Month: April 2020

When a natural disaster like an earthquake strikes, a community can literally be shaken to its core. One way to assess how well and how quickly that community recovers is to measure how, and how quickly, its hospitals and wider healthcare systems can become fully functional again and take care of its patients. Predicting the trajectory of that recovery is no easy task.

That’s because the resilience measures of a healthcare system are dizzyingly complex. They span everything from the availability of hospital staff, to the protection of critical equipment, to the state of the roads for ambulances to travel on, to the efficiency by which hospitals can transfer critically ill patients to different hospitals.

Hussam Mahmoud, an associate professor in the Department of Civil and Environmental Engineering at Colorado State University, and his students spend a lot of time thinking about how to define and describe “community resilience.” Mahmoud and graduate student Emad Hassan have created a modeling tool that could help city planners and emergency managers understand the full functionality and recovery of a healthcare system, in the wake of a natural disaster.

“We set out to develop models allowing us to understand, what is the demand on a hospital healthcare facility after an event like an earthquake,” Mahmoud said. “When we started looking into this, we were shocked to learn that there are no models currently that allow you to understand, what is the demand on the hospital, how is the hospital being impacted by the natural disaster, how is that going to impact demand and capacity, and how will that change over time?”

Their model, described in a forthcoming issue of the journal Reliability Engineering and System Safety, has wider implications for use in other disasters, including pandemics, like the one

By leveraging the basics of artificial intelligence technology now used to predict risk for suicide or other mental health issues, researchers developed an AI system that analyzes linguistic patterns to predict a youth’s risk for committing acts of school violence.

Study data published in the International Journal of Medical Informatics by physicians and clinical informaticians at Cincinnati Children’s Hospital Medical Center show the system can detect the risk of aggression for individual subjects.

The AI system uses pattern-recognizing machine learning and natural language processing (NLP) technologies. It combines the analytical scope and speed of information technology with clinical risk-assessment data and practitioner expertise, thereby automating a complex and time-consuming process, according to Yizhao Ni, Ph.D., co-principal investigator and a clinical informatician in the Division of Biomedical Informatics.

The technology uncovered multiple warning signs that could deliver useful clinical insights to assist personalized interventions, Ni explained. When fully developed, the system’s built-in risk assessment scales and automated risk prediction algorithms should produce an accurate and scalable computerized screening service to prevent school violence.

“Students are physically or verbally bullied on school property, electronically through texting or social media, and youth violence costs society billions of dollars in health care expenses or lost productivity,” Ni said, citing data from the U.S. Centers for Disease Control and Prevention.

“Our study demonstrates that overall, our AI system matches the clinical judgements and accuracy of psychiatrists 94 percent of the time. It has tremendous potential to help address youth violence at school and eventually other mental health conditions.”

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