Detecting and predicting septic shock early is crucial for the best possible outcome for patients. Accurately forecasting the vital signs of patients with sepsis provides valuable insights to clinicians for timely interventions, such as administering stabilizing drugs or optimizing infusion strategies. Our research examines N-BEATS, an interpretable deep-learning forecasting model that can forecast 3 hours of vital signs for sepsis patients in intensive care units (ICUs). In this work, we use the N-BEATS interpretable configuration to forecast the vital sign trends and compare them with the actual trend to understand better the patient's changing condition and the effects of infused drugs on their vital signs. We evaluate our approach using the publicly available eICU Collaborative Research Database dataset and rigorously evaluate the vital sign forecasts using out-of-sample evaluation criteria. We present the performance of our model using error metrics, including mean squared error (MSE), mean average percentage error (MAPE), and dynamic time warping (DTW), where the best scores achieved are 18.52e-4, 7.60, and 17.63e-3, respectively. We analyze the samples where the forecasted trend does not match the actual trend and study the impact of infused drugs on changing the actual vital signs compared to the forecasted trend. Additionally, we examined the mortality rates of patients where the actual trend and the forecasted trend did not match. We observed that the mortality rate was higher (92%) when the actual and forecasted trends closely matched, compared to when they were not similar (84%).

The democratization of any effective technology happens automatically by virtue of its success, even if the complexity it presents initially overwhelms some of the smartest people who wield it. But after six decades of commercial computing in the datacenter, we have certainly learned a thing or two about helping this process of adoption and integration along. There's nothing intrinsically special about artificial intelligence (AI) in this regard, which is arguably just the latest evolution in a long line of sophisticated data processing tools. Mainframes were kept in glasshouses as a kind of temple of computing during the 1960s and 1970s before being mimicked and copied into minicomputers. Eventually, PCs spawned two seismic shifts: becoming powerful enough to be servers and run mission-critical workloads in a client-server environment.

"As this technology continues to improve, it will have a significant impact on how clinical training is conducted in psychology and medicine," said psychologist and virtual reality technology expert Albert "Skip" Rizzo, PhD, who demonstrated recent advancements in virtual reality for use in psychology. Virtual humans can now be highly interactive, artificially intelligent and capable of carrying on a conversation with real humans, according to Rizzo, a research scientist at the University of Southern California Institute for Creative Technologies. "This has set the stage for the'birth' of intelligent virtual humans to be used in clinical training settings," he said. Rizzo showed videos of clinical psychiatry trainees engaging with virtual patients called "Justin" and "Justina." Justin is a 16-year-old with a conduct disorder who is being forced by his family to participate in therapy.

In response to the coronavirus health crisis, USC researchers have made a hard pivot, adapting labs and lessons learned from treating other diseases to help check the virus and save lives. At their disposal are numerous technologies that give a human advantage, despite the fast-break spread of COVID-19 once it exited central China and spread across the globe. The disease has afflicted thousands of Californians and poses a serious risk to public health and the world economy. Tools such as supercomputers, software apps, virtual reality, big data and algorithms are now in play. They are using the tools to find ways to search and destroy coronavirus DNA, turn smartphones into personal protection devices and use people-friendly simulators to help cope with the crush of medical cases.