Health Life

Model can predict hospital resilience for natural disasters, pandemics

Credit: CC0 Public Domain

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 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

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Constitutional mismatch repair deficiency syndrome – Genetics Home Reference

Constitutional mismatch repair deficiency (CMMRD) syndrome is a rare disorder that greatly increases the risk of developing one or more types of cancer in children and young adults. The cancers that most commonly occur in CMMRD syndrome are cancers of the (large intestine) and rectum (collectively referred to as colorectal cancer), brain, and blood (leukemia or lymphoma).

Almost all people with CMMRD syndrome develop cancer before age 18, generally in late childhood. The age of diagnosis varies depending on the cancer type; brain cancers, leukemia, and lymphomas tend to occur at younger ages than colorectal cancer in people with CMMRD syndrome. It is estimated that 20 to 40 percent of people with CMMRD syndrome who develop cancer will develop another cancer later in life.

People with CMMRD syndrome may develop multiple noncancerous (benign) growths (adenomas) in the colon that are likely to become cancerous (malignant) over time. Brain cancers in CMMRD syndrome often involve certain cells called glial cells, causing gliomas or glioblastomas. The most common blood cancer in CMMRD syndrome is called which affects white blood cells. Other cancers that can occur in CMMRD syndrome include cancers of , , or uterine lining ().

Many people with CMMRD syndrome develop features similar to those that occur in a condition called neurofibromatosis type 1. These features include changes in skin coloring (pigmentation), which are characterized by one or more flat patches on the skin that are darker than the surrounding area (). Some affected individuals have freckling or patches of skin that are unusually light in color (hypopigmented). Rarely, people with CMMRD syndrome will develop a feature of neurofibromatosis type 1 called Lisch nodules, which are benign growths that often appear in the colored part of the eye (the iris). Lisch nodules do not interfere with vision. Some people

Health Life

Study indicates artificial intelligence could help stem tide of school violence

This illustration shows how artificial intelligence technology might be leveraged to predict a youth’s risk for committing acts of school violence by analyzing linguistic patterns. Researchers published preclinical research data about the system in the International Journal of Medical Informatics. Credit: Cincinnati Children’s

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 .

“Students are physically or verbally bullied on 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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