, since healthy food can be in con ict with taste preferences [5].…”

Section: Introductionmentioning

confidence: 99%

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Flavour Enhanced Food Recommendation

Nag

Rao

Kulhalli

et al. 2019

Proceedings of the 5th International Workshop on Multimedia Assisted Dietary Management

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We propose a mechanism to use the features of avour to enhance the quality of food recommendations. An empirical method to determine the avour of food is incorporated into a recommendation engine based on major gustatory nerves. Such a system has advantages of suggesting food items that the user is more likely to enjoy based upon matching with their avour pro le through use of the taste biological domain knowledge. is preliminary intends to spark more robust mechanisms by which avour of food is taken into consideration as a major feature set into food recommendation systems. Our long term vision is to integrate this with health factors to recommend healthy and tasty food to users to enhance quality of life.

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