Predicting hospitalization with LLMs from health insurance data.

Predictions of hospitalizations can help in the development of applications for health insurance, hospitals, and medicine. The data collected by health insurance has potential that is not always explored, and extracting features from it for use in machine learning applications requires demanding processes and specialized knowledge. With the emergence of large language models (LLM) there are possibilities to use this data for a wide range of applications requiring little specialized knowledge.

#DeOlhoNosCorais: a polygonal annotated dataset to optimize coral monitoring.

Corals are colonial animals within the Phylum Cnidaria that form coral reefs, playing a significant role in marine environments by providing habitat for fish, mollusks, crustaceans, sponges, algae, and other organisms. Global climate changes are causing more intense and frequent thermal stress events, leading to corals losing their color due to the disruption of a symbiotic relationship with photosynthetic endosymbionts. Given the importance of corals to the marine environment, monitoring coral reefs is critical to understanding their response to anthropogenic impacts.

COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers.

Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them.

Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images.

COVID-19 frequently provokes pneumonia, which can be diagnosed using imaging exams. Chest X-ray (CXR) is often useful because it is cheap, fast, widespread, and uses less radiation. Here, we demonstrate the impact of lung segmentation in COVID-19 identification using CXR images and evaluate which contents of the image influenced the most.

Phasic dopamine release identification using convolutional neural network.

Dopamine has a major behavioral impact related to drug dependence, learning and memory functions, as well as pathologies such as schizophrenia and Parkinson's disease. Phasic release of dopamine can be measured in vivo with fast-scan cyclic voltammetry. However, even for a specialist, manual analysis of experiment results is a repetitive and time consuming task.

A Dataset for Breast Cancer Histopathological Image Classification.

Today, medical image analysis papers require solid experiments to prove the usefulness of proposed methods. However, experiments are often performed on data selected by the researchers, which may come from different institutions, scanners, and populations. Different evaluation measures may be used, making it difficult to compare the methods.

Temporal evolution of carbapenem-resistant Acinetobacter baumannii in Curitiba, southern Brazil.

Background: In the last few years, carbapenem-resistant Acinetobacter baumannii isolates (CR-AB) have been identified worldwide. The first description of OXA-23-producing A baumannii in Brazil was from the city of Curitiba in 2003. The aim of the present study was to evaluate the persistence and dissemination of the first OXA-23-producing A baumannii clone isolated from patients in Hospital de Clinicas, Curitiba, Brazil.

Reconstructing shredded documents through feature matching.

We describe a procedure for reconstructing documents that have been shredded by hand, a problem that often arises in forensics. The proposed method first applies a polygonal approximation in order to reduce the complexity of the boundaries and then extracts relevant features of the polygon to carry out the local reconstruction. In this way, the overall complexity can be dramatically reduced because few features are used to perform the matching.