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This systematic review documents current best practices, standards, and approaches for transportation safety data analytics. While standards exist for defining measures, there are few available standards or guides for processing driving and driver data. Standards are crucial for ensuring repeatability and appropriate cost-benefit decisions. The review identified 36 relevant studies describing behavioral and physiological measures. Most studies do not comprehensively report data processing steps. Of the studies that did report data processing steps, few analyzed the impact of decisions made during data processing on algorithm performance. Most studies were conducted in a controlled simulator environment and may not generalize to naturalistic settings. The findings show that driver behavior and physiological data show efficacy for detecting fatigue, distraction, stress, and driver errors. The results of these studies may necessitate additional data processing standards and future work should focus on measuring the effects of data decisions on model performance.

OBJECTIVE Type 2 diabetes is increasingly common and associated with substantial morbidity and mortality. This study examines trends in the patterns and costs of drug treatment of type 2 diabetes [...]

Fatigue is a significant contributor to accidents in the high-risk oil and gas industry. This study developed and evaluated models for forecasting fatigue manifestation in offshore workers using the Psychomotor Vigilance Task (PVT). Seventy offshore workers participated in a four-week study, providing data on sleep, physiological, subjective, and performance measures. Various machine learning models (Ridge, Random Forest, Support Vector, Long Short-Term Memory (LSTM) regressions) were employed to predict PVT reaction times using different data normalization between generalized and personalized datasets. Results indicate that personalized Support Vector Regression models outperform other models in predicting short-term fatigue. Age and perceived exertion emerged as crucial predictors of fatigue. The findings underscore the potential of personalized fatigue forecasting for enhancing safety in the oil and gas industry.