Hiroshi Mamiya
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View article: Quantifying the uncertainty of human activity recognition using a Bayesian machine learning method: a prediction study
Quantifying the uncertainty of human activity recognition using a Bayesian machine learning method: a prediction study Open
[Purpose] Machine learning methods accurately predict physical activity outcomes using accelerometer data generated by wearable devices. Thus, they allow investigation of the impact of the built environment on population physical activity.…
View article: How has Aggregated Mobility Data-informed public health research?
How has Aggregated Mobility Data-informed public health research? Open
View article: Characterizing co-purchased food products with soda, fresh fruits, and fresh vegetables using loyalty card purchasing data in Montréal, Canada, 2015–2017
Characterizing co-purchased food products with soda, fresh fruits, and fresh vegetables using loyalty card purchasing data in Montréal, Canada, 2015–2017 Open
Background Foods are not purchased in isolation but are normally co-purchased with other food products. The patterns of co-purchasing associations across a large number of food products have been rarely explored to date. Knowledge of such …
View article: Exploring Bias and Prediction Metrics to Characterise the Fairness of Machine Learning for Equity-Centered Public Health Decision-Making: A Narrative Review
Exploring Bias and Prediction Metrics to Characterise the Fairness of Machine Learning for Equity-Centered Public Health Decision-Making: A Narrative Review Open
Background: The rapid advancement of Machine Learning (ML) represents novel opportunities to enhance public health research, surveillance, and decision-making. However, there is a lack of comprehensive understanding of algorithmic bias, sy…
View article: Exploring Bias and Prediction Metrics to Characterise the Fairness of Machine Learning for Equity-Centered Public Health Decision-Making: A Narrative Review
Exploring Bias and Prediction Metrics to Characterise the Fairness of Machine Learning for Equity-Centered Public Health Decision-Making: A Narrative Review Open
The rapid advancement of Machine Learning (ML) represents novel opportunities to enhance public health research, surveillance, and decision-making. However, there is a lack of comprehensive understanding of algorithmic bias — systematic er…
View article: Quantifying the Uncertainty of Human Activity Recognition Using a Bayesian Machine Learning Method: A Prediction Study
Quantifying the Uncertainty of Human Activity Recognition Using a Bayesian Machine Learning Method: A Prediction Study Open
Background Machine learning methods accurately predict physical activity outcomes using accelerometer data generated by wearable devices, thus allowing the investigation of the impact of built environment on population physical activity. W…
View article: The temporal sequence between gentrification and cycling infrastructure expansions in Montreal, Canada
The temporal sequence between gentrification and cycling infrastructure expansions in Montreal, Canada Open
This work was supported by the Canadian Institutes of Health Research under award number PJT-165955.
View article: Large-scale characterization of co-purchased food products with soda, fruits, and vegetables: association rule mining on longitudinal loyalty card grocery purchasing data in Montréal, Canada. (Preprint)
Large-scale characterization of co-purchased food products with soda, fruits, and vegetables: association rule mining on longitudinal loyalty card grocery purchasing data in Montréal, Canada. (Preprint) Open
BACKGROUND Foods are not purchased in isolation but are normally co-purchased with other food products. The patterns of co-purchasing associations across a large number of food products are not known. OBJECTIVE To quantify the associati…
View article: Estimating the lagged effect of price discounting: a time-series study on sugar sweetened beverage purchasing in a supermarket
Estimating the lagged effect of price discounting: a time-series study on sugar sweetened beverage purchasing in a supermarket Open
View article: Revisiting Transfer Functions: Learning About a Lagged Exposure-Outcome Association in Time-Series Data
Revisiting Transfer Functions: Learning About a Lagged Exposure-Outcome Association in Time-Series Data Open
HINTS AND KINKS Int J Public Health, 11 July 2022 https://doi.org/10.3389/ijph.2022.1604841
View article: Transfer functions: learning about a lagged exposure-outcome association in time-series data
Transfer functions: learning about a lagged exposure-outcome association in time-series data Open
Many population exposures in time-series analysis, including food marketing, exhibit a time-lagged association with population health outcomes such as food purchasing. A common approach to measuring patterns of associations over different …
View article: Estimating the lagged effect of price discounting: a time-series study using transaction data of sugar sweetened beverages
Estimating the lagged effect of price discounting: a time-series study using transaction data of sugar sweetened beverages Open
Background Price discount is an unregulated obesogenic environmental risk factor for the purchasing of unhealthy food, including Sugar Sweetened Beverages (SSB). Sales of price discounted food items are known to increase during the period …
View article: Price discounting as a hidden risk factor of energy drink consumption
Price discounting as a hidden risk factor of energy drink consumption Open
View article: Application of Machine Learning and Grocery Transaction Data to Forecast Effectiveness of Beverage Taxation
Application of Machine Learning and Grocery Transaction Data to Forecast Effectiveness of Beverage Taxation Open
Sugar Sweetened Beverages (SSB) are the primary source of artificially added sugar and have a casual association with chronic diseases. Taxation of SSB has been proposed, but limited evidence exists to guide this public health policy. Groc…
View article: Susceptibility to price discounting of soda by neighbourhood educational status: an ecological analysis of disparities in soda consumption using point-of-purchase transaction data in Montreal, Canada
Susceptibility to price discounting of soda by neighbourhood educational status: an ecological analysis of disparities in soda consumption using point-of-purchase transaction data in Montreal, Canada Open
Price discounting is an important environmental risk factor for soda purchasing and can widen education inequalities in excess sugar intake across levels of education. Interventions to regulate price discounting warrant further investigati…
View article: A novel application of point-of-sales grocery transaction data to enhance community nutrition monitoring.
A novel application of point-of-sales grocery transaction data to enhance community nutrition monitoring. Open
Unhealthy eating is the most important preventable cause of global death and disability. Effective development and evaluation of preventive initiatives and the identification of disparities in dietary patterns require surveillance of nutri…
View article: Online Public Health Intelligence: Ethical Considerations at the Big Data Era
Online Public Health Intelligence: Ethical Considerations at the Big Data Era Open
View article: Towards Automated Risk-Factor Surveillance: Using Digital Grocery Purchasing Data to Measure Socioeconomic Inequalities in the Impact of In-Store Price Discounts on Dietary Choice
Towards Automated Risk-Factor Surveillance: Using Digital Grocery Purchasing Data to Measure Socioeconomic Inequalities in the Impact of In-Store Price Discounts on Dietary Choice Open
We explored the utility of electronic grocery store transaction data to assess the impact of in-store discounting of soda, a previously unknown risk factor of obesity and overweight, which cause substantial societal and economic burden wor…
View article: Digital Sources of Food Purchasing Data for the Surveillance of Dietary Patterns
Digital Sources of Food Purchasing Data for the Surveillance of Dietary Patterns Open
Food consumption data gathered at a fine enough spatial and temporal resolution is essential for the effective delivery and evaluation of diet-related public health interventions. Currently, the standard for food consumption data is food s…