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Enhancing Interpretable, Transparent, and Unobtrusive Detection of Acute Marijuana Intoxication in Natural Environments: Harnessing Smart Devices and Explainable AI to Empower Just-In-Time Adaptive Interventions: Longitudinal Observational Study

Sang Won Bae, Tammy Chung, Tongze Zhang, Anind K Dey, Rahul Islam

JMIR AI 2025;4:e52270

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Identifying Links Between Productivity and Biobehavioral Rhythms Modeled From Multimodal Sensor Streams: Exploratory Quantitative Study

Other methods such as multiple interpolations and Expectation-Maximization estimation introduce cross-correlation between features, and regression estimation and k-nearest neighbor increase auto-correlation of a single sensor feature [35,36]. However, the moving average method is sensitive to the number of continuous missing data. If the missing block is large, the moving average will introduce high noise and bias, and the data may need to be removed instead of imputed.

Runze Yan, Xinwen Liu, Janine M Dutcher, Michael J Tumminia, Daniella Villalba, Sheldon Cohen, John D Creswell, Kasey Creswell, Jennifer Mankoff, Anind K Dey, Afsaneh Doryab

JMIR AI 2024;3:e47194

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Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study

Sang Won Bae, Brian Suffoletto, Tongze Zhang, Tammy Chung, Melik Ozolcer, Mohammad Rahul Islam, Anind K Dey

JMIR Form Res 2023;7:e39862

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A Real-Time Mobile Intervention to Reduce Sedentary Behavior Before and After Cancer Surgery: Pilot Randomized Controlled Trial

Carissa A Low, Michaela Danko, Krina C Durica, Julio Vega, Meng Li, Abhineeth Reddy Kunta, Raghu Mulukutla, Yiyi Ren, Susan M Sereika, David L Bartlett, Dana H Bovbjerg, Anind K Dey, John M Jakicic

JMIR Perioper Med 2023;6:e41425

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Digital Biomarkers of Symptom Burden Self-Reported by Perioperative Patients Undergoing Pancreatic Surgery: Prospective Longitudinal Study

We evaluated a number of different binary classifiers, including logistic regression, k-nearest neighbors, support vector machine, random forest, gradient boosting, extreme gradient boosting, and Light GBM.

Carissa A Low, Meng Li, Julio Vega, Krina C Durica, Denzil Ferreira, Vernissia Tam, Melissa Hogg, Herbert Zeh III, Afsaneh Doryab, Anind K Dey

JMIR Cancer 2021;7(2):e27975

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A Real-Time Mobile Intervention to Reduce Sedentary Behavior Before and After Cancer Surgery: Usability and Feasibility Study

Carissa A Ann Low, Michaela Danko, Krina C Durica, Abhineeth Reddy Kunta, Raghu Mulukutla, Yiyi Ren, David L Bartlett, Dana H Bovbjerg, Anind K Dey, John M Jakicic

JMIR Perioper Med 2020;3(1):e17292

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Mobile Assessment of Acute Effects of Marijuana on Cognitive Functioning in Young Adults: Observational Study

Tammy Chung, Sang Won Bae, Eun-Young Mun, Brian Suffoletto, Yuuki Nishiyama, Serim Jang, Anind K Dey

JMIR Mhealth Uhealth 2020;8(3):e16240

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Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data

Unlike other clustering algorithms, such as k-means, DBSCAN does not require knowing the number of clusters a priori. It is able to find inner clusters (clusters surrounded by other clusters) and is robust to outliers and noise. Global clusters were extracted using all data and local clusters were extracted when data were split into daily time segments described earlier.

Afsaneh Doryab, Daniella K Villalba, Prerna Chikersal, Janine M Dutcher, Michael Tumminia, Xinwen Liu, Sheldon Cohen, Kasey Creswell, Jennifer Mankoff, John D Creswell, Anind K Dey