Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, MI 49931 # Anjum Shaik and Kristoffer Larsen contribute equally. Abstract Page ABSTRACT Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. We propose a novel staged model that combines advanced imaging and clinical data to improve predictive performance. By using convolutional neural networks (CNNs) to extract features from hip DXA images, along with clinical variables, shape measurements, and texture features, our method provides a comprehensive framework for assessing fracture risk. The study cohort included 547 patients, with 94 experiencing hip fracture. A staged machine learning-based model was developed using two ensemble models: Ensemble 1 (clinical variables only) and Ensemble 2 (clinical variables and DXA imaging features). This staged approach used uncertainty quantification from Ensemble 1 to decide if DXA features are necessary for further prediction. Ensemble 2 exhibited the highest performance, achieving an Area Under the Curve (AUC) of 0.9541, an accuracy of 0.9195, a sensitivity of 0.8078, and a specificity of 0.9427.

Children who grow up in low-income households but who make friends that come from higher-income homes are more likely to have higher salaries in adulthood than those who have fewer such friends. "There's been a lot of speculation… that the individuals' access to social capital, their social networks and the community they live in might matter a lot for a child's chance to rise out of poverty," says Raj Chetty at Harvard University. To find out how if that holds up, he and his colleagues analysed anonymised Facebook data belonging to 72.2 million people in the US between the ages of 25 and 44, accounting for 84 per cent of the age group's US population. It is relatively nationally representative of that age group, he says. The team used a machine learning algorithm to determine each person's socioeconomic status (SES), combining data such as the median income of people who live in the same region, the person's age, sex and the value of their phone model as a proxy for individual income.

Have you ever asked yourself a series of questions in order to help make a final decision on something? Maybe it was a simple decision like what you wanted to eat for dinner. You might have asked yourself if you wanted to cook or pick food up or get delivery. If you decided to cook, then you would have needed to figure out what type of cuisine you were in the mood for. And lastly, you probably needed to figure out if you had all of the ingredients in your fridge or needed to make a run to the store.