Aims To develop prediction models for identifying cases with poor visual outcomes after surgery for primary rhegmatogenous ...
The seven companies listed here cover the realistic range of what a buyer will encounter in 2026: embedded ML teams that own ...
TensorFlow, PyTorch, and Keras enable advanced deep learning applications. Scikit-learn, XGBoost, and LightGBM handle structured data efficiently. LangChain, Ollama, and Anthropic SDK support advanced ...
The development of predictive quantitative structure-activity relationship (QSAR) models using machine learning (ML) algorithms has become increasingly feasible due to the growing availability of ...
Scikit-learn, PyTorch, and TensorFlow remain core tools for structured data and deep learning tasks. New libraries like JAX, Polars, and LangChain offer speed, scalability, and real-time ML ...
Early prediction of acute respiratory distress syndrome (ARDS) after liver transplantation (LT) facilitates timely intervention. We aimed to develop a predictor of post-LT ARDS using machine learning ...
Sepsis is a global health threat that has a high incidence and mortality rate. Early prediction of sepsis onset can drive effective interventions and improve patients’ outcome. Data were collected ...
Post-stroke epilepsy (PSE) is a critical complication that worsens both prognosis and quality of life in patients with ischemic stroke. An interpretable machine learning model was developed to predict ...
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