Previous studies show that a female driver and right-front (RF) passenger have 13.4% and 20.5% more fatality risk, respectively, compared to a male of the same age; the addition of new safety precautions reduced this fatality risk gap. However, it has yet to be uncovered if these precautions improve the equity in protection against non-fatal/major injuries. Using Crash Report Sampling System (CRSS) data, Logistic Regression and Random Forest, this study models the non-fatal/major injury risks against age and gender to answer this question. The double-pair comparison method is used to evaluate the major injury risk between females and males with different ages and vehicle model years. It shows that females have a 35% higher major injury risk relative to males of the same age; even with newer model vehicles, the major injury risk for females relative to the males is decreased by roughly 2.3% only, which shows that technological advancements have improved equity, but further improvement is still needed. These results signify that the equity in protection between males and females is still large, which calls for the automobile industry to work toward better equity in protection.

Accurate forecasting of medication usage and ICD-9/10 code streams is critical for optimizing medical logistics, especially during periods of high demand, such as pandemics, disease outbreaks, wartime, or natural disasters. In this study, we develop a novel forecasting framework using unsupervised learning techniques and Natural Language Processing (NLP) methods to build vector representations of ICD-9/10 code stream and medication usage from Electronic Health Record (EHR) data. Multiple time-series forecasting models, including Vector Autoregression (VAR), Temporal Fusion Transformer (TFT), and Autoregressive Long Short-Term Memory (AR-LSTM) are trained, tested and evaluated. Finally multiple TFT and AR-LSTM models with different lookback horizons are trained and ensembled together to achieve better forecasting accuracy in near further. The AI framework is trained and tested with MIMIC-IV ER and MIMIC-III datasets, resulting in the average error as low as 5% for 5-th day forecasting and 18% for the 10-th day forecasting. The results demonstrate the ensemble model’s superior performance on near-future medication usage forecast as well as ICD code progression, offering valuable insights for healthcare logistics and decision-making. This adaptive forecasting approach provides a robust tool for managing healthcare logistics under extreme and fluctuating conditions, enhancing the ability to deliver timely care, ultimately saving lives and resources.