Toward Supporting Food Journaling Using Air Quality Data Mining and a Social Robot

Gerina, Federica; Pes, Barbara; Recupero, Diego Reforgiato; Riboni, Daniele

doi:10.1007/978-3-030-34255-5_22

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…Dietary data can also be automatically "mined" through ML approaches from social media for ecological studies to study community-level health outcomes (Shah et al [29]). In another study (Gerina et al [30]), a ML model was used to detect cooking activities based on air quality sensor data. The model's output was integrated with a social robot that, by interacting with the participant, supported filling in a food diary.…”

Section: Applications and Common Pitfallsmentioning

confidence: 99%

Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology

Russo

Bonassi

2022

Nutrients

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Nutritional epidemiology employs observational data to discover associations between diet and disease risk. However, existing analytic methods of dietary data are often sub-optimal, with limited incorporation and analysis of the correlations between the studied variables and nonlinear behaviours in the data. Machine learning (ML) is an area of artificial intelligence that has the potential to improve modelling of nonlinear associations and confounding which are found in nutritional data. These opportunities notwithstanding, the applications of ML in nutritional epidemiology must be approached cautiously to safeguard the scientific quality of the results and provide accurate interpretations. Given the complex scenario around ML, judicious application of such tools is necessary to offer nutritional epidemiology a novel analytical resource for dietary measurement and assessment and a tool to model the complexity of dietary intake and its relation to health. This work describes the applications of ML in nutritional epidemiology and provides guidelines to avoid common pitfalls encountered in applying predictive statistical models to nutritional data. Furthermore, it helps unfamiliar readers better assess the significance of their results and provides new possible future directions in the field of ML in nutritional epidemiology.

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Section: Applications and Common Pitfallsmentioning

confidence: 99%