Charles A. Downs

Yong Huang, Charles A. Downs, Amir M. Rahmani

Warfarin, an anticoagulant medication, is formulated to prevent and address conditions associated with abnormal blood clotting, making it one of the most prescribed drugs globally. However, determining the suitable dosage remains challenging due to individual response variations, and prescribing an incorrect dosage may lead to severe consequences. Contextual bandit and reinforcement learning have shown promise in addressing this issue. Given the wide availability of observational data and safety concerns of decision-making in healthcare, we focused on using exclusively observational data from historical policies as demonstrations to derive new policies; we utilized offline policy learning and evaluation in a contextual bandit setting to establish the optimal personalized dosage strategy. Our learned policies surpassed these baseline approaches without genotype inputs, even when given a suboptimal demonstration, showcasing promising application potential.

Milad Asgari Mehrabadi, Seyed Amir Hossein Aqajari, Iman Azimi et al.

The world has been affected by COVID-19 coronavirus. At the time of this study, the number of infected people in the United States is the highest globally (7.9 million infections). Within the infected population, patients diagnosed with acute respiratory distress syndrome (ARDS) are in more life-threatening circumstances, resulting in severe respiratory system failure. Various studies have investigated the infections to COVID-19 and ARDS by monitoring laboratory metrics and symptoms. Unfortunately, these methods are merely limited to clinical settings, and symptom-based methods are shown to be ineffective. In contrast, vital signs (e.g., heart rate) have been utilized to early-detect different respiratory diseases in ubiquitous health monitoring. We posit that such biomarkers are informative in identifying ARDS patients infected with COVID-19. In this study, we investigate the behavior of COVID-19 on ARDS patients by utilizing simple vital signs. We analyze the long-term daily logs of blood pressure and heart rate associated with 70 ARDS patients admitted to five University of California academic health centers (containing 42506 samples for each vital sign) to distinguish subjects with COVID-19 positive and negative test results. In addition to the statistical analysis, we develop a deep neural network model to extract features from the longitudinal data. Using only the first eight days of the data, our deep learning model is able to achieve 78.79% accuracy to classify the vital signs of ARDS patients infected with COVID-19 versus other ARDS diagnosed patients.