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.

Drugs from drugs: New chemical insights into a mature concept.

Developing new drugs from marketed ones is a well-established and successful approach in drug discovery. We offer a unified view of this field, focusing on the new chemical aspects of the involved approaches: (a) chemical transformation of the original drugs (late-stage modifications, molecular editing), (b) prodrug strategies, and (c) repurposing as a tool to develop new hits/leads. Special focus is placed on the molecular structure of the drugs and their synthetic feasibility.

Real-world and clinical trial outcomes in large B-cell lymphoma with axicabtagene ciloleucel across race and ethnicity.

Axicabtagene ciloleucel (axi-cel) is an autologous anti-CD19 chimeric antigen receptor (CAR) T-cell therapy approved for relapsed/refractory (R/R) large B-cell lymphoma (LBCL). Despite extensive data supporting its use, outcomes stratified by race and ethnicity groups are limited. Here, we report clinical outcomes with axi-cel in patients with R/R LBCL by race and ethnicity in both real-world and clinical trial settings.

A Novel Detection Refinement Technique for Accurate Identification of Burrows in Underwater Imagery.

With the evolution of the convolutional neural network (CNN), object detection in the underwater environment has gained a lot of attention. However, due to the complex nature of the underwater environment, generic CNN-based object detectors still face challenges in underwater object detection. These challenges include image blurring, texture distortion, color shift, and scale variation, which result in low precision and recall rates.