Assessing Alcohol Use Disorder: Insights from Lifestyle, Background, and Family History with Machine Learning Techniques

This study explored how lifestyle, personal background, and family history contribute to the risk of developing Alcohol Use Disorder (AUD). Survey data from the All of Us Program was utilized to extract information on AUD status, lifestyle, personal background, and family history for 6,016 participants. Key determinants of AUD were identified using decision trees including annual income, recreational drug use, length of residence, sex/gender, marital status, education level, and family history of AUD. Data visualization and Chi-Square Tests of Independence were then used to assess associations between identified factors and AUD. Afterwards, machine learning techniques including decision trees, random forests, and Naive Bayes were applied to predict an individual's likelihood of developing AUD. Random forests were found to achieve the highest accuracy (82%), compared to Decision Trees and Naive Bayes. Findings from this study can offer insights that help parents, healthcare professionals, and educators develop strategies to reduce AUD risk, enabling early intervention and targeted prevention efforts.

Identification of Adaptive Driving Style Preference through Implicit Inputs in SAE L2 Vehicles

A key factor to optimal acceptance and comfort of automated vehicle features is the driving style. Mismatches between the automated and the driver preferred driving styles can make users take over more frequently or even disable the automation features. This work proposes identification of user driving style preference with multimodal signals, so the vehicle could match user preference in a continuous and automatic way. We conducted a driving simulator study with 36 participants and collected extensive multimodal data including behavioral, physiological, and situational data. This includes eye gaze, steering grip force, driving maneuvers, brake and throttle pedal inputs as well as foot distance from pedals, pupil diameter, galvanic skin response, heart rate, and situational drive context. Then, we built machine learning models to identify preferred driving styles, and confirmed that all modalities are important for the identification of user preference. This work paves the road for implicit adaptive driving styles on automated vehicles.