Long hospital stays can be stressful for the patient for many reasons. We hypothesized that age would be the greatest predictor of hospital stay among patients who underwent orthopedic surgery. Through our models, we found that severity of illness was indeed the highest factor that contributed to determining patient length of stay. The other two factors that followed were the facility that the patient was staying in and the type of procedure that they underwent.
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Applying centrality analysis on a protein interaction network to predict colorectal cancer driver genes
In this article the authors created an interaction map of proteins involved in colorectal cancer to look for driver vs. non-driver genes. That is they wanted to see if they could determine what genes are more likely to drive the development and progression in colorectal cancer and which are present in altered states but not necessarily driving disease progression.
Read More...Analysis of professional and amateur tennis serves using computer pose detection
The authors looked at the dynamics of tennis serves from professional and amateur athletes.
Read More...Correlation of Prominent Intelligence Type & Coworker Relations
Ashley Moulton & Joseph Rasmus investigate 9 controversial categories of intelligence as predicted by Multiple Intelligence Theory, originally proposed in the mid-1980s. By collecting data from 56 participants, they record that there may not actually be a correlation between these categorical types when it comes to workplace atmosphere and project efficiency.
Read More...Building a video classifier to improve the accuracy of depth-aware frame interpolation
In this study, the authors share their work on improving the frame rate of videos to reduce data sent to users with both 2D and 3D footage. This work helps improve the experience for both types of footage!
Read More...The use of computer vision to differentiate valley fever from lung cancer via CT scans of nodules
Pulmonary diseases like lung cancer and valley fever pose serious health challenges, making accurate and rapid diagnostics essential. This study developed a MATLAB-based software tool that uses computer vision techniques to differentiate between these diseases by analyzing features of lung nodules in CT scans, achieving higher precision than traditional methods.
Read More...COVID-19 pandemic impact on emotional aspects of high school students
In this study, the impact of shutting down schools on the emotional aspects of high school students was analyzed using survey responses.
Read More...An efficient approach to automated geometry diagram parsing
Here, beginning from an initial interest in the possibility to use a computer to automatically solve a geometry diagram parser, the authors developed their own Fast Geometry Diagram Parser (FastGDP) that uses clustering and corner information. They compared their own methods to a more widely available, method, GeoSolver, finding their own to be an order of magnitude faster in most cases that they considered.
Read More...Comparing the Dietary Preference of Caenorhabditis elegans for Bacterial Probiotics vs. Escherichia coli.
In this experiment, the authors used C. elegans as a simple model organism to observe the impact of probiotics on the human digestive system. The results of the experiments showed that the C. elegans were, on average, most present in Chobani cultures over other tested yogurts. While not statistically significant, these results still demonstrated that C. elegans might prefer Chobani cultures over other probiotic yogurts, which may also indicate greater gut benefits from Chobani over the other yogurt brands tested.
Read More...Time-Efficient and Low-Cost Neural Network to detect plant disease on leaves and reduce food loss and waste
About 25% of the food grown never reaches consumers due to spoilage, and 11.5 billion pounds of produce from gardens are wasted every year. Current solutions involve farmers manually looking for and treating diseased crops. These methods of tending crops are neither time-efficient nor feasible. I used a convolutional neural network to identify signs of plant disease on leaves for garden owners and farmers.
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