amlmodelmonitoring/ ├── .env # Environment variables (create from template) ├── set_env.ps1 # Loads .env variables into PowerShell session ├── requirements.txt # Python dependencies │ ├── ...

Objectives: This study aimed to develop and validate an interpretable machine learning (ML) model based on structured preoperative CT features for non-invasive prediction of pancreatic neuroendocrine ...

Researchers sought to develop a machine learning-based prognostic model that can predict all-cause mortality or liver transplant among patients with autoimmune hepatitis. A machine learning model ...

Introduction: Early and accurate diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM) is known to improve prognosis. However, it is sometimes challenging to distinguish ATTR-CM from other ...

AWS Lambda provides a simple, scalable, and cost-effective solution for deploying AI models that eliminates the need for expensive licensing and tools. In the rapidly evolving landscape of artificial ...

Internal iliac and obturator lymph nodes are common sites of metastasis in rectal cancer. This study developed a machine learning (ML) model using clinical data to predict lymph node metastasis and ...

Clinical Interchangeability of PD-L1 Immunohistochemistry Assays in First-Line Non–Small Cell Lung Cancer Management With Cemiplimab Internal iliac and obturator lymph nodes are common sites of ...

Background: Although neoadjuvant immunochemotherapy (nICT) has revolutionized the management of locally advanced esophageal squamous cell carcinoma (ESCC), the inability to accurately predict ...

The XGBoost-based approach demonstrated robust external validation across multiple centers, supporting clinical adoption to guide personalized treatment decisions. A machine learning (ML) model using ...

Background Current risk scores inadequately predict long-term mortality after transcatheter aortic valve replacement (TAVR), limiting their ability to guide decisions around procedural futility. We ...